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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.
    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)
  • 10.
    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)
  • 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.
    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)
  • 12.
    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)
  • 13.
    Casas, Israel
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 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 environments2016In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511Article in journal (Refereed)
  • 14.
    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. 

  • 15.
    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.
    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: IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Piscataway, New Jersey, USA: 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.

  • 16.
    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.
    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)
  • 17.
    Deng, Shuiguang
    et al.
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
    Huang, Longtao
    Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Yin, Jianwei
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
    Zhou, MengChu
    Institute of Systems Engineering, Macau University of Science and Technology, Macau, China.
    Zomaya, Albert Y.
    School of Information Technologies, University of Sydney, Sydney, NSW, Australia.
    Mobility-Aware Service Composition in Mobile Communities2017In: ISSN 2168-2216, Vol. 47, no 3, p. 555-568Article in journal (Refereed)
    Abstract [en]

    The 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.

  • 18.
    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)
  • 19.
    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)
  • 20.
    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.

  • 21.
    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)
  • 22.
    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)
  • 23. 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.

  • 24.
    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)
  • 25.
    HoseinyFarahabady, MohammadReza
    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.
    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: International Conference on Parallel Processing (ICPP), Piscataway, New Jersey, USA: 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.

  • 26.
    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)
  • 27.
    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.

  • 28.
    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.

  • 29.
    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.

  • 30.
    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)
  • 31.
    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)
  • 32.
    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)
  • 33.
    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)
  • 34.
    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.

  • 35.
    Nguyen, Van Giang
    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.
    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.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. 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)
  • 36.
    Nguyen, Van-Giang
    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, Centre for HumanIT. 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. 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. 

  • 37.
    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.

  • 38.
    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)
  • 39.
    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. 

  • 40.
    Ramezani, Fahimeh
    et al.
    Univ Technol Sydney, Australia.
    Lu, Jie
    Univ Technol Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Hussain, Farookh Khadeer
    Univ Technol Sydney, Australia.
    Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments2015In: World wide web (Bussum), ISSN 1386-145X, E-ISSN 1573-1413, Vol. 18, no 6, p. 1737-1757Article in journal (Refereed)
  • 41.
    Ramezani, Fahimeh
    et al.
    Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
    Lu, Jie
    Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
    Zomaya, Albert Y
    School of Information Technologies, University of Sydney, Australia.
    A Multi-Objective Load Balancing System for Cloud Environments2017In: Computer journal, ISSN 0010-4620, E-ISSN 1460-2067, Vol. 60, no 9, p. 1316-1337Article in journal (Refereed)
    Abstract [en]

    Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time- and cost-consuming as it requires the transfer of large size files or memory pages and consumes a huge amount of power and memory for the origin and destination physical machine (PM), especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, we develop a Multi-objective Load Balancing (MO-LB) system that avoids VM migration and achieves system load balancing by transferring extra workload from a set of VMs allocated on an overloaded PM to other compatible VMs in the cluster with greater capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, MO-LB contains a CPU Usage Prediction (CUP) sub-system. The CUP not only predicts the performance of the VMs but also determines a set of appropriate VMs with the potential to execute the extra workload imposed on the VMs of an overloaded PM. We also design a Multi-Objective Task Scheduling optimization model using Particle Swarm Optimization to migrate the extra workload to the compatible VMs. The proposed method is evaluated using a VMware-vSphere-based private cloud in contrast to the VM migration technique applied by vMotion. The evaluation results show that the MO-LB system dramatically increases VM performance while reducing service response time, memory usage, job makespan, power consumption and the time taken for the load balancing process.

  • 42.
    Rizvandi, Nikzad Babaii
    et al.
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Moraveji, Reza
    The University of Sydney, Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    A Study on Using Uncertain Time Series Matching Algorithms for Map-Reduce Applications2013In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 25, no 12, p. 1699-1718Article in journal (Refereed)
  • 43.
    Rizvandi, Nikzad Babaii
    et al.
    The University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Moraveji, Reza
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Network Load Analysis and Provisioning of MapReduce Applications2012In: The Thirteenth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2012), IEEE Computer Society, 2012, p. 161-166Conference paper (Refereed)
  • 44.
    Rizvandi, Nikzad Babaii
    et al.
    University of Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Moraveji, Reza
    University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    On Modelling and Prediction of Total CPU Usage for Applications in MapReduce Environments2012In: Algorithms and Architectures for Parallel Processing: 12th International Conference, ICA3PP 2012, Fukuoka, Japan, September 4-7, 2012, Proceedings, Part I, Springer Berlin/Heidelberg, 2012, p. 414-427Conference paper (Refereed)
  • 45. Rizvandi, Nikzad Babaii
    et al.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    On Using Pattern Matching Algorithms in MapReduce Applications2011In: IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2011), IEEE Press, 2011Conference paper (Refereed)
  • 46.
    Rizvandi, Nikzad Babaii
    et al.
    Center for Distributed and High Performance Compu ting, School of Information Technologies, Universit y of Sydney .
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Preliminary Results on Using Matching Algorithms in Map-Reduce Applications2011Report (Refereed)
  • 47.
    Rizvandi, Nikzad Babaii
    et al.
    The University of Sydney, Australia, Natl ICT Australia NICTA, Sydney, NSW 1430, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Some Observations on Optimal Frequency Selection in DVFS–based Energy Consumption Minimization2011In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 71, no 8, p. 1154-1164Article in journal (Refereed)
  • 48.
    Rizvandi, Nikzad Babaii
    et al.
    The University of Sydney, Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Boloori, Ali Javadzadeh
    The University of Sydney, Sydney, Australia, Networked Systems Theme, National ICT Australia (NICTA), Australian Technology Park, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Preliminary Results: Modeling Relation between Total Execution Time of MapReduce Applications and Number of Mappers/Reducers2011Report (Refereed)
  • 49.
    Rizvandi, Nikzad Babaii
    et al.
    The University of Sydney, Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Lee, Young Choon
    The University of Sydney, Sydney, Australia.
    Boloori, Ali Javadzadeh
    The University of Sydney, Sydney, Australia.
    Taheri, Javid
    The University of Sydney, Australia.
    Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption2012In: Energy Efficient Distributed Computing / [ed] Albert Y. Zomay, Young Choon Lee, Hoboken, New Jersey: John Wiley & Sons, 2012Chapter in book (Refereed)
  • 50.
    Samani, Hamid R. Dehghani
    et al.
    Univ Sydney, Sch Informat Technol, Ctr Distributed & High Performance Comp, Sydney, NSW 2006, Australia..
    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, Ctr Distributed & High Performance Comp, Sydney, NSW 2006, Australia..
    RBT-MF: a Distributed Rubber Band Technique for Maximum Flow Problem in Azure2016In: PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS) / [ed] Chen, J Yang, LT, IEEE, 2016, p. 489-496Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a novel approach based on a rubber band technique to solve the " maximum flow" problem for a single-source, single-sink directed graph implemented on the Microsoft Azure cloud platform. The problem of finding the maximum possible flow for a given graph is a classic network optimization problem that arises in several real life circumstances. Having proposed several solutions to this problem in traditional distributed platforms, a solution that explicitly takes advantage of the cloud paradigm has not yet been thoroughly investigated. The rubber band technique, which is inspired by the behavior of an elastic rubber band on a plate with several poles, has been exploited in this paper to design a solution to the problem. Our experimental results show that the proposed technique can effectively find an answer for the maximum flow problem in a graph. As the size of the graph in terms of number of nodes, number of edges and flow value increase, the proposed scheme outperforms in comparison to the selected benchmarks.

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