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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.
    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: The Institution of Engineering and Technology , 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.

  • 10.
    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). Karlstad Univ, Dept Comp Sci, S-65188 Karlstad, Sweden..
    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.

  • 11.
    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)
  • 12.
    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)
  • 13.
    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)
  • 14.
    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)
  • 15.
    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)
  • 16.
    Casas, Israel
    et al.
    Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    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. 

  • 17.
    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.

  • 18.
    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: The Institution of Engineering and Technology , 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.

  • 19.
    Cho, Daewoong
    et al.
    University of Sydney, Australia.
    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.
    Wang, Lizhe
    China University of Geosciences, P. R. China.
    Virtual Network Function Placement: Towards Minimizing Network Latency and Lead Time2017In: 2017 9TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE, IEEE, 2017, p. 90-97Chapter in book (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 efficiently allocate Virtual Network Functions (VNF) to Virtual Machines (VMs). Although a significant amount of work/research has been already conducted for the generic VNF placement problem, network latency among various network components has not been comprehensively considered yet. To address this concern, in this article, we design a more comprehensive model based on real measurements to capture network latency among VNFs with more granularity to optimize placement of VNFs in CDCs. Experimental results are promising and indicate that our approach, namely VNF Low-Latency Placement (VNF-LLP), can reduce network latency by up to 64.24% (50.33% in average) compared with two generic algorithms. Furthermore, it has a lower lead time (time to find a suitable VM to host a VNF) as compared with two classic approaches.

  • 20.
    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.

  • 21.
    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 1349-2543, IEEE Transactions on Systems, Man, and Cybernetics: Systems, ISSN 2168-2216, 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.

  • 22.
    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)
  • 23.
    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.
    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)
  • 24.
    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.

  • 25.
    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). Karlstad University, Sweden..
    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.

  • 26.
    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)
  • 27. Fazio, Maria
    et al.
    Ranjan, Rajiv
    Girolami, Michele
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Dustdar, Schahram
    Villari, Massimo
    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.

  • 28.
    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)
  • 29. Harandi, Mehrtash
    et al.
    Taheri, Javid
    The University of Sydney, Australia.
    Lovell, Brian C.
    Machine Learning Applications in Computer Vision2013In: Image Processing: Concepts, Methodologies, Tools, and Applications / [ed] Information Resources Management Association, Hershey, PA, USA: IGI Global, 2013, p. 896-921Chapter in book (Refereed)
    Abstract [en]

    Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed; the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.

  • 30.
    Harandi, Mehrtash
    et al.
    NICTA, Australia & The University of Queensland, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Lovell, Brian C.
    NICTA, Australia & The University of Queensland, Australia).
    Machine Learning Applications in Computer Vision2012In: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques, IGI Global, 2012, 1, p. 99-132Chapter in book (Refereed)
  • 31.
    Hernandez Benet, Cristian
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Alizadeh Noghani, Kyoomars
    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 implementations and protocols2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, The Institution of Engineering and Technology , 2018, 1, p. 27-48Chapter in book (Refereed)
    Abstract [en]

    This chapter begins by explaining the main SDN concepts with the focus on a SDN controller. It presents the most important aspects to consider when we desire to go from traditional network to a SDN networks. We present an in-depth analysis of the most commonly used and modern SDN controllers and analyse the main features, capabilities and requirements of one of the presented controllers. OpenFlow is the standard leading in the market allowing the management of the forwarding plane devices such as routers or switches. While there are other standards with the same aim, OpenFlow has secured a position in the market and has been expanded rapidly. Therefore, an analysis is presented on a different OpenFlow compatible device for the implementation of an SDN network. This study encompasses both software and hardware solutions along with the scope of implementation or use of these devices. This chapter ends up presenting a description of OpenFlow protocol alternatives, a more detailed description of OpenFlow and its components and other wellknown southbound protocols involved for the management and configuration of the devices.

  • 32.
    Hoseiny Farahabady, Mohammad Reza
    et al.
    Sch. of IT, 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).
    Tari, Zahir
    Sch. of Sci., RMIT Univ., Melbourne, VIC, Australia.
    Zomaya, Albert Y
    School of Information Technologies, University of Sydney.
    A Dynamic Resource Controller for a Lambda Architecture2017In: 2017 46th International Conference on Parallel Processing (ICPP), Piscataway: IEEE, 2017, p. 332-341Conference paper (Refereed)
    Abstract [en]

    Lambda architecture is a novel event-driven serverless paradigm that allows companies to build scalable and reliable enterprise applications. As an attractive alternative to traditional service oriented architecture (SOA), Lambda architecture can be used in many use cases including BI tools, in-memory graph databases, OLAP, and streaming data processing. In practice, an important aim of Lambda's service providers is devising an efficient way to co-locate multiple Lambda functions with different attributes into a set of available computing resources. However, previous studies showed that consolidated workloads can compete fiercely for shared resources, resulting in severe performance variability/degradation. This paper proposes a resource allocation mechanism for a Lambda platform based on the model predictive control framework. Performance evaluation is carried out by comparing the proposed solution with multiple resource allocation heuristics, namely enhanced versions of spread and binpack, and best-effort approaches. Results confirm that the proposed controller increases the overall resource utilization by 37% on average and achieves a significant improvement in preventing QoS violation incidents compared to others.

  • 33.
    Hoseinyfarahabady, M. Reza
    et al.
    The University of Sydney, 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.
    The University of Sydney, Australia.
    Tari, Zahir
    RMIT University, Australia.
    Khan, Samee Ullah
    North Dakota State University, United States.
    Toward designing a dynamic CPU cap manager for timely dataflow platforms2018In: HPC '18 Proceedings of the High Performance Computing Symposium / [ed] Watson L.T., Thacker W.I., Sosonkina M., Rupp K., Weinbub J., ACM Digital Library, 2018, Vol. 50, no 4, p. 60-70, article id 6Conference paper (Refereed)
    Abstract [en]

    In this work, we propose a control-based solution for the problem of CPU resource allocation in data-flow platform that considers the degradation of performance caused by running concurrent data-flow processes. Our aim is to cut the QoS violation incidents for applications belonging to the highest QoS class. The performance of the proposed solution is bench-marked with the famous round robin algorithm. The experimental results confirms that the proposed algorithm can decrease the latency of processing data records for applications by 48% compared to the round robin policy.

  • 34.
    Iftikhar, Mohsin
    et al.
    King Saud Univ, Riyadh, Saudi Arabia.
    Zuair, Mansour
    King Saud Univ, Riyadh, Saudi Arabia.
    Rahaal, Abdul Malik
    King Saud Univ, Riyadh, Saudi Arabia.
    Rahaal, Muhammad
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Landfeldt, Bjorn
    The Implementation of Novel Idea of Translation Matrix to Maintain QoS for a Roaming User between Heterogeneous 4G Wireless Networks2012In: IEEE 37th Conference on Local Computer Networks Workshops, IEEE Press, 2012, p. 718-725Conference paper (Refereed)
  • 35.
    Kim, Sung-Soo
    et al.
    South Korea.
    Byeon, Ji-Hwan
    South Korea.
    Taheri, Javid
    The University of Sydney, Australia.
    Liu, Hongbo
    China.
    Swarm Intelligent Approaches for Location Area Planning2014In: Journal of Multiple-Valued Logic and Soft Computing, ISSN 1542-3980, E-ISSN 1542-3999, Vol. 22, no 3, p. 287-306Article in journal (Refereed)
    Abstract [en]

    Location Area Planning (LAP) problem is to partition the cellular mobile network into location areas. It is very important to determine which one cellular network the users are. One strategy used in location management is to partition the network into location areas, in such a way that the handoff and paging costs will be minimized. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is a difficult combinatorial optimization problem. We make an attempt to solve the problem by the swarm intelligent algorithms, i.e. Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). They help us to obtain the optimal number of location areas and the corresponding configuration of the partitioned network. We also illustrate our approaches using the small, medium, and large size benchmark problems.

  • 36.
    Kim, Sung-Soo
    et al.
    South Korea.
    Kim, Gon
    South Korea.
    Byeon, Ji-Hwan
    South Korea.
    Taheri, Javid
    The University of Sydney, Australia.
    Particle Swarm Optimization For Location Mobility Management2012In: International Journal of Innovative Computing Information and Control, ISSN 1349-4198, E-ISSN 1349-418X, Vol. 8, no 12, p. 8387-8398Article in journal (Refereed)
    Abstract [en]

    In the generic mobile location problem for locating mobile terminals in a network, assignment of cells to either "reporting" or "non-reporting" cells is an NP-complete problem with known exponential complexity, also known as the reporting cell planning (RCP). The number of reporting cells as well as their locations must be carefully determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCP. In this paper, we propose binary particle swarm optimization (BPSO) for optimal design of RCP. Our extensive set of experimental simulations demonstrates the effectiveness of BPSO; BPSO also proved to be a competitive approach in terms of quality of solution for the optimal design of several benchmark problems. Results also provide invaluable insights into the nature of this classical formidable problem and its effective solutions.

  • 37.
    Lee, Young Choon
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    A Parallel Metaheuristic Framework Based on Harmony Search for Scheduling in Distributed Computing Systems2012In: International Journal of Foundations of Computer Science, ISSN 0129-0541, Vol. 23, no 2, p. 445-464Article in journal (Refereed)
    Abstract [en]

    A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.

  • 38.
    Li, Wei
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Seredynski, Franciszek
    Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland.
    Landfeldt, Bjorn
    Lund University, Lund, Sweden.
    Nature‐Inspired Computing for Autonomic Wireless Sensor Networks2013In: Large Scale Network-Centric Distributed Systems / [ed] Hamid Sarbazi-Azad, Albert Y. Zomaya, John Wiley & Sons, 2013, 1, p. 219-254Chapter in book (Refereed)
  • 39.
    Li, Yanbiao
    et al.
    Hunan University, Changsha, China.
    Zhang, Dafang
    Hunan University, Changsha, China.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Li, Keqin
    State University of New York, New York, NY.
    SDN components and OpenFlow2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: The Institution of Engineering and Technology , 2018, 1, p. 49-68Chapter in book (Refereed)
    Abstract [en]

    Today's Internet suffers from ever-increasing challenges in scalability, mobility, and security, which calls for deep innovations on network protocols and infrastructures. However, the distributed controlling mechanism, especially the bundle of control plane and the data plane within network devices, sharply restricts such evolutions. In response, the software-defined networking (SDN), an emerging networking paradigm, proposes to decouple the control and data planes, producing logically centralized controllers, simple yet efficient forwarding devices, and potential abilities in functionalities programming. This chapter presents a short yet comprehensive overview of SDN components and the OpenFlow protocol on basis of both classic and latest literatures. The topics range from fundamental building blocks, layered architectures, novel controlling mechanisms, and design principles and efforts of OpenFlow switches.

  • 40.
    Matloobi, Roozbeh
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Fuzzy modeling to predict performance of collocated virtual machines in private clouds2014In: Software, Telecommunications and Computer Networks (SoftCOM), 2014 22nd International Conference on, IEEE Press, 2014Conference paper (Refereed)
  • 41.
    Mendes, Reginaldo
    et al.
    SERPRO, Brasilia, Brazil.
    Pires, Paulo F.
    DCC/IM-Federal University of Rio de Janeiro, Brazil.
    Delicato, Flávia C.
    DCC/IM-Federal University of Rio de Janeiro, Brazil.
    Batista, Thais
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Using Semantic Web to Build and Execute Ad-Hoc Processes2011In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-2011), IEEE Press, 2011, p. 233-240Conference paper (Refereed)
  • 42.
    Moraveji, Reza
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Reza, Mohammad
    The University of Sydney, Sydney, Australia.
    Rizvandi, Nikzad Babaii
    The University of Sydney, Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Data-Intensive Workload Consolidation on Hadoop Distributed File System2012In: The 13th ACM/IEEE International Conference on Grid Computing (GRID’12), Beijing, China: IEEE Press, 2012, p. 95-103Conference paper (Refereed)
  • 43.
    Nasim, Robayet
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Optimizing Virtual Machine Consolidation in Virtualized Datacenters Using Resource Sensitivity2016In: Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, IEEE, 2016, p. 168-175Conference paper (Refereed)
    Abstract [en]

    In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used to achieve both energy-efficiency and load balancing among different physical machines (PMs). Using VM live migrations, we can consolidate VMs on a smaller number of hosts to power down unused PMs and save energy. Most migration schemes are however oblivious to the characteristics of services that run inside VMs, and thus may lead to migrations where VMs competing for the same resource type are packed on the same PM. As a result, VMs may suffer from significant resource contention and noticeable degradation in their performance. Using resource sensitivity values of VMs (ie, quantitative measures to reflect how much a VM is sensitive to its requested resources such as CPU, Mem, and Disk), we have designed a novel VM consolidation approach to optimize placement of VMs on available PMs. We validated our approach using five well-known applications/benchmarks with various resource demand signatures: varying from pure CPU/Mem/Disk-intensive to mixtures of them. Our extensive numerical evaluation illustrates that, for the same power consumption, our approach improve the performance of cloud services by 9 - 12\%, on average, when compared with current sensitivity oblivious approaches.

  • 44.
    Nguyen, Van Giang
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunström, Anna
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT.
    Grinnemo, Karl-Johan
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. 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). The University of Sydney, Australia.
    5G Mobile Networks – Requirements, Enabling Technologies, and Research Activities2017In: Comprehensive Guide to 5G Security / [ed] Madhusanka Liyanage, Andrei Gurtov, Mika Yliantilla, Ijaz Ahmed & Ahmed Bux Abro, John Wiley & Sons, 2017Chapter in book (Refereed)
  • 45.
    Nguyen, Van-Giang
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunström, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Grinnemo, Karl-Johan
    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 velocity in Big Data2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: The Institution of Engineering and Technology , 2018, 1, p. 207-228Chapter in book (Refereed)
    Abstract [en]

    Currently, improving the performance of Big Data in general and velocity in particular is challenging due to the inefficiency of current network management, and the lack of coordination between the application layer and the network layer to achieve better scheduling decisions, which can improve the Big Data velocity performance. In this chapter, we discuss the role of recently emerged software defined networking (SDN) technology in helping the velocity dimension of Big Data. We start the chapter by providing a brief introduction of Big Data velocity and its characteristics and different modes of Big Data processing, followed by a brief explanation of how SDN can overcome the challenges of Big Data velocity. In the second part of the chapter, we describe in detail some proposed solutions which have applied SDN to improve Big Data performance in term of shortened processing time in different Big Data processing frameworks ranging from batch-oriented, MapReduce-based frameworks to real-time and stream-processing frameworks such as Spark and Storm. Finally, we conclude the chapter with a discussion of some open issues.

  • 46.
    Nguyen, Van-Giang
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Brunström, Anna
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Grinnemo, Karl-Johan
    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). The University of Sydney, Australia.
    SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey2017In: IEEE Communications Surveys and Tutorials, ISSN 1553-877X, E-ISSN 1553-877X, Vol. 19, no 3, p. 1567-1602Article in journal (Refereed)
    Abstract [en]

    The emergence of two new technologies, namely Software Defined Networking (SDN) and Network Function Virtualization (NFV) have radically changed the development of network functions and the evolution of network architectures. These two technologies bring to mobile operators the promises of reducing costs, enhancing network flexibility and scalability, and shortening the time-to-market of new applications and services. With the advent of SDN and NFV and their offered benefits, the mobile operators are gradually changing the way how they architect their mobile networks to cope with ever-increasing growth of data traffic, massive number of new devices and network accesses, and to pave the way towards the upcoming fifth generation (5G) networking. This paper aims at providing a comprehensive survey of state-of-the-art research work, which leverages SDN and NFV into the most recent mobile packet core network architecture, Evolved Packet Core (EPC). The research work is categorized into smaller groups according to a proposed four-dimensional taxonomy reflecting the (1) architectural ap- proach, (2) technology adoption, (3) functional implementation, and (4) deployment strategy. Thereafter, the research work is exhaustively compared based on the proposed taxonomy and some added attributes and criteria. Finally, the paper identifies and discusses some major challenges and open issues such as scalability and reliability, optimal resource scheduling and allocation, management and orchestration, network sharing and slicing that raise from the taxonomy and comparison tables that need to be further investigated and explored. 

  • 47.
    Nguyen, Van-Giang
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Grinnemo, Karl-Johan
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. 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).
    Brunström, Anna
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    On Load Balancing for a Virtual and Distributed MME in the 5G CoreManuscript (preprint) (Other academic)
  • 48.
    Oljira, Dejene Boru
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Brunström, Anna
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Grinnemo, Karl-Johan
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Analysis of Network Latency in Virtualized Environments2016In: Global Communications Conference (GLOBECOM), 2016 IEEE, IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Virtualization is central to cloud computing systems. It abstracts computing resources to be shared among multiple virtual machines (VMs) that can be easily managed to run multiple applications and services. To benefit from the advantages of cloud computing, and to cope with increasing traffic demands, telecom operators have adopted cloud computing. Telecom services and applications are, however, characterized by real-time responsiveness, strict end-to-end latency, and high reliability. Due to the inherent overhead of virtualization, the network performance of applications and services can be degraded. To improve the performance of emerging applications and services that demand stringent end-to-end latency, and to understand the network performance bottleneck of virtualization, a comprehensive performance measurement and analysis is required. To this end, we conducted controlled and detailed experiments to understand the impact of virtualization on end-to-end latency and the performance of transport protocols in a virtualized environment. We also provide a packet delay breakdown in the virtualization layer which helps in the optimization of hypervisor components. Our experimental results indicate that the end-to-end latency and packet delay in the virtualization layer are increased with co-located VMs.

  • 49.
    Oljira, Dejene Boru
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Brunström, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Grinnemo, Karl-Johan
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Measurement and Analysis of Network Performance in Virtualized Environments2016In: Proceedings of the 12th Swedish National Computer Networking Workshop (SNCNW), June 2016, Sundsvall, Sweden, 2016Conference paper (Refereed)
  • 50.
    Oljira, Dejene Boru
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Grinnemo, Karl-Johan
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Brunstrom, Anna
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    A Model for QoS-Aware VNF Placement and Provisioning2017In: Network Function Virtualization and Software Defined Networks (NFV-SDN), 2017 IEEE Conference on / [ed] IEEE, IEEE, 2017Conference paper (Refereed)
    Abstract [en]

    Network Function Virtualization (NFV) is a promising solution for telecom operators and service providers to improve business agility, by enabling a fast deployment of new services, and by making it possible for them to cope with the increasing traffic volume and service demand. NFV enables virtualization of network functions that can be deployed as virtual machines on general purpose server hardware in cloud environments, effectively reducing deployment and operational costs. To benefit from the advantages of NFV, virtual network functions (VNFs) need to be provisioned with sufficient resources and perform without impacting network quality of service (QoS). To this end, this paper proposes a model for VNFs placement and provisioning optimization while guaranteeing the latency requirements of the service chains. Our goal is to optimize resource utilization in order to reduce cost satisfying the QoS such as end- to-end latency. We extend a related VNFs placement optimization with a fine-grained latency model including virtualization overhead. The model is evaluated with a simulated network and it provides placement solutions ensuring the required QoS guarantees. 

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