A Hybrid Optimized Resource Allocation Model for Multi-Cloud Environment Using Bat and Particle Swarm Optimization Algorithms
In cloud computing, scheduling and resource allocation are the major factors that define the overall quality of services. An efficient resource allocation module is required in cloud computing since resource allocation in a single cloud environment is a complex process. Whereas resource allocation in a multi-cloud environment further increases the complexity of allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resource availability status and resource capability before allocating to the requested tasks. So, in this research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resource considering the resource status, distance, bandwidth, and task requirements. Proposed model performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resources in 47 s, with a minimum energy consumption of 200 kWh. Compared to conventional approaches, the performance of the proposed model is much better in terms of deadline missed tasks, resource requirement, energy consumption, and allocation time.
KeywordsMulti-cloud computing, Resource allocation, Hybrid optimization, BAT Algorithm, Particle Swarm Optimization, Quality of services (QoS),
References1. J.S. Manoharan, A novel user layer cloud security model based on chaotic Arnold transformation using fingerprint biometric traits, Journal of Innovative Image Processing (JIIP), 3(1): 36–51, 2021, doi: 10.36548/JIIP.2021.1.004.
2. S. Shakya, An efficient security framework for data migration in a cloud computing environment, Journal of Artificial Intelligence, 1(1): 45–53, 2019, doi: 10.36548/jaicn.2019.1.006.
3. J. Son, R. Buyya, Priority-aware VM allocation and network bandwidth provisioning in software-defined networking (SDN)-enabled clouds, IEEE Transactions on Sustainable Computing, 4(1): 17–28, 2019, doi: 10.1109/TSUSC.2018.2842074.
4. M.H. Siddiqi, M. Alruwaili, A. Ali, S.F. Haider, F. Ali, M. Iqbal, Dynamic priority-based efficient resource allocation and computing framework for vehicular multimedia cloud computing, IEEE Access, 8: 81080–81089, 2020, doi: 10.1109/ACCESS.2020.2990915.
5. L. Wei, C.H. Foh, B. He, J. Cai, Towards efficient resource allocation for heterogeneous workloads in IaaS clouds, IEEE Transactions on Cloud Computing, 6(1): 264–275, 2018, doi: 10.1109/TCC.2015.2481400.
6. S. Tang, B.-S. Lee, B. He, Fair resource allocation for data-intensive computing in the cloud, IEEE Transactions on Services Computing, 11(1): 20–33, 2018, doi: 10.1109/TSC.2016.2531698.
7. P. Poullie, T. Bocek, B. Stiller, A survey of the state-of-the-art in fair multi-resource allocations for data centers, IEEE Transactions on Network and Service Management, 15(1): 169–183, 2018, doi: 10.1109/TNSM.2017.2743066.
8. S. Gong, B. Yin, Z. Zheng, K.-Y. Cai, Adaptive multivariable control for multiple resource allocation of service-based systems in cloud computing, IEEE Access, 7: 13817–13831, 2019, doi: 10.1109/ACCESS.2019.2894188.
9. L.-D. Chou, H.-F. Chen, F.-H. Tseng, H.-C. Chao, Y.-J. Chang, DPRA: Dynamic powersaving resource allocation for cloud data center using particle swarm optimization, IEEE Systems Journal, 12(2): 1554–1565, 2018, doi: 10.1109/JSYST.2016.2596299.
10. G. Peng, H. Wang, J. Dong, H. Zhang, Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment, IEEE Transactions on Services Computing, 11(2): 306–317, 2018, doi: 10.1109/TSC.2016.2518161.
11. X. Gao, R. Liu, A. Kaushik, Hierarchical multi-agent optimization for resource allocation in cloud computing, IEEE Transactions on Parallel and Distributed Systems, 32(3): 692–707, 2021, doi: 10.1109/TPDS.2020.3030920.
12. F.-H. Tseng, X. Wang, L.-D. Chou, H.-C. Chao, V.C.M. Leung, Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm, IEEE Systems Journal, 12(2): 1688–1699, 2018, doi: 10.1109/JSYST.2017.2722476.
13. K.M. Sim, Agent-based approaches for intelligent intercloud resource allocation, IEEE Transactions on Cloud Computing, 7(2): 442–455, 2019, doi: 10.1109/TCC.2016.2628375.
14. K. Chard, K. Bubendorfer, Co-operative resource allocation: building an open cloud market using shared infrastructure, IEEE Transactions on Cloud Computing, 7(1): 183–195, 2019, doi: 10.1109/TCC.2016.2594174.
15. A. Alsarhan, A. Itradat, A.Y. Al-Dubai, A.Y. Zomaya, G. Min, Adaptive resource allocation and provisioning in multi-service cloud environments, IEEE Transactions on Parallel and Distributed Systems, 29(1): 31–42, 2018, doi: 10.1109/TPDS.2017.2748578.
16. T. Thein, M.M. Myo, S. Parvin, A. Gawanmeh, Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers, Journal of King Saud University – Computer and Information Sciences, 32(10): 1127–1139, 2020, doi: 10.1016/j.jksuci.2018.11.005.
17. P. Haratian, F. Safi-Esfahani, L. Salimian, A. Nabiollahi, An adaptive and fuzzy resource management approach in cloud computing, IEEE Transactions on Cloud Computing, 7(4): 907–920, doi: 10.1109/TCC.2017.2735406.
18. S.G. Domanal, R.M.R. Guddeti, R. Buyya, A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment, IEEE Transactions on Services Computing, 13(1): 3–15, 2020, doi: 10.1109/TSC.2017.2679738.
19. S.K. Mishra et al., Energy-aware task allocation for multi-cloud networks, IEEE Access, 8: 178825–178834, 2020, doi: 10.1109/ACCESS.2020.3026875.
20. M. Farid, R. Latip, M. Hussin, N.A.W. Abdul Hamid, Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment, IEEE Access, 8: 24309–24322, 2020, doi: 10.1109/ACCESS.2020.2970475.
21. T. Shi, H. Ma, G. Chen, S. Hartmann, Location-aware and budget-constrained service deployment for composite applications in multi-cloud environment, IEEE Transactions on Parallel and Distributed Systems, 31(8): 1954–1969, 2020.
22. H. Li, H. Xu, C. Zhou, X. Lü, Z. Han, Joint optimization strategy of computation offloading and resource allocation in multi-access edge computing environment, IEEE Transactions on Vehicular Technology, 69(9): 10214–10226, 2020, doi: 10.1109/TVT.2020.3003898.
23. Madhusudhan H.S., Satish Kumar T., S.M.F.D. Syed Mustapha, P. Gupta, R.P. Tripathi, Hybrid approach for resource allocation in cloud infrastructure using random forest and genetic algorithm, Scientific Programming, 2021: Article ID 4924708, 10 pages, 2021, doi: 10.1155/2021/4924708.
24. K. Kottursamy, A review on finding efficient approach to detect customer emotion analysis using deep learning analysis, Journal of Trends in Computer Science and Smart Technology, 3(2): 95–113, 2021.
25. H.K. Andi, Analysis of serverless computing techniques in cloud software framework, Journal of IoT in Social, Mobile, Analytics, and Cloud, 3(3): 221–234, doi: 10.36548/jismac.2021.3.004.
26. Y.B. Hamdan, A. Sathesh, Construction of efficient smart voting machine with liveness detection module, Journal of Innovative Image Processing, 3: 255–268, 2021, doi: 10.36548/jiip.2021.3.007.
27. S. Senthilkumar, V. Mohan, S.P. Mangaiyarkarasi, M. Karthikeyan, Analysis of dinglediode PV model and optimized MPPT model for different environmental conditions, International Transactions on Electrical Energy Systems, 2022: Article ID 4980843, 17 pages, 2022, doi: 10.1155/2022/4980843.