Total views : 188

Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment

Affiliations

  • Department of Computer Science and Engineering, MGIT, Gandipet Main Road, Kokapet, Hyderabad - 500075, Telangana, India
  • Department of Computer Science and Engineering, JNTU Vizianagaram, Dwarapudi, Vizianagaram - 535003, Andhra Pradesh, India

Abstract


Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms.

Keywords

Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

Full Text:

 |  (PDF views: 221)

References


  • Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems. 2009; 25(6):599-616. Available from: Crossref.
  • Brindha T, Shaji RS. An Efficient Framework for Providing Secured Transaction of Data in Cloud Environment. Indian Journal of Science and Technology. 2016 Nov; 9(44):1-6.Available from: Crossref.
  • Emeakaroha VC, Brandic I, Maurer M, Dustdar S. Cloud resource provisioning and SLA enforcement via LoM2HiS framework. Concurrency and Computation: Practice and Experience. 2013; 25(10):1462-81. Available from: Crossref.
  • Macias M, Guitart J. SLA negotiation and enforcement policies for revenue maximization and client classification in cloud providers. Future Generation Computer Systems.2014; 41:19-31. Available from: Crossref.
  • Rajkumar R, Mala T. Runtime Estimation Aware Scheduling Algorithm for Handling Deadline Based Tasks in Grid Environment. Advances in Intelligent and Soft Computing.2012; 132:555-62. Available from: Crossref.
  • Rajkumar R, Mala T. Achieving Service Level Agreement in Cloud Environment using Task Prioritization in Hierarchical Scheduling. Advances in Intelligent and Soft Computing.2012; 132:547-54. Available from: Crossref.
  • Zhuo T, Yanqing M, Kenli L, Keqin L. Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment. Journal of Supercomputing.2014; 70:1279-96.
  • Raju IRK, Varma PS, Sundari MVR, Moses GJ. Deadline Aware Two Stage Scheduling Algorithm in Cloud Computing.Indian Journal of Science and Technology. 2016 Jan; 9(4):1-10.
  • Diallo L, Hashim AHA, Olanrewaju RF, Islam S, Zarir AA.Two Objectives Big Data task Scheduling using Swarm Intelligence in Cloud Computing. Indian Journal of Science and Technology. 2016 Jul; 9(28):1-10. Available from: Crossref.
  • Usman MJ, Ismail AS, Chizari H, Gital AY, Aliyu A. A Conceptual Framework for Realizing Energy Efficient Resource Allocation in Cloud Data Centre. Indian Journal of Science and Technology. 2016; 9(46):1-8.
  • Komarasamy D, Muthuswamy V. A Novel Approach for Dynamic Load Balancing with Effective Bin Packing and VM Reconfiguration in Cloud. Indian Journal of Science and Technology. 2016; 9(11):1-6. Available from: Crossref.
  • Syed HHM, Muhammad SAL, Yahaya C, Shafi‘Ima. Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. Journal of Network and Computer Applications. 2016; 68:173-200.Available from: Crossref.
  • Akilandeswari P, Srimathi H, Srimathi H. Survey and analysis on Task scheduling in Cloud environment. Indian Journal of Science and Technology. 2016 Oct; 9(37):1-6.Available from: Crossref.
  • Antony T, Krishnalal G, Jagathy RVP. Credit Based Scheduling Algorithm in Cloud Computing Environment. Procedia Computer Science. 2015; 46:913-20. Available from: Crossref.
  • Rui Z, Kui W, Minming L, Jianping W. Online Resource Scheduling Under Concave Pricing for Cloud Computing.IEEE Transactions on Parallel and Distributed Systems.2016; 27(4):1131-45. Available from: Crossref.
  • Nuttapong N, Booncharoen S, Tiranee A. Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. Journal of Supercomputing. 2014; 68:1579-603.
  • Florin P, Ciprian D, Valentin C, Nik B, Fatos X, Leonard B. Deadline scheduling for a periodic tasks in inter-Cloud environments: a new approach to resource management.Journal of Supercomputing. 2015; 71:1754-65. Available from: Crossref.
  • Sanjaya KP, Prasanta KJ. Efficient task scheduling algorithms for heterogeneous multi-cloud environment. Journal of Supercomputing. 2015; 71:1505-33. Available from: https://doi.org/10.1007/s11227-014-1376-6.
  • Shafi’Ima, Muhammad SAL, Syed HHM, Mohammed A.Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm.Neural Computing and Applications. 2016; p. 1-15.
  • Sook Kyong C, Kwang Sik C, Heonchang Y. Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Cluster Computer. 2014; 17:911-26. Available from: Crossref.
  • Xiaoli W, Yuping W, Yue C. An energy-aware bi-level optimization model for multi-task scheduling problems under cloud computing. Soft Computer. 2016; 20:303-17. Available from: Crossref.
  • Tarandeep K, Inderveer C. Energy aware scheduling of deadline-constrained tasks in cloud computing. Cluster Computer. 2016; 19:679-98. Available from: Crossref.
  • Zhuo T, Ling Q, Zhenzhen C, Kenli L, Samee UK, Keqin L. An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment. Journal of Grid Computing.2016; 14:55-74. Available from: Crossref.
  • Seyedmehdi H, Farshad K, Rashidaldin S. SEATS: smart energy-aware task scheduling in real-time cloud computing.Journal of Supercomputer. 2015; 71:45-66. Available from: Crossref.
  • Sanjaya KP, Prasanta KJ. Normalization-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment.Information Systems Frontiers. 2016; p. 1-27.
  • Bei G, Jingzheng W, Yongji W, Samee UK. CIVSched: A Communication-Aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-Located VMs.IEEE Transactions on Cloud Computing. 2014; 2(3):32032. Available from: Crossref.
  • Dzmitry K, Johnatan EP, Andrei T, Pascal B, Samee UK, Albert YZ. CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing. Journal of Grid Computing. 2016; 14:23-39. Available from: Crossref.
  • Rajkumar R, Thamarai SS, Kannan G. Dynamic Load Balancer Algorithm for the Computational Grid Environment.Communications in Computer and Information Science.2010; 101:223-7. Available from: Crossref.
  • Rajkumar R. Topology and Load Aware – Grid Scheduler for the Computational Grid Environment. India: Proceedings of IEEE International Conference on Communication Control and Computing Technologies. 2010; p. 431-36.
  • Zhiping P, Delong C, Jinglong Z, Qirui L, Bo X, Weiwei L.Random task scheduling scheme based on reinforcement learning in cloud computing. Cluster Computing. 2015; 18:1595-607. Available from: Crossref.
  • Chun-Wei T, Wei-Cheng H, Meng-Hsiu C, Ming-Chao C, Chu-Sing Y. A Hyper-Heuristic Scheduling Algorithm for Cloud. IEEE Transactions on Cloud Computing. 2014; 2(2):236-50. Available from: Crossref.
  • Keng-Mao C, Pang-Wei T, Chun-Wei T, Chu-Sing Y. A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Computer and Application.2015; 26:1297-309. Available from: Crossref.
  • Arash GD, Yalda A. HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Cluster Computer.2014; 17:129-37. Available from: Crossref.
  • Mohammad S, Saeed J, Saeid A, Nicola C. FUGE: A joint meta-heuristic approach to cloud task scheduling algorithm using fuzzy theory and a genetic method. Cluster Computer. 2015; 18:829-44. Available from: Crossref.
  • Fan Z, Junwei C, Keqin L, Samee UK, Kai H. Multi-objective scheduling of many tasks in cloud platforms. Future Generation Computer Systems. 2014; 37:309-20. Available from: Crossref.
  • Jinglian W, Bin G, Hong L, Shaohui L. Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling. Applied Intelligence. 2015; 43:662-75. Available from: Crossref.
  • Ellendula M, Thirumalaisamy R. Efficient Scheduling Algorithm for Cloud. Procedia Computer Science. 2015; 50:35356. Available from: Crossref.
  • Yiming H, Anthony TC. Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems. International Journal of Parallel Programming. 2016; p. 1-17.
  • Haohao Z, Su D, Hongbin H. Stability property of clouds and cooperative scheduling policies on multiple types of resources in cloud computing. Journal of Supercomputing.2016; 72:2417-36. Available from: Crossref.
  • Rajinder S, Sandeep KS. Scheduling of big data applications on distributed cloud based on QoS parameters. Cluster Computer. 2015; 18:817-28. Crossref.
  • Esmail A, Azadeh A, Mostafa D, Michel GK, Mohsen S, Sayed VA. Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments. Cluster Computer. 2016; 19:567-83. Available from: Crossref.
  • Andrei T, Luz L, Uwe S, Pascal B, Johnatan EP, Sergio N, Alexander YD. Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service. Journal of Grid Computing. 2016; 14:5-22. Available from: Crossref.
  • Tamal A, Amit KD, Md AR, Ahmad A, Majed A, Mohammad MH. Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing. Mobile Networks and Applications. 2016; 21:482-93. Available from: Crossref.
  • Rajkumar R, Mala T. SLAOCMS: A Layered Architecture of SLA Oriented Cloud Management System for Achieving Agreement during Resource Failure. Advances in Intelligent Systems and Computing. 2014; 236:801-09. Available from: Crossref.
  • Navendu J, Ishai M, Joseph SN, Jonathan Y. A Truthful Mechanism for Value-Based Scheduling in Cloud Computing.Theory of Computing Systems. 2014; 54:388-406. Available from: Crossref.
  • Wei W, Guosun Z. Bayesian Cognitive Model in Scheduling Algorithm for Data Intensive Computing. Journal of Grid Computing. 2012; 10:173-84. Available from: Crossref.
  • Sanjaya KP, Indrajeet G, Prasanta KJ. Allocation-Aware Task Scheduling for Heterogeneous Multi-Cloud Systems.Procedia Computer Science. 2015; 50:176-84. Available from: Crossref.
  • Atul VL, Dharmendra KY. Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization. Procedia Computer Science. 2015; 48:107-13. Crossref.
  • Jena RK. Multi objective Task Scheduling in Cloud Environment Using Nested PSO Framework. Procedia Computer Science. 2015; 57:1219-27. Available from: Crossref.
  • Fahimeh R, Jie L, Javid T, Farookh KH. Evolutionary algorithmbased multi-objective task scheduling optimization model in cloud environments. World Wide Web. 2015; 18:1737-57. Available from: Crossref
  • Rodrigo NC, Rajiv R, Anton B, C’esar AFDR, Rajkumar B. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software Practice and Experience. 2011; 41:23-50. Available from: Crossref.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.