Resource scheduling in cloud environmet: a survey

Mangla, N.; Singh, M.; Rana, S. K.

doi:10.12913/22998624/62746

Artykuł - szczegóły

Tytuł artykułu

Resource scheduling in cloud environmet: a survey

Autorzy

Mangla N. , Singh M. , Rana S. K.

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12913/22998624/62746

Warianty tytułu

Języki publikacji

Abstrakty

Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

Słowa kluczowe

resource scheduling energy conservation cloud computing virtualization service level agreement

Wydawca

Lublin University of Technology
Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin

Czasopismo

Advances in Science and Technology. Research Journal

Rocznik

2016

Tom

Vol. 10, nr 30

Strony

38--50

Opis fizyczny

Bibliogr. 93 poz., fig., tab.

Twórcy

autor

Mangla N.

erneerajynr@gmail.com

Computer Engineering Department, Engineering College, M.M. University Mullana 133207 Ambala, India

autor

Singh M.

Computer Engineering Department, Engineering College, M.M. University Mullana 133207 Ambala, India

autor

Rana S. K.

Computer Engineering Department, Engineering College, M.M. University Mullana 133207 Ambala, India

Bibliografia

1. Demchenko, Y., Van der Ham, J., Yakovenko, V., De Laat, C., Ghijsen, M., & Cristea, M. (2011, May). On-demand provisioning of cloud and grid based infrastructure services for collaborative projects and groups. In Collaboration Technologies and Systems (CTS), 2011 International Conference on (pp. 134-142). IEEE.
2. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
3. Shawish, A., & Salama, M. (2014). Cloud computing: paradigms and technologies. In Inter-cooperative Collective Intelligence: Techniques and Applications (pp. 39-67). Springer Berlin Heidelberg.
4. Whaiduzzaman, M., Gani, A., Anuar, N. B., Shiraz, M., Haque, M. N., & Haque, I. T. (2014). Cloud service selection using multicriteria decision analysis. The Scientific World Journal, 2014.
5. Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008, November). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE'08 (pp. 1-10). IEEE.
6. Huang, J., Liu, Y., & Duan, Q. (2012, December). Service provisioning in virtualization-based Cloud computing: Modeling and optimization. In Global Communications Conference (GLOBECOM), 2012 IEEE (pp. 1710-1715). IEEE.
7. Mohamadi, A., & Barani, S. (2015, September). A review on approaches in service level agreement in cloud computing environment. In Fuzzy and Intelligent Systems (CFIS), 2015 4th Iranian Joint Congress on (pp. 1-4). IEEE.
8. Okada, T. K., De La Fuente Vigliotti, A., Macedo Batista, D., & Goldman vel Lejbman, A. (2015, May). Consolidation of VMs to improve energy efficiency in cloud computing environments. In Computer Networks and Distributed Systems (SBRC), 2015 XXXIII Brazilian Symposium on (pp. 150-158). IEEE.
9. Hou, R., Jiang, T., Zhang, L., Qi, P., Dong, J., Wang, H., & Zhang, S. (2013, February). Cost effective data center servers. In High Performance Computer Architecture (HPCA2013), 2013 IEEE 19th International Symposium on (pp. 179-187). IEEE.
10. Gupta, A., & Kalé, L. V. (2013, May). Towards efficient mapping, scheduling, and execution of HPC applications on platforms in cloud. In Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2013 IEEE 27th International (pp. 2294-2297). IEEE.
11. Han, R., Ghanem, M. M., Guo, L., Guo, Y., & Osmond, M. (2014). Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Future Generation Computer Systems, 32, 82-98.
12. Weingärtner, R., Bräscher, G. B., & Westphall, C. B. (2015). Cloud resource management: A survey on forecasting and profiling models. Journal of Network and Computer Applications, 47, 99-106.
13. Zhang, X., Liu, C., Nepal, S., Pandey, S., & Chen, J. (2013). A privacy leakage upper bound constraint-based approach for cost-effective privacy preserving of intermediate data sets in cloud. Parallel and Distributed Systems, IEEE Transactions on, 24(6), 1192-1202.
14. Verma, A., & Kaushal, S. (2012, April). Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In IJCA Proceedings on international conference on recent advances and future trends in information technology (iRAFIT 2012).
15. Calheiros, R. N., & Buyya, R. (2012). Cost-effective provisioning and scheduling of deadline-constrained applications in hybrid clouds. In Web Information Systems Engineering-WISE 2012 (pp. 171-184). Springer Berlin Heidelberg.
16. Rodriguez, M. A., & Buyya, R. (2014). Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. Cloud Computing, IEEE Transactions on, 2(2), 222-235.
17. Mei, J., Li, K., Ouyang, A., & Li, K. (2015). A profit maximization scheme with guaranteed quality of service in cloud computing. Computers, IEEE Transactions on, 64(11), 3064-3078.
18. Sandholm, T., Ward, J., Balestrieri, F., & Huberman, B. A. (2015). QoS-Based Pricing and Scheduling of Batch Jobs in OpenStack Clouds. arXiv preprint arXiv:1504.07283.
19. Guo, L., Zhao, S., Shen, S., & Jiang, C. (2012). Task scheduling optimization in cloud computing based on heuristic algorithm. Journal of Networks, 7(3), 547-553.
20. Shi, W., & Hong, B. (2011, December). Towards profitable virtual machine placement in the data center. In Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on (pp. 138-145). IEEE.
21. Wang, L., Shen, J., Luo, J., & Dong, F. (2013, October). An improved genetic algorithm for cost-effective data-intensive service composition. In Semantics, Knowledge and Grids (SKG), 2013 Ninth International Conference on (pp. 105-112). IEEE.
22. Li, C., & Li, L. (2013). Efficient resource allocation for optimizing objectives of cloud users, IaaS provider and SaaS provider in cloud environment. The Journal of Supercomputing, 65(2), 866-885.
23. Li, K., Liu, C., Li, K., & Zomaya, A. (2015). A Framework of Price Bidding Configurations for Resource Usage in Cloud Computing.
24. Liu, Z., Wang, S., Sun, Q., Zou, H., & Yang, F. (2013). Cost-aware cloud service request scheduling for SaaS providers. The Computer Journal, bxt009.
25. Palanisamy, B., Singh, A., & Liu, L. (2015). Cost-effective resource provisioning for mapreduce in a cloud. Parallel and Distributed Systems, IEEE Transactions on, 26(5), 1265-1279.
26. Zhou, A., Wang, S., Sun, Q., Zou, H., & Yang, F. (2013, December). Dynamic Virtual Resource Renting Method for Maximizing the Profits of a Cloud Service Provider in a Dynamic Pricing Model. In Parallel and Distributed Systems (ICPADS), 2013 International Conference on (pp. 118-125). IEEE.
27. Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010, April). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced information networking and applications (AINA), 2010 24th IEEE international conference on (pp. 400-407). IEEE.
28. Zhu, Q., & Agrawal, G. (2010, June). Resource provisioning with budget constraints for adaptive applications in cloud environments. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (pp. 304-307). ACM.
29. Chen, J., Wang, C., Zhou, B. B., Sun, L., Lee, Y. C., & Zomaya, A. Y. (2011, June). Tradeoffs between profit and customer satisfaction for service provisioning in the cloud. In Proceedings of the 20th international symposium on High performance distributed computing (pp. 229-238). ACM.
30. Yuan, D., Cui, L., Liu, X., Fu, E., & Yang, Y. (2016). A Cost-Effective Strategy for Storing Scientific Datasets with Multiple Service Providers in the Cloud. arXiv preprint arXiv:1601.07028.
31. Imai, S., Patterson, S., & Varela, C. A. (2015, May). Cost-Efficient High-Performance Internet-Scale Data Analytics over Multi-Cloud Environments. In Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on (pp. 793-796). IEEE.
32. Goiri, Í., Berral, J. L., Fitó, J. O., Julià, F., Nou, R., Guitart, J., ... & Torres, J. (2012). Energy-efficient and multifaceted resource management for profit-driven virtualized data centers. Future Generation Computer Systems, 28(5), 718-731.
33. Li, J., Su, S., Cheng, X., Song, M., Ma, L., & Wang, J. (2015). Cost-efficient coordinated scheduling for leasing cloud resources on hybrid workloads. Parallel Computing, 44, 1-17.
34. Malawski, M., Juve, G., Deelman, E., & Nabrzyski, J. (2015). Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Generation Computer Systems, 48, 1-18.
35. Videv, S., & Haas, H. (2011, June). Energy-efficient scheduling and bandwidth-energy efficiency trade-off with low load. In Communications (ICC), 2011 IEEE International Conference on (pp. 1-5). IEEE.
36. Singh, S., & Chana, I. (2014). Energy based efficient resource scheduling: a step towards green computing. Int J Energy Inf Commun, 5(2), 35-52.
37. Liu, D., & Han, N. (2014). An Energy-efficient Task Scheduler in Virtualized Cloud Platforms. International Journal of Grid and Distributed Computing,7(3), 123-134.
38. Beloglazov, A., & Buyya, R. (2010, May). Energy efficient resource management in virtualized cloud data centers. In Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (pp. 826-831). IEEE Computer Society.
39. Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.
40. Sharifi M, Salimi H, Najafzadeh M. (2012). Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques. The Journal of Supercomputing, pp. 46-66, Springer 2012.
41. Al-Qawasmeh, A. M., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2015). Power and thermal-aware workload allocation in heterogeneous data centers. Computers, IEEE Transactions on, 64(2), 477-491.
42. Quang-Hung, N., Thoai, N., & Son, N. T. (2014). EPOBF: energy efficient allocation of virtual machines in high performance computing cloud. In Transactions on Large-Scale Data-and Knowledge-Centered Systems XVI (pp. 71-86). Springer Berlin Heidelberg.
43. Quang-Hung, N., Le, D. K., Thoai, N., & Son, N. T. (2014). Heuristics for energy-aware vm allocation in hpc clouds. In Future Data and Security Engineering (pp. 248-261). Springer International Publishing.
44. Farahnakian, F., Liljeberg, P., & Plosila, J. (2014, February). Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In Parallel, Distributed and Network-Based Processing (PDP), 2014 22nd Euromicro International Conference on (pp. 500-507). IEEE.
45. Sampaio, A. M., & Barbosa, J. G. (2014). Towards high-available and energy-efficient virtual computing environments in the cloud. Future Generation Computer Systems, 40, 30-43.
46. Xiong, A. P., & Xu, C. X. (2014). Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Mathematical Problems in Engineering, 2014.
47. Dong, D., & Herbert, J. (2013, May). Energy efficient vm placement supported by data analytic service. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on (pp. 648-655). IEEE.
48. Arianyan, E., Taheri, H., & Sharifian, S. (2015). Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Computers & Electrical Engineering, 47, 222-240.
49. Feller, E., Rohr, C., Margery, D., & Morin, C. (2012, June). Energy management in IaaS clouds: a holistic approach. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on (pp. 204-212). IEEE.
50. Wang, S., Liu, Z., Zheng, Z., Sun, Q., & Yang, F. (2013, December). Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In Parallel and Distributed Systems (ICPADS), 2013 International Conference on (pp. 102-109). IEEE.
51. Uddin, M., Darabidarabkhani, Y., Shah, A., & Memon, J. (2015). Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review. Renewable and Sustainable Energy Reviews, 51, 1553-1563.
52. Dabbagh, M., Hamdaoui, B., Guizani, M., & Rayes, A. (2015). Energy-efficient resource allocation and provisioning framework for cloud data centers. Network and Service Management, IEEE Transactions on, 12(3), 377-391.
53. Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., & Wu, J. (2015). Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. Journal of Systems and Software, 99, 20-35.
54. Zhang, Z., Hsu, C. C., & Chang, M. (2015, June). Cool Cloud: A Practical Dynamic Virtual Machine Placement Framework for Energy Aware Data Centers. In Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on (pp. 758-765). IEEE.
55. Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), 268-280.
56. Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13), 1397-1420.
57. Barroso, L. A., & Hölzle, U. (2007). The case for energy-proportional computing. Computer, (12), 33-37.
58. Lu, K., Yahyapour, R., Wieder, P., Yaqub, E., Abdullah, M., Schloer, B., & Kotsokalis, C. (2016). Fault-tolerant Service Level Agreement lifecycle management in clouds using actor system. Future Generation Computer Systems, 54, 247-259.
59. Moon, H. J., Chi, Y., & Hacigumus, H. (2010, July). SLA-aware profit optimization in cloud services via resource scheduling. In Services (SERVICES-1), 2010 6th World Congress on (pp. 152-153). IEEE.
60. Buyya, R., Garg, S. K., & Calheiros, R. N. (2011, December). SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions. In Cloud and Service Computing (CSC), 2011 International Conference on (pp. 1-10). IEEE.
61. Nayak, D., Martha, V. S., Threm, D., Ramaswamy, S., Prince, S., & Fatimberger, G. (2015, July). Adaptive scheduling in the cloud—SLA for Hadoop job scheduling. In Science and Information Conference (SAI), 2015 (pp. 832-837). IEEE.
62. García, A. G., Espert, I. B., & García, V. H. (2014). SLA-driven dynamic cloud resource management. Future Generation Computer Systems, 31, 1-11.
63. Wu, L., Garg, S. K., & Buyya, R. (2011, May). SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments. In Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on (pp. 195-204). IEEE.
64. Yaqub, E., Yahyapour, R., Wieder, P., Jehangiri, A. I., Lu, K., & Kotsokalis, C. (2014, December). Metaheuristics-based planning and optimization for sla-aware resource management in paas clouds. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (pp. 288-297). IEEE Computer Society.
65. Rajabi, A., Faragardi, H. R., & Yazdani, N. (2013, October). Communication-aware and energy-efficient resource provisioning for real-time cloud services. In Computer Architecture and Digital Systems (CADS), 2013 17th CSI International Symposium on (pp. 125-129). IEEE.
66. Bi, J., Yuan, H., Tie, M., & Tan, W. (2015). SLA-based optimisation of virtualised resource for multi-tier web applications in cloud data centres. Enterprise Information Systems, 9(7), 743-767.
67. Farokhi, S., Jrad, F., Brandic, I., & Streit, A. (2014). HS4MC-Hierarchical SLA-based Service Selection for Multi-Cloud Environments. In CLOSER (pp. 722-734).
68. Serrano D, Bouchenak S, Kouki Y, de Oliveira Jr FA, Ledoux T, Lejeune J, Sopena J, Arantes L, Sens P. (2016). SLA guarantees for cloud services”, In Future Generation Computer Systems. In Future Generation Computer Systems, pp. 233-246.
69. Morshedlou, H., & Meybodi, M. R. (2014). Decreasing impact of sla violations: a proactive resource allocation approach for cloud computing environments. Cloud Computing, IEEE Transactions on, 2(2), 156-167.
70. Lu, K., Yahyapour, R., Wieder, P., Kotsokalis, C., Yaqub, E., & Jehangiri, A. I. (2013, June). Qos-aware vm placement in multi-domain service level agreements scenarios. In 2013 IEEE Sixth International Conference on Cloud Computing (pp. 661-668). IEEE.
71. Selvi, S. T., SathiaBham, P. R., Architha, S., Kaarunya, T., & Vinothini, K. (2010). Scheduling In Virtualized Grid Environment Using Hybrid Approach. International Journal of Grid Computing & Applications (IJGCA) Vol, 1.
72. Wu, S., Chen, H., Di, S., Zhou, B., Xie, Z., Jin, H., & Shi, X. (2015). Synchronization-Aware Scheduling for Virtual Clusters in Cloud. Parallel and Distributed Systems, IEEE Transactions on, 26(10), 2890-2902.
73. Wang, H., Isci, C., Subramanian, L., Choi, J., Qian, D., & Mutlu, O. (2015, March). A-DRM: Architecture-aware distributed resource management of virtualized clusters. In Proceedings of the 11th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (pp. 93-106). ACM.
74. Garbacki, P., & Naik, V. K. (2007, May). Efficient resource virtualization and sharing strategies for heterogeneous grid environments. In Integrated Network Management, 2007. IM'07. 10th IFIP/IEEE International Symposium on (pp. 40-49). IEEE.
75. Nguyen, Q. T., Quang-Hung, N., Tuong, N. H., Tran, V. H., & Thoai, N. (2013, January). Virtual machine allocation in cloud computing for minimizing total execution time on each machine. In Computing, Management and Telecommunications (ComManTel), 2013 International Conference on (pp. 241-245). IEEE.
76. Sahal, R., & Omara, F. A. (2014, December). Effective virtual machine configuration for cloud environment. In Informatics and Systems (INFOS), 2014 9th International Conference on (pp. PDC-15). IEEE.
77. Wang, X., Liu, X., Fan, L., & Jia, X. (2013). A decentralized virtual machine migration approach of data centers for cloud computing. Mathematical Problems in Engineering, 2013.
78. Aral, A., & Ovatman, T. (2014). Improving Resource Utilization in Cloud Environments using Application Placement Heuristics. In CLOSER (pp. 527-534).
79. Li, K., Wu, J., & Blaisse, A. (2013, November). Elasticity-aware virtual machine placement for cloud datacenters. In Cloud Networking (CloudNet), 2013 IEEE 2nd International Conference on (pp. 99-107). IEEE.
80. Li, W., Tordsson, J., & Elmroth, E. (2011). Virtual machine placement for predictable and time-constrained peak loads. In Economics of Grids, Clouds, Systems, and Services (pp. 120-134). Springer Berlin Heidelberg.
81. Park, J. G., Kim, J. M., Choi, H., & Woo, Y. C. (2009, February). Virtual machine migration in self-managing virtualized server environments. In Advanced Communication Technology, 2009. ICACT 2009. 11th International Conference on (Vol. 3, pp. 2077-2083). IEEE.
82. Ferreto, T. C., Netto, M. A., Calheiros, R. N., & De Rose, C. A. (2011). Server consolidation with migration control for virtualized data centers. Future Generation Computer Systems, 27(8), 1027-1034.
83. Tordsson, J., Montero, R. S., Moreno-Vozmediano, R., & Llorente, I. M. (2012). Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems, 28(2), 358-367.
84. Baruchi, A., Toshimi Midorikawa, E., & Netto, M. A. (2014, November). Improving Virtual Machine live migration via application-level workload analysis. In Network and Service Management (CNSM), 2014 10th International Conference on (pp. 163-168). IEEE.
85. Hieu, N. T., Di Francesco, M., & Yla-Jaaski, A. (2015, June). Virtual Machine Consolidation with Usage Prediction for Energy-Efficient Cloud Data Centers. In Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on (pp. 750-757). IEEE.
86. Ezugwu, A. E., Buhari, S. M., & Junaidu, S. B. (2013). Virtual machine allocation in cloud computing environment. International Journal of Cloud Applications and Computing (IJCAC), 3(2), 47-60.
87. Z. Xiao, W. Song and Q. Chen (June 2013). Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment. In IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107-1117.
88. M. Seddigh, H. Taheri and S. Sharifian (2015). Dynamic prediction scheduling for virtual machine placement via ant colony optimization. Signal Processing and Intelligent Systems Conference (SPIS), Tehran, Iran, pp. 104-108.
89. Xu, X., Hu, H., Hu, N., & Ying, W. (2012). Cloud task and virtual machine allocation strategy in cloud computing environment. In Network Computing and Information Security (pp. 113-120). Springer Berlin Heidelberg.
90. Quang-Hung, N., Nien, P. D., Nam, N. H., Tuong, N. H., & Thoai, N. (2013). A genetic algorithm for power-aware virtual machine allocation in private cloud. In Information and Communication Technology (pp. 183-191). Springer Berlin Heidelberg.
91. Saraswathi, A. T., Kalaashri, Y. R. A., & Padmavathi, S. (2015). Dynamic resource allocation scheme in cloud computing. Procedia Computer Science, 47, 30-36.
92. Coutinho, R. D. C., Drummond, L. M., Frota, Y., & de Oliveira, D. (2015). Optimizing virtual machine allocation for parallel scientific workflows in federated clouds. Future Generation Computer Systems, 46, 51-68.
93. de Oliveira, D., Viana, V., Ogasawara, E., Ocaña, K., & Mattoso, M. (2013, June). Dimensioning the virtual cluster for parallel scientific workflows in clouds. In Proceedings of the 4th ACM workshop on Scientific cloud computing (pp. 5-12). ACM.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-2b4fea1d-8afa-4a00-8f6b-0790e9e39959