Enhancing Reconfiguration in Two-Phase Virtual Machine Placement for Cloud Datacenters Using Tabu Search Algorithm

Abdurrahman Aminu Abdulkadir, Faruku Umar Ambursa

Abstract


Cloud computing enables users to have access to resources on demand from cloud datacenter. One of the major challenges in cloud datacenters is virtual machine placement (VMP) which is selecting appropriate physical machines for each virtual machine request. VMP can be online, offline or two-phase which combines both online and offline. For solving the VMP problem, two-phase optimization approach is used recently. The first phase, also called the incremental VMP (iVMP) phase, involves dynamic handling of arriving VM requests from users, where VMs could be created, modified or destroyed at runtime. The second phase, referred to as the VMP reconfiguration (VMPr) phase, deals with improving the quality of solutions obtained in the iVMP phase. This work aims to improve the VMPr phase of the two-phase VMP. Previous works used Memetic algorithm in the VMPr reconfiguration phase. However, a population’s lack of diversity causes the Memetic Algorithm to allocate repeated reproductive, mutation and refinement trials to the same individuals thus wasting precious CPU resources. Balancing population diversity is a critical issue. Therefore, another metaheuristic algorithm (Tabu Search) is adopted to solve the problem. The strength of the TS resides in its capacity to escape the trap of local optimality, and therefore gives good solutions of hard combinatorial optimization problems. Experiment has been conducted taking into account 400 different scenarios, experimental evaluation was performed against the benchmark research. The new method gives better aggregate scores of power consumption, economic revenue, and resource usage and reconfiguration time thus achieving better results (i.e Minimum cost).

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Anass, E ., Abdellah E., Mohammad C., Mohamed R.(2016). Tabu Search and Memetic Algorithms for a Real Scheduling and Routing Problem, Logistics Research (2017) 10:7 DO I 10.23773/2017_7.

Baloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy aware resource allocation heuristics for efficient management of datacenters for cloud Computing. Future Gener. Comp. Syst., 755-768.

Calcavecchia, N. M., Biran, O., Hadad, E., &Moatti, Y. (2012). VM placement strategies for cloud scenarios in cloud computing (CLOUD), 2012 IEEE 5th International Conference on, IEEE, 2012, 852–859.

Chen, L., Li, X., Liu, Y., & Zhang, Y. (2023). A Dynamic Load Balancing Algorithm Based on Workload Characteristics for Cloud Computing. Journal of Cloud Computing, 12(1), 1-18.

Chen, S., Liu, J., Li, X., & Zhang, H. (2022). Reinforcement Learning-Based Virtual Machine Placement for Cloud Computing. IEEE Transactions on Parallel and Distributed Systems, 33(6), 1522-1536.

Chen, X., Wang, L., Li, X., & Li, J. (2023). Edge-Assisted Virtual Machine Placement for Latency-Sensitive Applications in Cloud Computing. Journal of Network and Computer Applications, 188, 103077.

Chen, Y., Shi, Y., Gong, L., &Cai, Y. (2018). Performance and Reliability Challenges in Cloud Computing. In 2018 IEEE International Conference on Services Computing (SCC) (pp. 294-297). IEEE.

Gaggero, M., & Caviglione, L. (2019). Holistic Placement of Virtual Machines in Cloud Data Centers. In 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 327-333). IEEE.

Goudarzi, H., Pedram, M. S., & Pedram, M. (2012). Dynamic consolidation of virtual machines in cloud data centers using online and offline algorithms. IEEE Transactions on Cloud Computing, 1(2), 145-158.

Gupta, A., Milojicic, D., &Kal'e, L. V. (2012). Optimizing VM placement for hpc in cloud in proceedings of the 2012 workshop on Cloud Services, federation and 8th open cirrus summit, ACM, (pp. 1-6).

Grandl, R., Ananthanarayanan, G., Kandula, S., & Rao, S. (2014). Scaling Datacenter Networks to 100Gbps and Beyond with Zero Packet Loss. In Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (pp. 361-374).

Han Z, Tan H, Wang R, Chen G, Li Y, Lau FCM. Energy-efficient dynamic virtual machine management in data centers. IEEE/ACM Trans Network 2019;27(1): 344–60.

Huang, X., Sun, Y., Luo, Y., & Wang, S. (2021). Genetic Algorithm-Based Virtual Machine Placement Optimization in Cloud Computing. Future Generation Computer Systems, 116, 1-14.

Huang, Q., Li, H., Zhang, J., & Wu, Q. (2022). Scalable Storage Architecture for Cloud Systems. IEEE Transactions on Parallel and Distributed Systems, 34(2), 564-577.

Hu, Z., Gu, J., & Sun, J. (2010). A genetic algorithm based load balancing strategy in cloud computing. In Proceedings of the 2010 International Conference on Computational Intelligence and Software Engineering (pp. 1-4). IEEE.

Jula, A., Pop, F., & Prodan, R. (2014). A Survey on Resource Allocation in Cloud Computing: Taxonomy, Challenges, and Solutions. Journal of Grid Computing, 12(2), 335-377.

Kumbhare, A., Malani, I., &Anand, S. (2018). Vendor Lock-in Issues in Cloud Computing. In Proceedings of the 2018 2nd International Conference on Communication System, Computing and IT Applications (pp. 1-5). IEEE.

Lago, P., Garg, S. K., & Buyya, R. (2011). Job Scheduling and Resource Provisioning for Cloud Computing. Future Generation Computer Systems, 28(5), 684-692.

Li, J., Zhao, L., Li, J., Wang, Y., & Wang, S. (2013). Resource utilization optimization in cloud computing based on cooperative game. In 2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC) (pp. 1259-1264). IEEE.

Li, J., Jiang, X., Guo, M., & Zhang, T. (2021). A Privacy-Preserving Machine Learning Framework for Cloud Environments. Future Generation Computer Systems, 128, 52-64.

Li, S., Xu, C., Li, S., & Zhang, L. (2023). Energy-Efficient Virtual Machine Placement in Cloud Computing. IEEE Transactions on Sustainable Computing, 8(1), 1-14.

Liu, Y., Cai, Y., & Zhang, C. (2017). Ant Colony Optimization for Minimizing the Number of Active Physical Servers in Cloud Data Centers. In 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (pp. 448-453). IEEE.

Liu, Z., Chen, S., Xu, L., & Huang, S. (2022). Virtual Machine Placement in Hybrid Cloud Environments: Challenges and Opportunities. ACM Computing Surveys, 55(2), 1-27.

Liu, Z., Chen, S., Xu, L., & Huang, S. (2022). Integrating Edge Computing with Cloud Platforms: A Comprehensive Survey. ACM Computing Surveys, 55(1), 1-37.

Madni, S. H., & Lattif, A. A. (2016). Resource scheduling and allocation techniques in cloud computing: A comprehensive review. International Journal of Computer Applications, 146(5), 39-45.

Mark, L., Lee, C., Yang, C., & Zhang, J. (2011). Economic revenue optimization in cloud computing: A revenue-sharing model. In Proceedings of the 2011 IEEE International Conference on Cloud Computing (CLOUD) (pp. 196-203). IEEE.

Mary, S. (2014). Quality of Service in Cloud Computing: A Systematic Review. International Journal of Computer Applications, 101(1), 17-23.

Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016). An Overview of virtual machine placement schemes In Cloud Computing. Journal of Network and Computer Applications.

Mell, P., & Grance, T.(2011). The NIST Definition of Cloud Computing. National Standards and Technology, Special Publication 800 – 145.

Meng X, Pappas V, Zhang L. Improving the scalability of data center networks with traffic-aware virtual machine placement. Proceedings of the IEEE INFOCOM. 2010 Proceedings IEEE INFOCOM; 2010. p. 1–9.

Marinos, A., &Malek, M. (2016). Security, Privacy and Trust in Cloud Systems: A Literature Review. Future Generation Computer Systems, 56, 186-207.

Natalio, Krasnogor & Aragón, Alberto & Pacheco, Joaquín. (2006). Memetic Algorithms. 10.1007/0-387-33416-5_11.

Ortigoza, J., Pires, F., &Baran, B. (2016). Workload generation for virtual machine placement in cloud computing environments. XLII Latin American Computing Conference, CLEI, 1-9.

Pires, F. L., &Baran, B. (2014). Virtual machine placement literature review (data). Polytechnic School, National University of Asuncion on, Tech. Rep.

Pires, F. L., &Baran, B. (2015). A Virtual Machine Placement Taxonomy. International Sympodium on Cluster, Cloud and Grid Computing, 15th IEEE/ACM, 159-168.

Pires, F. L., Baran, B., Benitez, L., Zalinmben, S., &Amarilla, A. (2017). Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty. Future Generation Computer Systems.

Rahman, A. A., Lashkarara, F., Baida, R., &Yassine, A. (2020). Compliance Challenges in Cloud Computing Environments: A Systematic Literature Review. IEEE Access, 8, 150556-150573.

RolikO, ZharikovE, KovalA, TelenykS. Dynamic management of data center resources using reinforcement learning. Proceedings of the 14th international conference on advanced t rends in radioelecrtronics, telecommunications and computer engineering (TCSET). 2018 14th International Conference on Advanced Trends in Radio electronics, Telecommunications and Computer Engineering (TCSET); 2018. p. 237–44. Sharma NK, Reddy GRM. Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput 2019; 12(1):158–71.

Smith, A., Johnson, B., & Brown, C. (2021). Hybrid Cloud Models: Integrating Edge Computing for Real-Time Applications. International Journal of Information Management, 61, 102263.

Speitkamp, B., &Bichler, M. (2010). A mathematical programming approach for server. IEEE Trans. Serv. Comput, 266–278.

Uhlig, V. (2006). Intel Virtualization Technology. Intel Technology Journal, 10(3), 167-178.

Wang, L., von Laszewski, G., Younge, A. et al. New Gener. Comput. (2010) 28: 137.

Wang, H., Li, H., Lian, Q., & Lin, C. (2021). Locality-Aware Virtual Machine Placement Algorithm for Reducing Network Latency in Cloud Computing. Journal of Supercomputing, 77(9), 8396-8416.

Wang, Q., Li, S., & Xu, C. (2023). Serverless Computing in Cloud Environments: Architecture, Challenges, and Future Directions. Future Internet, 15(1), 1-20.

Wu, H., Zhang, S., Liu, M., & Li, K. (2022). Energy-Aware Virtual Machine Placement in Cloud Data Centers. Journal of Parallel and Distributed Computing, 162, 80-91.

Xu, Y., Tian, Y., & Buyya, R. (2017). A survey on load balancing algorithms for virtual machines placement in cloud computing environments. Concurrency and Computation: Practice and Experience, 29(12), e3959.

Yang, M., Wang, W., J., & Zhang, H. (2022). Multi-Cloud Resource Allocation Based on Machine Learning for Performance Optimization. Journal of Network and Computer Applications, 194, 103173.

Zhang, J., Li, M., Zhou, L., & Zhou, Y. (2022). Deep Learning-Based Virtual Machine Placement for Performance Optimization in Cloud Computing. Future Generation Computer Systems, 128, 538-549.

Zhang, X., Zhang, Y., Chen, X., Liu, K., Huang, G., & Zhan, J. (2013). A relationship-based VM placement framework of cloud environment.in proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference 124-133.

Zhang, Q., Wu, S., Li, X., & Lau, F. (2018). Online Auction-Based Resource Allocation for Social Welfare Maximization in Cloud Computing. IEEE Transactions on Services Computing, 11(1), 134-147.

Zhang, Y., Xiong, S., Zhou, Y., & Li, X. (2022). A Secure Data Sharing Scheme with Attribute-Based Encryption in Cloud Computing. Future Generation Computer Systems, 125, 551-561.

Zhao H, Wang J, Liu F, Wang Q, Zhang W, Zheng Q. Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans Parallel Distributed Syst.2018;29(6):1385–400.


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