Abstract
A Federated cloud is a composition of several clouds where a single federated cloud manager (FCM) is responsible for communication with the cloud service providers (CSPs) of associated clouds to accomplish the task of resource management. Finding optimal schedules for the incoming workloads from users is a highly complex task, as these workloads are expected to be completed under different deadlines. The existing frameworks for workload scheduling in federated cloud faced serious drawbacks such as inefficiency, unable to meet the deadlines assigned by users, etc. To overcome such drawbacks, this work introduces a new and effective strategy based on an optimization algorithm to achieve optimal scheduling of workloads. The proposed architecture involves a single FCM to identify the load in different clouds through communication with CSPs. A load level calculator (LLC) and a workload partitioning module (WPM) are maintained by the FCM to analyze the load in each cloud. Further, based on the load factor (LF) computed, the incoming workloads are partitioned into sub-queues of different sizes and are forwarded to the main clouds. The respective CSPs of the clouds maintain a cloud workload queue (CWQ) to locate the workloads and analyze the resource requirements. The CSP executes a scheduler based on hybrid flow-directed whale optimization (HFDWO) to find the optimal VMs in the data centre (DC) that can run the incoming workloads in the queue. The workloads are scheduled accordingly, and evaluations are conducted through simulations in the CloudSim tool. The performance of the approach is analyzed using the GWA T-12 Bitbrains dataset under different metrics. The overall improvement attained by the proposed approach compared to the existing frameworks is 25% in terms of SLA violation rate, 12% in terms of execution cost, 11% in terms of resource utilization, 33% in terms of makespan, 19% in terms of throughput and 28% in terms of response time.
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References
Tabrizchi, H., Kuchaki Rafsanjani, M.: A survey on security challenges in cloud computing: issues, threats, and solutions. J. Supercomput. 76(12), 9493–9532 (2020)
Saif, M.A., Niranjan, S.K., Al-Ariki, H.D.: Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis. Wireless Netw. 27(4), 2829–2866 (2021)
Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23, 377–395 (2020)
Liang, H., Du, Y., Gao, E., Sun, J.: Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging. Futur. Gener. Comput. Syst. 107, 351–367 (2020)
Lindsay, D., Gill, S.S., Smirnova, D., Garraghan, P.: The evolution of distributed computing systems: from fundamental to new frontiers. Computing 103(8), 1859–1878 (2021)
Tomarchio, O., Calcaterra, D., Modica, G.D.: Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. J. Cloud Comput. 9, 1–24 (2020)
Ray, B.K., Saha, A., Khatua, S., Roy, S.: Proactive fault-tolerance technique to enhance reliability of cloud service in cloud federation environment. IEEE Trans. Cloud. Comput. 10(2), 957–971 (2020)
Ahmad, A., Alzahrani, A.S., Ahmed, N., Ahsan, T.: A delegation model for SDN-driven federated cloud. Alex. Eng. J. 59(5), 3653–3663 (2020)
Neto, E.P., Silva, F.S.D., Schneider, L.M., Neto, A.V., Immich, R.: Seamless mano of multi-vendor sdn controllers across federated multi-domains. Comput. Netw. 186, 107752 (2021)
Yadav, M., Poongodi, T.: Federated cloud service management and IoT. In: Prakash, K.B. (ed.) Internet of Things: From the Foundations to the Latest Frontiers in Research, pp. 101–126. De Gruyter, Berlin, Boston (2020)
Celesti, A., Ruggeri, A., Fazio, M., Galletta, A., Villari, M., Romano, A.: Blockchain-based healthcare workflow for tele-medical laboratory in federated hospital IoT clouds. Sensors 20(9), 2590 (2020)
Saxena, D., Gupta, R., Singh, A.K.: A survey and comparative study on multi-cloud architectures: emerging issues and challenges for cloud federation. arXiv preprint arXiv:2108.12831 (2021)
Chouhan, L., Bansal, P., Lauhny, B., Chaudhary, Y.: A survey on cloud federation architecture and challenges. In: Social Networking and Computational Intelligence: Proceedings of SCI-2018, pp. 51–65. Springer, Singapore (2020)
Amin, R., Vadlamudi, S., Rahaman, M.M.: Opportunities and challenges of data migration in cloud. Eng. Int. 9(1), 41–50 (2021)
Mazidi, A., Golsorkhtabaramiri, M., Yadollahzadeh Tabari, M.: An autonomic risk-and penalty-aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioning. Int. J. Commun. Syst. 33(7), e4334 (2020)
Yang, F.: Cloud Computing Virtual Resource Dynamic System Allocation and Application Based on System Architecture. Dynam. Systems Appl. 30(5), 753–770 (2021)
Kar, B., Yahya, W., Lin, Y.-D., Ali, A.: Offloading using traditional optimization and machine learning in federated cloud-edge-fog systems: A survey. IEEE Commun. Surv. Tutor. 25(2), 1199–1226 (2023)
Javanmardi, S., Shojafar, M., Mohammadi, R., Persico, V., Pescapè, A.: S-FoS: A secure workflow scheduling approach for performance optimization in SDN-based IoT-Fog networks. J. Inf. Secur. Appl. 72, 103404 (2023)
Pacini, E., Iacono, L., Mateos, C., Garino, C.G.: A bio-inspired datacenter selection scheduler for federated clouds and its application to frost prediction. J. Netw. Syst. Manage. 27(3), 688–729 (2019)
Serrano, M., Hauswirth, M., Soldatos, J., Kefalakis, N.: Insights on federated cloud service management and the internet of things. In: Ovidiu, V., Peter, E. (eds.) Internet of Things Applications-From Research and Innovation to Market Deployment, pp. 315–349. River Publishers (2022)
Khorasani, N., Abrishami, S., Feizi, M., Esfahani, M.A., Ramezani, F.: Resource management in the federated cloud environment using Cournot and Bertrand competitions. Futur. Gener. Comput. Syst. 113, 391–406 (2020)
Luo, C., Qiao, B., Chen, X., Zhao, P., Yao, R., Zhang, H., Wu, W., Zhou, A., Lin, Q.: Intelligent Virtual Machine Provisioning in Cloud Computing, pp. 1495–1502. Proceedings of the 29th IJCAI-20 (2020)
Jalali Moghaddam, M., Esmaeilzadeh, A., Ghavipour, M., Zadeh, A.K.: Minimizing virtual machine migration probability in cloud computing environments. Clust. Comput. 23, 3029–3038 (2020)
Mc Donnell, N., Howley, E., Duggan, J.: Dynamic virtual machine consolidation using a multi-agent system to optimize energy efficiency in cloud computing. Futur. Gener. Comput. Syst. 108, 288–301 (2020)
Tripathi, A., Pathak, I.: Vidyarthi, DP: Modified dragonfly algorithm for optimal virtual machine placement in cloud computing. J. Netw. Syst. Manage. 28, 1316–1342 (2020)
Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., Rida, M.: FACO: A hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing. J. Ambient. Intell. Humaniz. Comput. 11, 3975–3987 (2020)
Fister Jr, I., Yang, X.S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186. (2013)
Rajeshwari, B.S., Dakshayini, M., Guruprasad, H.S.: Efficient task scheduling and fair load distribution among federated clouds. J. ICT Res. Appl. 15(3), 216–238 (2021)
Keshavarzi, A., Haghighat, A.T., Bohlouli, M.: Clustering of large scale QoS time series data in federated clouds using improved variable Chromosome Length Genetic Algorithm (CQGA). Expert Syst. Appl. 164, 113840 (2021)
Ebadifard, F., Babamir, S.M.: Federated geo-distributed clouds: optimizing resource allocation based on request type using autonomous and multi-objective resource sharing model. Big Data Res. 24, 100188 (2021)
Varghese, J., Sreenivasaiah, J.: Entropy Based Monotonic Task Scheduling and Dynamic Resource Mapping in Federated Cloud Environment. Int. J. Intell. Eng. Syst. 15(1), 235–250 (2022)
Shishira, S.R., Kandasamy, A.: Ontology Based Context-Aware Model for Intelligent Scheduling in Federated Cloud. Int. J. Futur. Gener. Commun. Netw. 13(1), 1072–1080 (2020)
Saif, M.A., Niranjan, S.K., Murshed, B.A., Al-ariki, H.D., Abdulwahab, H.M.: Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerizedmulti-cloud environment. J. Ambient Intell Humanized Comput. 14(9), 12895–12920 (2022)
Mosleh, M.A., Radhamani, G., Hazber, M.A., Hasan, S.H.: Adaptive cost-based task scheduling in cloud environment. Sci. Program. 2016, 1–9 (2016)
Karami, H., Anaraki, M.V., Farzin, S., Mirjalili, S.: Flow direction algorithm (FDA): a novel optimization approach for solving optimization problems. Comput. Ind. Eng. 156, 107224 (2021)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Gharehchopogh, F.S., Gholizadeh, H.: A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm Evol. Comput. 48, 1–24 (2019)
Shen, S., Van Beek, V., Iosup, A.: Statistical characterization of business-critical workloads hosted in cloud datacenters. In2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 465–474 (2015)
Karthikeyan, R., Sundaravadivazhagan, B., Cyriac, R., Balachandran, P.K., Shitharth, S.: Preserving resource handiness and exigency-based migration algorithm (PRHEM) for energy efficient federated cloud management systems. Mob. Inf. Syst. 2023, 1–11 (2023)
Vadla, P.K., Kolla, B.P., Perumal, T.: FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud. Pertanika J. Sci. Technol. 28(1), 117–140 (2020)
Khelifa, A., Mokadem, R., Hamrouni, T., Charrada, F.B.: Data correlation and fuzzy inference system-based data replication in federated cloud systems. Simul. Model. Pract. Theory 115, 102428 (2022)
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Kshatriya, D., Lepakshi, V.A. An Efficient Hybrid Scheduling Framework for Optimal Workload Execution in Federated Clouds to Maintain Performance SLAs. J Grid Computing 21, 47 (2023). https://doi.org/10.1007/s10723-023-09682-x
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DOI: https://doi.org/10.1007/s10723-023-09682-x