Efficient resource management techniques in cloud computing environment: a review and discussion

References

• de Oliveira-GRR20112021 FAN, Risso-GRR20120726 JVT . Dynamic resource allocation in software defined and virtual networks: a comparative analysis .
   Google Scholar
• Bari MF , Boutaba R , Esteves R et al . Data center network virtualization: a survey. IEEE Commun Surv Tutorials. 2013;15(2):909–928.10.1109/SURV.2012.090512.00043
   Web of Science ®Google Scholar
• Endo PT , Batista MS , Goncalves GE , et al . Self-organizing strategies for resource management in Cloud Computing: State-of-the-art and challenges. 2nd IEEE Latin American Conference on Cloud Computing and Communications (LatinCloud); Maceio, Brazil; 2013.
   Google Scholar
• Nguyen Van H , Dang Tran F , Menaud J-M . Autonomic virtual resource management for service hosting platforms. Proceedings of the ICSE Workshop on Software Engineering Challenges of Cloud Computing; IEEE Computer Society; Vancouver, Canada; 2009.
   Google Scholar
• Ullrich M , Lässig J , Gaedke M . Towards efficient resource management in cloud computing: a survey. IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud); Vienna, Austria; 2016.
   Google Scholar
• Mell P , Grance T . The NIST definition of cloud computing; 2011.
   Google Scholar
• Lee G , Katz RH . Heterogeneity-aware resource allocation and scheduling in the cloud. HotCloud; 2011.
   Google Scholar
• Ngenzi A , Nair SR . Dynamic resource management in Cloud datacenters for Server consolidation . arXiv preprint arXiv:1505.00577; 2015.
   Google Scholar
• Coffman EG , Garey MR , Johnson DS . Dynamic bin packing. SIAM J Comput. 2006;12(2):227–258.
   Google Scholar
• Koneru S , Uddandi VR , Kavuri S . Resource allocation method using scheduling methods for parallel data processing in cloud. Int J Comput Sci Inf Technol [IJCSIT]. 2012;3(4):4625–4628.
   Google Scholar
• Maguluri ST , Srikant R , Ying L . Stochastic models of load balancing and scheduling in cloud computing clusters. International Conference on Computer Communications; Orlando, FL; 2012.
   Google Scholar
• Kang Z , Wang H . A novel approach to allocate cloud resource with different performance traits. IEEE International Conference on Services Computing (SCC); Santa Clara, CA; 2013.
   Google Scholar
• Zhang Q , Cheng L , Boutaba R . Cloud computing: state-of-the-art and research challenges. J Internet Services Appl. 2010;1(1):7–18.10.1007/s13174-010-0007-6
   Google Scholar
• Hadji M , Zeghlache D . Minimum cost maximum flow algorithm for dynamic resource allocation in clouds. IEEE 5th International Conference on Cloud Computing (CLOUD); Honolulu, HI; 2012. p. 876–882.
   Google Scholar
• Yang HC , Dasdan A , Hsiao RL , et al . Map-reduce-merge: simplified relational data processing on large clusters. Proceedings of the ACM SIGMOD international conference on Management of data; Beijing, China: ACM; 2007.
   Google Scholar
• Jung G , Sim KM . Agent-based adaptive resource allocation on the cloud computing environment. 40th International Conference on Parallel Processing Workshops (ICPPW); Taipei City, Taiwan; 2011.
   Google Scholar
• Feng T , Bi J , Wang K . Joint allocation and scheduling of network resource for multiple control applications in SDN. IEEE Network Operations and Management Symposium (NOMS); Krakow, Poland; 2014.
   Google Scholar
• Moens H , Hanssens B , Dhoedt B , et al . Hierarchical network-aware placement of service oriented applications in clouds. IEEE Network Operations and Management Symposium (NOMS); Krakow, Poland; 2014.
   Google Scholar
• Di S , Wang C-L . Dynamic optimization of multiattribute resource allocation in self-organizing clouds. IEEE Trans Parallel Distrib Syst. 2013;24(3):464–478.10.1109/TPDS.2012.144
   Web of Science ®Google Scholar
• Leelipushpam PGJ , Sharmila J . Live VM migration techniques in cloud environment–a survey. IEEE Conference on Information & Communication Technologies (ICT); Thuckalay, Tamil Nadu, India; 2013.
   Google Scholar
• Rabbani M . Resource management in virtualized data center; 2014.
   Google Scholar
• Sonkar S , Kharat M . A review on resource allocation and VM scheduling techniques and a model for efficient resource management in cloud computing environment. International Conference on ICT in Business Industry & Government (ICTBIG); Anaheim, California; 2016.
   Google Scholar
• Singh G , Behal S , Taneja M . Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing. Procedia Comput Sci. 2015;57:91–103.10.1016/j.procs.2015.07.373
   Google Scholar
• Kale RMCO . Virtual machine migration techniques in cloud environment: a survey .
   Google Scholar
• Vyas N , Chauhan A . A survey on virtual machine migration techniques in cloud computing .
   Google Scholar
• Yang Y , et al . Disk failure prediction model for storage systems based on disk SMART technology. Int J Comput Appl. 2015;37(3–4):111–119.10.1080/1206212X.2016.1188562
   Google Scholar
• Hines M , Deshpande, U , Gopalan K . Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning. OSR. 2009;43(3): 14–26.
   Google Scholar
• Jin H , Pan D , Xu J , et al . Efficient VM placement with multiple deterministic and stochastic resources in data centers. Global Communications Conference; Anaheim, California; 2012.
   Google Scholar
• Zhang L , et al . Moving Big Data to the cloud: an online cost-minimizing approach. IEEE Journal on Selected Areas in Communications. 2013;31(12):2710–2721.10.1109/JSAC.2013.131211
   Web of Science ®Google Scholar
• Hwang I , Pedram M . Hierarchical Virtual Machine Consolidation in a Cloud Computing System. International Conference on Cloud Computing; Santa Clara, California; 2013.
   Google Scholar
• Wood T , et al . Memory buddies: exploiting page sharing for server consolidation in virtualized data centers. Citeseer; 2007. (Technical Report).
   Google Scholar
• Vernekar A , Anandalingam G , Dorny C . Optimization of resource location in hierarchical computer networks. Comput Oper Res. 1990;17(4):375–388.10.1016/0305-0548(90)90016-Z
   Web of Science ®Google Scholar
• Wood T , Shenoy PJ , Venkataramani A , et al . Black-box and gray-box strategies for virtual machine migration. NSDI’07 Proceedings of the 4th USENIX Conference on Networked Systems Design & Implementation; Cambridge, MA; 2007. p. 17.
   Google Scholar
• Kumar M , Sharma SC . Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. Int J Comput Appl. 2017;1–10.
   Google Scholar
• Manvi SS , Shyam GK . Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Network Comput Appl. 2014;41:424–440.10.1016/j.jnca.2013.10.004
   Web of Science ®Google Scholar
• Parikh SM . A survey on cloud computing resource allocation techniques. Engineering (NUiCONE) . Nirma University International Conference; Ahmedabad, Gujarat India; 2013.
   Google Scholar
• Madni SHH , et al . Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. J Network Comput Appl. 2016;68:173–200.10.1016/j.jnca.2016.04.016
   Web of Science ®Google Scholar
• Vakilinia S , Ali MM , Qiu D . Modeling of the resource allocation in cloud computing centers. Comput Network. 2015;91:453–470.10.1016/j.comnet.2015.08.030
   Web of Science ®Google Scholar
• Lee L-T , et al . A dynamic resource management with energy saving mechanism for supporting cloud computing. Int J Grid Distrib Comput. 2013;6(1):67–76.
   Google Scholar
• Zhang Y . Classified scheduling algorithm of big data under cloud computing. Int J Comput Appl. 2017;1–6.
   Web of Science ®Google Scholar
• Younge AJ , Von Laszewski G , Wang L , et al . Efficient resource management for cloud computing environments. International Green Computing Conference; Chicago, IL; 2010.
   Google Scholar
• Kong W , Lei Y , Ma J . Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism. Optik. 2016;127(12):5099–5104.10.1016/j.ijleo.2016.02.061
   Web of Science ®Google Scholar
• Kim N , Cho J , Seo E . Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Gener Comput Syst. 2014;32:128–137.10.1016/j.future.2012.05.019
   Web of Science ®Google Scholar
• Ding Y , et al . Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener Comput Syst. 2015;50:62–74.10.1016/j.future.2015.02.001
   Web of Science ®Google Scholar
• Chen LY , Ansaloni D , Smirni E , et al . Achieving application-centric performance targets via consolidation on multicores: myth or reality? Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing; ACM; 2012.
   Google Scholar
• Wang W , Jiang Y , Wu W . Multiagent-based resource allocation for energy minimization in cloud computing systems . IEEE Trans Syst Man Cybern Syst; 2017;47:205–220.
   Google Scholar
• Kim KH , Beloglazov A , Buyya R . Power-aware provisioning of cloud resources for real-time services. Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science; 2009.
   Google Scholar
• Mishra M , Das A , Kulkarni P . Dynamic resource management using virtual machine migrations . IEEE Commun Mag. 2012;50(9):34–40.
   Google Scholar
• Clark C , Fraser K , Hand S , et al . Live migration of virtual machines. Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation. Vol. 2; Cairo, Egypt: USENIX Association; 2005. p. 273–286.
   Google Scholar
• Bacalhau ET , Usberti FL , Lyra C . A dynamic programming approach for optimal allocation of maintenance resources on power distribution networks. IEEE Power and Energy Society General Meeting (PES); 2013.
   Google Scholar
• Awasare V , Deshmukh S . Survey and comparative study on resource allocation strategies in cloud computing environment. IOSR J Comput Eng. 2014;16(2):94–101.10.9790/0661
   Google Scholar
• Abdelaal MA , Ebrahim GA , Anis WR . Network-aware resource management strategy in cloud computing environments. 11th International Conference on Computer Engineering & Systems (ICCES); Cairo, Egypt; 2016. p. 26–31.
   Google Scholar
• Verma M , et al . Dynamic resource demand prediction and allocation in multi-tenant service clouds. Concurrency and Computation: Practice and Experience; 2016.
   Google Scholar
• Nair TG , Vaidehi M . Efficient resource arbitration and allocation strategies in cloud computing through virtualization. IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS); Beijing, China; 2011. p. 397–401.
   Google Scholar
• Huang C-J , et al . An adaptive resource management scheme in cloud computing. Eng Appl Artif Intell. 2013;26(1):382–389.10.1016/j.engappai.2012.10.004
   Web of Science ®Google Scholar
• Lin W , et al . A threshold-based dynamic resource allocation scheme for cloud computing. Procedia Eng. 2011;23:695–703.10.1016/j.proeng.2011.11.2568
   Google Scholar
• Xu B , et al . Job scheduling algorithm based on Berger model in cloud environment. Adv Eng Software. 2011;42(7):419–425.10.1016/j.advengsoft.2011.03.007
   Web of Science ®Google Scholar
• Singh AN , Prakash S . Challenges and opportunities of resource allocation in cloud computing: a survey. 2nd International Conference on Computing for Sustainable Global Development (INDIACom); New Delhi, India; 2015. p. 2047–2051.
   Google Scholar
• Zhu X , Wang J , Guo H , et al . Fault-tolerant scheduling for real-time scientific workflows with elastic resource provisioning in virtualized clouds. IEEE Trans Parallel Distrib Syst. 2016;27(12):3501–3517.10.1109/TPDS.2016.2543731
   Web of Science ®Google Scholar
• Shi W , et al . An online auction framework for dynamic resource provisioning in cloud computing. IEEE/ACM Trans Network. 2016;24(4):2060–2073.10.1109/TNET.2015.2444657
   Web of Science ®Google Scholar
• Rodriguez, MA , Buyya R . Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds . Cloud Comput. 2014;2(2):222–235.
   Google Scholar
• Shawky DM . Performance evaluation of dynamic resource allocation in cloud computing platforms using Stochastic Process Algebra. 8th International Conference on Computer Engineering & Systems (ICCES); Cairo, Egypt; 2013. p. 39–44.
   Google Scholar
• Bernardo M , Donatiello L , Ciancarini P . Stochastic process algebra: from an algebraic formalism to an architectural description language. IFIP International Symposium on Computer Performance Modeling, Measurement and Evaluation; Rome, Italy: Springer; 2002. p. 236–260.
   Google Scholar
• Baeten JC , Weijland WP . Process algebra, volume 18 of Cambridge tracts in theoretical computer science; Cambridge: University Press Cambridge; 1990.
   Google Scholar
• Hermanns H , Herzog U , Mertsiotakis V . Stochastic process algebras as a tool for performance and dependability modelling. Proceedings of the International Computer Performance and Dependability Symposium; Erlangen, Germany; 1995. p. 102–111.
   Google Scholar
• Herzog U . Formal description, time and performance analysis a framework. Entwurf und Betrieb verteilter Systeme; Springer; 1990, p. 172–190.
   Google Scholar
• Khethavath P , Thomas J , Chan-Tin E , et al . Introducing a distributed cloud architecture with efficient resource discovery and optimal resource allocation. IEEE Ninth World Congress on Services (SERVICES); 2013.
   Google Scholar
• Chung WC , Hsu CJ , Lai KC , et al . Direction-aware resource discovery service in large-scale grid and cloud computing. IEEE International Conference on Service-Oriented Computing and Applications (SOCA); Irvine, CA; 2011. p. 1–8.
   Google Scholar
• Yazir YO , Matthews C , Farahbod R , et al . Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis. IEEE 3rd International Conference on Cloud Computing (CLOUD); 2010.
   Google Scholar
• Singh A , Juneja D , Malhotra M . A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing . J King Saud University Comput Inf Sci; 2017;29(1):19–28.
   Google Scholar
• Javadi B , Abawajy J , Buyya R . Failure-aware resource provisioning for hybrid Cloud infrastructure. J Parallel Distrib Comput. 2012;72(10):1318–1331.10.1016/j.jpdc.2012.06.012
   Web of Science ®Google Scholar
• Kaur P , Mehta S . Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. J Parallel Distrib Comput. 2017;101:41–50.10.1016/j.jpdc.2016.11.003
   Web of Science ®Google Scholar
• Yang S , et al . Energy-aware provisioning in optical cloud networks. Comput Networks. 2017;118:78–95.10.1016/j.comnet.2017.03.008
   Web of Science ®Google Scholar
• Cheraghlou MN , Khadem-Zadeh A , Haghparast M . A survey of fault tolerance architecture in cloud computing. J Network Comput Appl. 2016;61:81–92.10.1016/j.jnca.2015.10.004
   Web of Science ®Google Scholar
• Ghobaei-Arani M , Jabbehdari S , Pourmina MA . An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Future Gener Comput Syst. 2017.
   Web of Science ®Google Scholar
• Katsaros G , et al . A service framework for energy-aware monitoring and VM management in clouds. Future Gener Comput Syst. 2013;29(8):2077–2091.10.1016/j.future.2012.12.006
   Web of Science ®Google Scholar
• Rong H , et al . Optimizing energy consumption for data centers. Renew Sust Energy Rev. 2016;58:674–691.10.1016/j.rser.2015.12.283
   Web of Science ®Google Scholar
• Pietri I , Sakellariou R . Mapping virtual machines onto physical machines in cloud computing: a survey. ACM Comput Surv (CSUR). 2016;49(3):49.
   Web of Science ®Google Scholar
• Chaudhari A , Kapadia A . Load Balancing Algorithm for Azure Virtualization with Specialized VM . algorithms. 2013;1:2.
   Google Scholar
• Sran N , Kaur N . Comparative analysis of existing load balancing techniques in cloud computing. Int J Eng Sci Invent. 2013;2(1):60–63.
   Google Scholar
• Katyal M , Mishra A . A comparative study of load balancing algorithms in cloud computing environment . arXiv preprint arXiv:1403.6918; 2014.
   Google Scholar
• Pinto P . Introducing the Min-Max Algorithm. Submited to the AI Depot article contest. 2002;1–10.
   Google Scholar
• Ld DB , Krishna PV . Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput. 2013;13(5):2292–2303.
   Web of Science ®Google Scholar
• Kaur S , Kaur A , Gobindgarh M . Energy aware resources allocation heuristic for efficient management of data centers for cloud computing; 2016.
   Google Scholar
• Beloglazov A , Abawajy J , Buyya R . Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Gener Comput Syst. 2012;28(5):755–768.10.1016/j.future.2011.04.017
   Web of Science ®Google Scholar
• Garg SK , et al . Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers. J Parallel Distrib Comput. 2011;71(6):732–749.10.1016/j.jpdc.2010.04.004
   Web of Science ®Google Scholar
• Burge J , Ranganathan P , Wiener JL . Cost-aware scheduling for heterogeneous enterprise machines (CASH’EM). IEEE International Conference on Cluster Computing; Austin, TX; 2007. p. 481–487.
   Google Scholar
• Bradley DJ , Harper RE , Hunter SW . Workload-based power management for parallel computer systems . IBM J Res Dev. 2003;47(5.6):703–718.
   Web of Science ®Google Scholar
• Lefèvre L , Orgerie A-C . Designing and evaluating an energy efficient Cloud. J Supercomput. 2010;51(3):352–373.10.1007/s11227-010-0414-2
   Web of Science ®Google Scholar
• Salfner F , Lenk M , Malek M . A survey of online failure prediction methods. ACM Computing Surveys (CSUR). 2010;42(3):10.
   Web of Science ®Google Scholar
• Wang, C.-F , Hung W-Y , Yang C-S . A prediction based energy conserving resources allocation scheme for cloud computing. IEEE International Conference on Granular Computing (GrC); Noboribetsu, Hokkaido, Japan; 2014. p. 320–324.
   Google Scholar
• Chen Y , Das A , Qin W , et al . Managing server energy and operational costs in hosting centers. ACM SIGMETRICS performance evaluation review; 2005.
   Google Scholar
• Mezmaz M , Melab N , Kessaci Y , et al . A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput. 2011;71(11):1497–1508.10.1016/j.jpdc.2011.04.007
   Web of Science ®Google Scholar
• Tesfatsion SK , Wadbro E , Tordsson J . A combined frequency scaling and application elasticity approach for energy-efficient cloud computing. Sustainable Comput Inf Syst. 2014;4(4):205–214.
   Web of Science ®Google Scholar
• Khosravi A , Garg SK , Buyya R . Energy and carbon-efficient placement of virtual machines in distributed cloud data centers. European Conference on Parallel Processing; Aachen, Germany: Springer; 2013. p. 317–328.
   Google Scholar
• Subrata R , Zomaya AY , Landfeldt B . Cooperative power-aware scheduling in grid computing environments. J Parallel Distrib Comput. 2010;70(2):84–91.10.1016/j.jpdc.2009.09.003
   Web of Science ®Google Scholar
• Islam T , Manivannan D , Zeadally S . A classification and characterization of security threats in cloud computing . Int J Next-Gener Comput. 2016; 7 (1).
   Google Scholar
• Gholami A , Laure E . Security and privacy of sensitive data in cloud computing: a survey of recent developments . arXiv preprint arXiv:1601.01498; 2016.
   Google Scholar
• Lomotey RK , Deters R . Saas authentication middleware for mobile consumers of iaas cloud. IEEE Ninth World Congress on Services (SERVICES); 2013.
   Google Scholar
• Kim H , Timm SC . X. 509 authentication and authorization in fermi cloud. Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing; London, England: IEEE Computer Society; 2014. p. 732–737.
   Google Scholar
• Popa RA , Lorch JR , Molnar D , et al.. Enabling security in cloud storage SLAs with CloudProof. USENIX Annual Technical Conference; 2011.
   Google Scholar
• Garfinkel T , Pfaff B ., Chow J , et al . Terra: a virtual machine-based platform for trusted computing. ACM SIGOPS Operating Systems Review; 2003.
   Google Scholar
• Santos N , Gummadi KP , Rodrigues R . Towards Trusted Cloud Computing. Proceedings of the 2009 Conference on Hot Topics in Cloud Computing; San Diego, CA; 2009. p. 1–5.
   Google Scholar
• Zhu S , Gong G . Fuzzy authorization for cloud storage. IEEE Trans Cloud Comput. 2014;2(4):422–435.10.1109/TCC.2014.2338324
   Google Scholar
• Pillai P , Shin KG . Real-time dynamic voltage scaling for low-power embedded operating systems. ACM SIGOPS Operating Systems Review; 2001.
   Google Scholar
• Martin SM , Flautner K , Mudge T , et al . Combined dynamic voltage scaling and adaptive body biasing for lower power microprocessors under dynamic workloads. Proceedings of the 2002 IEEE/ACM International Conference on Computer-Aided Design; 2002.
   Google Scholar
• Kim KH , Beloglazov A , Buyya R . Power-aware provisioning of virtual machines for real-time Cloud services. Concurr Comput Pract Exper. 2011;23(13):1491–1505.10.1002/cpe.v23.13
   Web of Science ®Google Scholar
• Wang X , et al . An intelligent economic approach for dynamic resource allocation in cloud services. IEEE Trans Cloud Comput. 2015;3(3):275–289.10.1109/TCC.2015.2415776
   Web of Science ®Google Scholar
• Amiri M , Mohammad-Khanli L . Survey on prediction models of applications for resources provisioning in cloud. J Network Comput Appl. 2017;82:93–113.
   Web of Science ®Google Scholar
• Prasad B , Angel S . Predicting future resource requirement for efficient resource management in cloud.
   Google Scholar