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  • 1. Abu Bakar, Sakhinah
    et al.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Characterization of essential proteins in proteins interaction networks2013In: Journal of Quality Measurement and Analysis, ISSN 1823-5670, Vol. 9, no 2, p. 11-26Article in journal (Refereed)
  • 2.
    Ahammed, Farhan
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Finding lower bounds of localization with noisy measurements using genetic algorithms2011In: Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet '11), Miami, Florida, USA: Association for Computing Machinery (ACM), 2011, p. 47-54Conference paper (Refereed)
    Abstract [en]

    Vehicular Ad-Hoc Networks (VANETs) are wireless networks with mobile nodes (vehicles) which connect in an ad-hoc manner. Many vehicles use the Global Positioning System (GPS) to provide their locations. However the inaccuracy of GPS devices leads to some vehicles incorrectly assuming they are located at different positions and sometimes on different roads. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information regarding the measured distances to neighbouring vehicles. This paper looks at finding how much improvement can be made given the erroneous measurements present in the system. An evolutionary algorithm is used to evolve instances of parameters used by the VLOCI2 algorithm, also presented in this paper, to find instances which minimises the inaccuracy in computed locations. Simulation results show a definite improvement in location accuracy and lower bounds on how much improvement is possible is inferred.

  • 3. Ahammed, Farhan
    et al.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Finding Lower Bounds of Localization with Noisy Measurements Using Genetic Algorithms2011Report (Refereed)
  • 4.
    Ahammed, Farhan
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Using simulated annealing to find lower bounds of localization with noisy measurements2012In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), IEEE conference proceedings, 2012, p. 601-608Conference paper (Refereed)
  • 5.
    Ahammed, Farhan
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Ott, Max
    NICTA Australia Australian Technology Park.
    LICA: Improving Localization Given Noisy Data In GPS-Equipped VANETs Using Trilateration With Cluster Analysis2011Report (Refereed)
  • 6.
    Ahammed, Farhan
    et al.
    University of Sydney.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Ott, Max
    NICTA.
    VLOCI: Using Distance Measurements to Improve the Accuracy of Location Coordinates in GPS-Equipped VANETs2012In: Mobile and Ubiquitous Systems: 7th International ICST Conference, MobiQuitous 2010, Sydney, Australia, December 6-9, 2010, Revised Selected Papers / [ed] Patrick Sénac, Max Ott, Aruna Seneviratne, Springer Berlin/Heidelberg, 2012, Vol. 73, p. 149-161Conference paper (Refereed)
  • 7.
    Ahammed, Farhan
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Ott, Max
    NICTA Australia Australian Technology Park.
    VLOCI2: An Iterative Method To Improve Location Coordinates In GPS-Equipped VANETs in Multiple Lanes2011Report (Refereed)
  • 8.
    Ahammed, Farhan
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Ott, Max
    Australian Technology Park, Australia.
    VLOCI2: An Iterative Method To Improve Location Coordinates In GPS-Equipped VANETs in Multiple Lanes2011Report (Refereed)
  • 9.
    Al-Dulaimy, A.
    et al.
    Mälardalen University.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Papadopoulos, A. V.
    Mälardalen University.
    Nolte, T.
    Mälardalen University.
    LOOPS: A Holistic Control Approach for Resource Management in Cloud Computing2021In: ICPE 2021 - Proceedings of the ACM/SPEC International Conference on Performance Engineering, Association for Computing Machinery (ACM), 2021, p. 117-124Conference paper (Refereed)
    Abstract [en]

    In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-Time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-Time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.

  • 10.
    Al-Dulaimy, Auday
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Itani, Wassim
    Beirut Arab University, Lebanon.
    Shamseddine, Maha
    Beirut Arab University, Lebanon.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Privacy-Aware Job Submission in the Cloud2019In: 2019 2nd IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2019, IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    The services offered by cloud computing are provided to individuals and organizations by varied shared resources which are forming the hardware layer of cloud data centers. Cloud users do not deal or interact directly with those resources, instead, they deal with the virtualized version of them, in other words, users deal with the virtualization layer which conceals to a great extent the specifics of the physical hardware layer. Based on the virtualization concept, more than one virtual machine can be co-hosted on the same physical machine. In spite of the wide range of benefits, co-hosting virtual machines on the same host comes with privacy and security threats. From one side, cloud providers are serving the virtual machines without being aware of their contents. On the other side, once cloud users submit their jobs to be serviced in the cloud, they lose their control on their jobs' sensitive information. Thus, cloud users' hesitation from moving to the cloud is logical since their sensitive jobs' content leakage or misuse is possible, especially when cloud services are not designed with privacy considerations. This paper proposes an approach to make the jobs with sensitive information more secure when submitted to the cloud environment. The core idea of the approach is to request the inclusion of the privacy specification of a set of one or more provider services in the Service Level Agreement contract.

  • 11.
    Al-Dulaimy, Auday
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Itani, Wassim
    University of Houston Victoria, USA..
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Shamseddine, Maha
    Beirut Arab University, LBN..
    BWSLICER: A bandwidth slicing framework for cloud data centers2020In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 112, p. 767-784Article in journal (Refereed)
    Abstract [en]

    Bandwidth allocation is an important and influential factor in enhancing the performance of the data centers' nodes. In this paper we propose bwSlicer, a framework for bandwidth slicing in cloud data centers, that sheds light on the virtues of effective dynamic bandwidth allocation on improving the system performance and energy efficiency. Three algorithms are investigated to deal with this issue. In the first algorithm, called Fair Bandwidth Reallocation (FBR), two virtual machines co-hosted on the same node conditionally exchange bandwidth slices based on their requirements. The second algorithm, called Required Bandwidth Allocation (RBA), periodically monitors the co-hosted virtual machines and adds/removes bandwidth slices for each of them based on their bandwidth utilization. The third algorithm, called Divide Bandwidth Reallocation (DBR), divides the bandwidth of the virtual machine into slices once it finishes its execution, and distributes the slices among the co-hosted running virtual machines according to a specific policy. The proposed bandwidth slicing algorithms are emulated in a virtualized networking environment using the Mininet network emulator. The emulation results demonstrated a promising improvement ratio in execution time and energy consumption reaching up to 30%. These results present a call for action for further research into bandwidth slicing and reallocation as a viable complement to other energy-saving techniques for enhancing the energy consumption in cloud data centers.

  • 12.
    Al-Dulaimy, Auday
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Sharma, Yogesh
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Gokan Khan, Michel
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Introduction to edge computing2020In: Edge Computing: Models, technologies and applications / [ed] Javid Taheri; Shuiguang Deng, Institution of Engineering and Technology, 2020Chapter in book (Refereed)
    Abstract [en]

    Edge computing is the model that extends cloud computing services to the edge of the network. This model aims to move decision-making operations as close as possible to data sources since it acts as an intermediate layer connecting cloud data centres to edge devices/sensors. Transferring all the data from the network edge to the cloud data centres for processing may create a latency problem and outstrip the network's bandwidth capacity. To resolve this issue, it might be best to process data closer to the devices/sensors. This chapter will take a deep dive into edge computing, its applications, and the existing challenges related to this model.

  • 13.
    Al-Dulaimy, Auday
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Hoseiny Farahabady, M. R.
    University of Sydney, AUS.
    Deng, S.
    Zhejiang University, CHN.
    Zomaya, A.
    University of Sydney, AUS.
    MultiScaler: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications2020In: IEEE Transactions on Cloud Computing, ISSN 2168-7161Article in journal (Refereed)
    Abstract [en]

    Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MultiScaler , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MultiScaler is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MultiScaler approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MultiScaler produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

  • 14.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Hernandez Benet, Cristian
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    SDN helps volume in Big Data2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: IET Digital Library, 2018, 1, p. 185-206Chapter in book (Refereed)
    Abstract [en]

    Both Big Data and SDN are described in detail in previous chapters. This chapter investigates how SDN architecture can leverage its unique features to mitigate the challenges of Big Data volume. Accordingly, first, we provide an overview of Big Data volume, its effects on the underlying network, and mention some potential SDN solutions to address the corresponding challenges. Second, we elaborate more on the network-monitoring, traffic-engineering, and fault-tolerant mechanisms which we believe they may help to address the challenges of Big Data volume. Finally, this chapter is concluded with some open issues.

  • 15.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration2019Conference paper (Refereed)
    Abstract [en]

    Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.

    Download full text (pdf)
    Cloudnet-2019
  • 16.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Economic Sciences, Communication and IT (discontinued), Department of Computer Science.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ohlen, Peter
    Ericsson Research, Sweden.
    Curescu, Calin
    Ericsson Research, Sweden.
    Multi-Objective genetic algorithm for fast service function chain reconfiguration2023In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 20, no 3, p. 3501-3522Article in journal (Refereed)
    Abstract [en]

    The optimal placement of virtual network functions (VNFs) improves the overall performance of servicefunction chains (SFCs) and decreases the operational costs formobile network operators. To cope with changes in demands,VNF instances may be added or removed dynamically, resourceallocations may be adjusted, and servers may be consolidated.To maintain an optimal placement of SFCs when conditionschange, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, suchreconfigurations may lead to stress on the VNF infrastructure,which may cause service degradation. On the other hand, notchanging the placement may lead to suboptimal operation,and servers and links may become congested or underutilized,leading to high operational costs. In this paper, we investigatethe trade-off between the reconfiguration of SFCs and theoptimality of their new placement and service-flow routing. Wedevelop a multi-objective genetic algorithm that explores thePareto front by balancing the optimality of the new placementand the cost to achieve it. Our numerical evaluations show thata small number of reconfigurations can significantly reduce theoperational cost of the VNF infrastructure. In contrast, toomuch reconfiguration may not pay off due to high costs. Webelieve that our work provides an important tool that helpsnetwork providers to plan a good reconfiguration strategy fortheir service chains.

  • 17.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Öhlen, Peter
    Ericsson AB.
    Curescu, Calin
    Ericsson AB.
    On the Cost-Optimality Trade-off for Fast Service Function Chain ReconfigurationManuscript (preprint) (Other academic)
    Abstract [en]

    Optimal placement of Virtual Network Functions (VNFs) in data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. In order to cope with changes in demands, VNF instances may be added or removed dynamically, resource allocations may be adjusted, and servers may be consolidated. To maintain an optimal placement of SFC under changing conditions, dynamic reconfiguration is required including the migration of VNFs and the re-routing of service flows. However, such reconfiguration may lead to notable service disruptions and can be exacerbated when reconfiguration entails state or VNF migration, both imposing additional overhead on the VNF infrastructure. On the other hand, not changing the placement may lead to a suboptimal operation, servers and links may become congested or underutilized, leading to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow routing. We model reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the new placement and the reconfiguration cost necessary to achieve it. We also develop a fast multi-objective genetic algorithm that finds near-optimal solutions for online decisions. Our numerical evaluations show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure. In contrast, too much reconfiguration may not pay off due to high costs. We believe that our work is an important tool that helps network provider to plan a good reconfiguration strategy for their service chains.

  • 18.
    Anwar, Adnan
    et al.
    UNSW, Canberra, ACT 2600, Australia..
    Mahmood, A. N.
    La Trobe Univ, Bundoora, Vic 3086, Australia..
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Tari, Zahir
    RMIT Univ, Distributed Syst, Melbourne, Vic 3001, Australia..
    Zomaya, Albert Y.
    Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia..
    HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise2017In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 3, p. 1446-1459Article in journal (Refereed)
    Abstract [en]

    The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.

  • 19.
    Aslanpour, M. S.
    et al.
    Monash University, AUS.
    Toosi, A. N.
    Monash University, AUS.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Gaire, R.
    CSIRO DATA61, AUS.
    AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds2021In: Simulation (San Diego, Calif.), ISSN 1569-190X, E-ISSN 1878-1462, Vol. 108, article id 102245Article in journal (Refereed)
    Abstract [en]

    Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.

  • 20.
    Bakar, Sakhinah Abu
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Characterization of essential proteins based on network topology in proteins interaction networks2014In: Proceedings of the 3rd International Conference on Mathematical Sciences, American Institute of Physics (AIP), 2014, Vol. 1602, p. 36-42Conference paper (Refereed)
  • 21.
    Bakar, Sakhinah Abu
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    FIS-PNN: A Hybrid Computational Method for Protein-Protein Interactions Prediction Using the Secondary Structure Information2011In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-2011), IEEE conference proceedings, 2011Conference paper (Refereed)
  • 22.
    Bakar, Sakhinah Abu
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Identifying Hub Proteins and Their Essentiality from Protein-protein Interaction Network2011In: Bioinformatics and Bioengineering (BIBE), 2011 IEEE 11th International Conference on, Taichung, Taiwan: IEEE Press, 2011Conference paper (Refereed)
  • 23.
    Bhamare, Deval
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Vestin, Jonathan
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Khoshkholghi, Mohammad Ali
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring2019In: 2019 IEEE International Conference on Communications (ICC) Próceedings, IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Managing and scaling virtual network function(VNF) service chains require the collection and analysis ofnetwork statistics and states in real time. Existing networkfunction virtualization (NFV) monitoring frameworks either donot have the capabilities to express the range of telemetryitems needed to perform management or do not scale tolarge traffic volumes and rates. We present IntOpt, a scalableand expressive telemetry system designed for flexible VNFservice chain network monitoring using active probing. IntOptallows to specify monitoring requirements for individual servicechain, which are mapped to telemetry item collection jobsthat fetch the required telemetry items from P4 (programmingprotocol-independent packet processors) programmable dataplaneelements. In our approach, the SDN controller creates theminimal number of monitoring flows to monitor the deployedservice chains as per their telemetry demands in the network.We propose a simulated annealing based random greedy metaheuristic(SARG) to minimize the overhead due to activeprobing and collection of telemetry items. Using P4-FPGA, webenchmark the overhead for telemetry collection and compareour simulated annealing based approach with a na¨ıve approachwhile optimally deploying telemetry collection probes. Ournumerical evaluation shows that the proposed approach canreduce the monitoring overhead by 39% and the total delays by57%. Such optimization may as well enable existing expressivemonitoring frameworks to scale for larger real-time networks.

    Download full text (pdf)
    fulltext
  • 24.
    Bhamare, Deval
    et al.
    University of Surrey, United Kingdom.
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Vestin, Jonathan
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Khoshkholghi, Mohammad Ali
    King's College London, United Kingdom.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Mahmoodi, Toktam
    King's College London, United Kingdom.
    Öhlén, Peter
    Ericsson Research, Stockholm.
    Curescu, Calin
    Ericsson Research, Stockholm.
    IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P42022In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 216, article id 109214Article in journal (Refereed)
    Abstract [en]

    The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks. 

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  • 25.
    Calvo, J.C.
    et al.
    University of Granada, Spain.
    Ortega, J.
    University of Granada, Spain.
    Anguita, M.
    University of Granada, Spain.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    A Method to Improve the Accuracy of Protein Torsion Angles2011In: International Conference on Bioinformatics Models, Methods and Algorithms (Bioinformatics-2011), Rome, Italy: SciTePress, 2011, p. 297-300Conference paper (Refereed)
  • 26.
    Casas, Israel
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ranjan, Rajiv
    CSIRO, Australia.
    Wang, Lizhe
    School of Computer Science, China University of Geosciences, China.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    GA-ETI: An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments2018In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511, Vol. 26, p. 318-331Article in journal (Refereed)
  • 27.
    Casas, Israel
    et al.
    Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ranjan, Rajiv
    Australia, UK.
    Wang, Lizhe
    China.
    Zomaya, Albert Y.
    Australia.
    A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems2017In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 74, p. 168-178Article in journal (Refereed)
    Abstract [en]

    Cloud computing provides substantial opportunities to researchers who demand pay-as-you-go computing systems. Although cloud provider (e.g., Amazon Web Services) and application provider (e.g., biologists, physicists, and online gaming companies) both have specific performance requirements (e.g. application response time), it is the cloud scheduler’s responsibility to map the application to underlying cloud resources. This article presents a Balanced and file Reuse-Replication Scheduling (BaRRS) algorithm for cloud computing environments to optimally schedule scientific application workflows. BaRRS splits scientific workflows into multiple sub-workflows to balance system utilization via parallelization. It also exploits data reuse and replication techniques to optimize the amount of data that needs to be transferred among tasks at run-time. BaRRS analyzes the key application features (e.g., task execution times, dependency patterns and file sizes) of scientific workflows for adapting existing data reuse and replication techniques to cloud systems. Further, BaRRS performs a trade-off analysis to select the optimal solution based on two optimization constraints: execution time and monetary cost of running scientific workflows. BaRRS is compared with a state-of-the-art scheduling approach; experiments prove its superior performance. Experiments include four well known scientific workflows with different dependency patterns and data file sizes. Results were promising and also highlighted most critical factors affecting execution of scientific applications on clouds. 

  • 28.
    Casas, Israel
    et al.
    University of Sydney, Australia; CSIRO, Data61, Canberra, ACT, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ranjan, Rajiv
    Newcastle University, England ; CSIRO, Data61, Canberra, ACT, Australia.
    Zomaya, Albert Y.
    University of Sydney, Australia.
    PSO-DS: a scheduling engine for scientific workflow managers2017In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 73, no 9, p. 3924-3947Article in journal (Refereed)
    Abstract [en]

    Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user-provider affiliation (CUPA) to guide workflow's owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.

  • 29.
    Chahed, Hamza
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Usman, Muhammad
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Chatterjee, Ayan
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Bayram, Firas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Chaudhary, Rajat
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunstrom, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ahmed, Bestoun S.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Czech Technical University in Prague, Czech Republic.
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Deggendorf Institute of Technology, Germany.
    AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications2023In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 22, article id 100805Article in journal (Refereed)
    Abstract [en]

    Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

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  • 30.
    Cho, Daewoong
    et al.
    School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
    Bastani, Saeed
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zomaya, Albert Y
    School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
    Big Data helps SDN to optimize its controllers2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: IET Digital Library, 2018, 1, p. 389-408Chapter in book (Refereed)
    Abstract [en]

    In this chapter, we first discuss the basic features and recent issues of the SDN control plane, notably the controller element. Then, we present feasible ideas to address the SDN controller-related problems using Big Data analytics techniques. Accordingly, we propose that Big Data can help various aspects of the SDN controller to address scalability issue and resiliency problem. Furthermore, we proposed six applicable scenarios for optimizing the SDN controller using the Big Data analytics: (i) controller scale-up/out against network traffic concentration, (ii) controller scale-in for reduced energy usage, (iii) backup controller placement for fault tolerance and high availability, (iv) creating backup paths to improve fault tolerance, (v) controller placement for low latency between controllers and switches, and (vi) flow rule aggregation to reduce the SDN controller's traffic. Although real-world practices on optimizing SDN controllers using Big Data are absent in the literature, we expect scenarios we highlighted in this chapter to be highly applicable to optimize the SDN controller in the future.

  • 31.
    Cho, Daewoong
    et al.
    Univ. of Sydney, Sydney, NSW, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zomaya, Albert Y
    School of Information Technologies, University of Sydney.
    Wang, Lizhe
    China Univ. of Geosci., China.
    Virtual Network Function Placement: Towards Minimizing Network Latency and Lead Time2017In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Piscataway: IEEE, 2017, p. 90-97Conference paper (Refereed)
    Abstract [en]

    Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations.

  • 32.
    Deng, Shuiguang
    et al.
    Zhejiang University, Hangzhou, China.
    Huang, Longtao
    Chinese Academy of Sciences, Beijing, China.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Yin, Jianwei
    Zhejiang University, Hangzhou, China.
    Zhou, MengChu
    Macau University of Science and Technology, Macau, China.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Mobility-Aware Service Composition in Mobile Communities2016In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 2168-2232, Vol. 47, no 3, p. 555-568Article in journal (Refereed)
    Abstract [en]

    he advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

  • 33.
    Deng, Shuiguang
    et al.
    Zhejiang University, Hangzhou, China.
    Huang, Longtao
    Zhejiang University, Hangzhou, China.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Computation Offloading for Service Workflow in Mobile Cloud Computing2015In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183, Vol. 26, no 12, p. 3317-3329Article in journal (Refereed)
  • 34.
    Deng, Shuiguang
    et al.
    Zhejiang Univ, Coll Comp Sci, Hangzhou, Zhejiang, Peoples R China..
    Huang, Longtao
    Zhejiang Univ, Comp Sci, Hangzhou, Zhejiang, Peoples R China..
    Wu, Hongyue
    Zhejiang Univ, Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China..
    Tan, Wei
    IBM TJ Watson Res Ctr, Yorktown Hts, NY USA..
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zomaya, Albert Y.
    Univ Sydney, Sch Informat Technol, High Performance Comp & Networking, Sydney, NSW 2006, Australia..
    Wu, Zhaohui
    Zhejiang Univ, Coll Comp Sci, Hangzhou, Zhejiang, Peoples R China..
    Toward Mobile Service Computing: Opportunities and Challenges2016In: IEEE CLOUD COMPUTING, ISSN 2325-6095, Vol. 3, no 4, p. 32-41Article in journal (Refereed)
  • 35.
    Deng, Shuiguang
    et al.
    Zhejiang University, Hangzhou, China.
    Wu, Hongyue
    Zhejiang University, Hangzhou, China.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Wu, Zhaohui
    Zhejiang University, Hangzhou, China.
    Cost Performance Driven Service Mashup: A Developer Perspective2016In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183, Vol. 27, no 8, p. 2234-2247Article in journal (Refereed)
    Abstract [en]

    Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the QoS (quality of service) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, GA4MC (genetic algorithm for mashup creation), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal . Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

  • 36.
    Deng, Shuiguang
    et al.
    Zhejiang University, CHN.
    Xiang, Zhengzhe
    Zhejiang University, CHN.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Khoshkholghi, Mohammad Ali
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Yin, Jianwei
    Zhejiang University, CHN.
    Zomaya, Albert Y.
    University Sydney, AUS.
    Dustdar, Schahram
    Technische Universität Wien, AUT.
    Optimal Application Deployment in Resource Constrained Distributed Edges2021In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 20, no 5, p. 1907-1923Article in journal (Refereed)
    Abstract [en]

    The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

  • 37.
    Deng, Shuiguang
    et al.
    Zhejiang University, China..
    Xiang, Zhengzhe
    Zhejiang University, China..
    Yin, Jianwei
    Zhejiang University, China..
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zomaya, Albert Y.
    University of Sydney, Australia..
    Composition-Driven IoT Service Provisioning in Distributed Edges2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 54258-54269Article in journal (Refereed)
    Abstract [en]

    The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.

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  • 38.
    Deng, Shuiguang
    et al.
    Zhejiang University School of Medicine, CHN.
    Xiang, Zhengzhe
    Zhejiang University, CHN.
    Zhao, Peng
    Zhejiang University School of Medicine, CHN.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Gao, Honghao
    Computing Center Shanghai University, CHN.
    Yin, Jianwei
    Zhejiang University, CHN.
    Zomaya, Albert Y.
    University of Sydney, AUS.
    Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method2020In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 9, p. 6103-6113, article id 9001216Article in journal (Refereed)
    Abstract [en]

    Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.

  • 39.
    Dorronsoro, Bernabé
    et al.
    Univ Lille, Lille, France.
    Nesmachnow, Sergio
    Univ Republica, Montevideo, Uruguay.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Talbi, El-Ghazali
    Univ Lille, Lille, France.
    Bouvry, Pascal
    Univ Luxembourg, Luxembourg, Luxembourg.
    A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems2014In: Sustainable Computing: Informatics and Systems, ISSN 2210-5379, E-ISSN 2210-5387, Vol. 4, no 4, p. 252-261Article in journal (Refereed)
  • 40.
    Fazio, Maria
    et al.
    Univ Messina, Comp Sci, Messina, Italy.
    Ranjan, Rajiv
    Newcastle University.
    Girolami, Michele
    Italian Natl Council Res, Rome, Italy.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Dustdar, Schahram
    TU Wien, Computer Science.
    Villari, Massimo
    Univ Messina, Comp Sci, Messina, Italy.
    A Note on the Convergence of IoT, Edge, and Cloud Computing in Smart Cities2018In: IEEE Cloud Computing, ISSN 2325-6095, Vol. 5, no 5, p. 22-24Article in journal (Refereed)
    Abstract [en]

    The purpose of the special issue is to cover all aspects of design and implementation, as well as deployment and evaluation of solutions aimed at the osmotic convergence of IoT, edge, and cloud computing, with specific reference to the smart cities application scenario.

  • 41.
    Galletta, A.
    et al.
    University of Messina, ITA.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Villari, M.
    University of Messina, ITA.
    On the applicability of secret share algorithms for saving data on iot, edge and cloud devices2019In: Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, IEEE, 2019, p. 14-21, article id 8875319Conference paper (Refereed)
    Abstract [en]

    A common practice to store data is to use remote Cloud-based storage systems. However, storing files in remote services can arise privacy and security issues, for example, they can be attacked or even discontinued. A possible solution to solve this problem is to split files into chunks and add redundancy by means of Secret Share techniques. When it comes to Internet of Things (IoT), Edge and Cloud environments, these techniques have not been evaluated for the purpose of storing files. This work aims to address this issue by evaluating two of the most common Secret Share algorithms in order to identify their suitability for different environments, while considering the size of the file and the availability of resources. In particular, we analysed Shamir's Secret Share schema and the Redundant Residue Number System (RRNS) to gauge their efficiency regarding storage requirement and execution time. We made our experiments for different file sizes (from 1kB up to 500MB), number of parallel threads (1 to 4) and data redundancy (0 to 7) in all aforementioned environments. Results were promising and showed that, for example, to have seven degrees of redundancy, Shamir uses eight times more storage than RRNS; or, Shamir is faster than RRNS for small files (up to 20 kB). We also discovered that the environment on which the computation should be performed depends on both file size and algorithm. For instance, when employing RRNS, files up to 500kB can be processed on the IoT, up to 50MB on the Edge, and beyond that on the Cloud; whereas, in Shamir's schema, the threshold to move the computation from the IoT to the Edge is about 50kB, and from the Edge to the Cloud is about 500kB.

  • 42.
    Galletta, Antonino
    et al.
    University of Messina, ITA.
    Buzachis, Alina
    University of Messina, ITA.
    Fazio, Maria
    University of Messina, ITA.
    Celesti, Antonio
    University of Messina, ITA.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Villari, Massimo
    University of Messina, ITA.
    Smart Hospitals Enabled by Edge Computing2020In: Edge Computing: Models, Technologies and Applications / [ed] Javid Taheri ; Shuiguang Deng, The Institution of Engineering and Technology (IET) , 2020, p. 357-380Chapter in book (Other academic)
    Abstract [en]

    The development of smart cities is inseparable from the application of edge computing technology. The value of edge computing in smart cities is reflected in specific application scenarios. So, this chapter mainly introduces the definition of smart cities and their architecture, and enumerates some applications with edge computing technology. It also introduces how edge computing technology is applied in dealing with urban traffic congestion. Next, the computation offloading, resource allocation and task scheduling problems in edge computing -enabled smart city are discussed according to the academic research. Finally, we discuss the security and privacy problem in edge computing -based smart city. The application of edge computing in smart cities is much more than that mentioned in this chapter. With experts and scholars in different domain investing in edge computing research, the role of edge computing in smart cities will be further enhanced.

  • 43.
    Galletta, Antonino
    et al.
    University of Messina, Italy.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Fazio, Maria
    University of Messina, Italy.
    Celesti, Antonio
    University of Messina, Italy..
    Villari, Massimo
    University of Messina, Italy.
    Overcoming security limitations of Secret Share techniques: The Nested Secret Share2021In: 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021, p. 289-296Conference paper (Refereed)
    Abstract [en]

    Secret Share (SS) is becoming a very hot topic within the scientific community. It allows us to split a secret into fragments and to share them among parties in such a way that a subset can recompose the original information. SS techniques assure a high redundancy degree, but the security level is fixed. Therefore, if a minimum number of peers collude then attackers can recompose the secret easily. A possible approach to improve the security of SS is designing nest fragment sharing techniques. In this paper, we propose the Nested Secret Share (NSS) as a more reliable and scalable strategy. In particular, we discuss the security of NSS considering the number of recomposition attempts that an attacker has to perform to retrieve the secret and then we deeply analyse the impact of the redundancy and the number of peers on the secret management against the percentage of compromised nodes. Experiments were promising and showed that the redundancy degree of SS can be highly improved by NSS.

  • 44.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Al-Dulaimy, Auday
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Khoshkholghi, Mohammad Ali
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Open-source projects for edge computing2020In: Edge Computing: Models, technologies and applications / [ed] Javid Taheri & Shuiguang Deng, Institution of Engineering and Technology, 2020Chapter in book (Refereed)
    Abstract [en]

    In this chapter, the author covered an overview to the EC technologies as well as a scope classification to its entire paradigm. The author also reviewed the state-of-the-art reference architectures and standardization, as well as top ten open-source projects and platforms in EC. Moreover, the author mentioned open issues and challenges in the EC paradigm and discussed them in detail.

  • 45.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Bastani, Saeed
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Deng, Shuigang
    Zhejiang Univ, China.
    NFV-Inspector: A Systematic Approach to Profile and Analyze Virtual Network Functions2018In: 2018 IEEE 7th International Conference on Cloud Networking (CloudNet), IEEE, 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Network Function Virtualization (NFV) focuses on decoupling network functions from proprietary hardware (i.e., middleboxes) by leveraging virtualization technology. Combining it with Software Defined Networking (SDN) enables us to chain network services much easier and faster. The main idea of using these technologies is to consolidate several Virtual Network Functions (VNFs) into a fewer number of commodity servers to reduce costs, increase VNFs fluidity and improve resource efficiency. However, the resource allocation and placement of VNFs in the network is a multifaceted decision problem that depends on many factors, including VNFs resource demand characteristics, arrival rate, configuration of underlying infrastructure, available resources and agreed Quality of Services (QoS) in Service Level Agreements (SLAs). This paper presents a bottom-up open-source NFV analysis platform (NFV-Inspector) to (1) systematically profile and classify VNFs based on resource capacities, traffic demand rate, underlying system properties, placement of VNFs in the network, etc. and (2) extract/calculate the correlation among the QoS metrics and resource utilization of VNFs. We evaluated our approach using an emulated virtual Evolved Packet Core platform (Open5GCore) to showcase how complex relation among various NFV service chains can be systematically profiled and analyzed.

  • 46.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Al-Dulaimy, Auday
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    PerfSim: A Performance Simulator for Cloud Native Microservice Chains2023In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, no 2, p. 1395-1413Article in journal (Refereed)
    Abstract [en]

    Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

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  • 47.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Boodaghian Asl, Arsineh
    KTH, Sweden.
    Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning ApproachManuscript (preprint) (Other academic)
  • 48.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Darula, Marian
    R&D Technology and Industry, Ericsson, Sweden.
    Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach2018In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Discovering insights about Virtual Network Function (VNFs) resource demand characteristics will enable cloud vendors to optimize their underlying Network Function Virtualization (NFV) system orchestration and dramatically mitigate CapEx and OpEx spendings. However, analyzing large-scale NFV systems, especially in mobile network environments, is a challenging task and requires tailor-made approaches for each particular application. In this demo, we showcase NFV-Inspector, an open source and extensible VNF analysis platform that is capable of systematically benchmark and profile NFV deployments. Based on its pluggable framework, NFV-Inspector classifies VNFs resource demand characteristics and correlate their Key Performance Indicators (KPIs) with system-level Quality of Service (QoS) measurements. 

    Download full text (pdf)
    fulltext
  • 49.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Khoshkholghi, Mohammad Ali
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Cartwright, Carolyn
    Ericsson, Stockholm.
    Darula, Marian
    Ericsson, Stockholm.
    Deng, Shuiguang
    University, Hangzhou, CHN.
    A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions2020In: 2020 6th IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020, p. 292-300Conference paper (Refereed)
    Abstract [en]

    Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2–5 times more computing resources than the PTE.

  • 50.
    Harandi, Mehrtash
    et al.
    NICTASchool of ITEE, The University of Queensland, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Lovell, Brian C
    NICTASchool of ITEE, The University of Queensland.
    Ensemble Learning for Object Recognition and Tracking2011In: Pattern Recognition, Machine Intelligence and Biometrics, Springer Berlin/Heidelberg, 2011, 1, p. 261-278Chapter in book (Refereed)
1234 1 - 50 of 173
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