Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

Ramanpreet Kaur; Divya Anand; Upinder Kaur; Sahil Verma

doi:10.4108/eetsis.5716

Authors

Ramanpreet Kaur Lovely Professional University
Divya Anand Lovely Professional University
Upinder Kaur Akal University
Sahil Verma CGC Jhanjeri

DOI:

https://doi.org/10.4108/eetsis.5716

Keywords:

Multi-cloud, Deep reinforcement learning, Resource allocation, Cyber shake seismogram workflow, Task scheduling Enhanced Flower Pollination

Abstract

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.

OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.

METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.

RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment

CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Author Biography

Ramanpreet Kaur, Lovely Professional University

Baba Farid College of Engineering and Technology

References

Petcu,D.: Consuming Resources and Services from Multiple Clouds,.J Grid Computing ,12(2) ,321–345(2014).

Panda,S.K.,Jana,P.K.: SLA-based task scheduling algorithms for the heterogeneous multi-cloud environment. J Supercomputing,73(6), 2730–2762(2017).

Keshavarzi, A., Haghighat, A.T., Bohlouli, M.: Enhanced time-aware QoS prediction in multi-cloud: a hybrid k-medoids and lazy learning approach(QoPC). Computing 102( 4),923–949(2020).

Munteanu,V.,Sandru,C.,Petcu,D.: Multi-cloud resource management: cloud service interfacing, Munteanuetal. Journal of Cloud Computing: Advances, Systems and Applications (2014).

Ferry,N., Chauvel,F., Song,H., Rossini,A.: Lushpenko,M.,Solberg.:CloudMF: Model-Driven Management of Multi-Cloud Applications. ACM Transactions on Internet Technology18(2)(2018).

Guerrero,C., Lera,I.,Juiz, C.:Resource optimization of container orchestration:a case study in multi-cloud microservices-based application J Supercomput74(7),2956-2983(2018).

Kritikos,k.,Zeginis,C.,Iranzo,J.,Gonzalez,R.,Seybold,D.,Griesinger,F., Domaschk,J.:Multi-cloud provisioning of business processes.Journal of Cloud Computing: Advances, Systems and Applications(2019).

Li,C.,Zhang,J.,Tang,H.: Replica-aware task scheduling and load-balanced cache placement for delay reduction in the multi-cloud environment.The Journal of Supercomputing, 75(5),2805-2836(2019).

Mohammadi,S.,Pedram,H.,Karimi,.: Integer linear programming-based cost optimization for scheduling scientiﬁc workﬂows in multi-cloud environments.The Journal of Supercomputing 4717–4745(2018).

Li,J.,Lin,Y.,Jia,X.,Ren,K.:Multiple-replica integrity auditing schemes for cloud data storage.Concurrency Computat Pract Exper(2019).

Souri,A.,Rahmani,A.,Rezaei,N.:A hybrid formal verification approach for QoS-aware multi-cloud service composition. Cluster Computing(2453–2470)2020.

Carvalho,J., Trinta,F., Vieira,D., Cortes,O.:Evolutionary solutions for resources management in multiple clouds: State-of-the-art and future directions. Future Generation Computer Systems, 88,284-296, 2018.

Masdari,M.,Zangakani,M.: Efficient task and workflow scheduling in inter‑cloud environments: challenges and opportunities.The Journal of Supercomputing,499-535(2019).

Bruno,R.:Costa,F.,P,Ferreira.:free Cycles - Efficient Multi-Cloud Computing Platform”, J Grid Computing,15(1)501–526(2017).

Paraiso,F.,Merle,P.,Seinturier,L.:so Cloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds.Computing.539–565(2016).

Rashida,S.,Sabaei, M.,Ebadzadeh, M.,Rahmani,A.:A memetic grouping genetic algorithm for cost-efficient VM placement in the multi-cloud environment,Cluster Computing 23(2),797–836(2020).

Khan, M.:Optimized hybrid service brokering for multi‑cloud architectures”, The Journal of Supercomputing, 76,666–687(2020).

Panda,S.,Gupta,I., Jana,P.:Task scheduling algorithms for multi-cloud systems: allocation-aware approa21, 241–259 (2019).

Lijin P.: Resource Allohation in Multi-Cloud Based on Usage Logs.International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT 3(2018).

Pietrabissa,A., Priscoli,F., Giorgio,A., Giuseppi,A.,Panfili,M., Suraci,V.:An Approximate Dynamic Programming Approach to Resource Management in Multi-Cloud Scenarios,International Journal of Control, 492-503(2016).

Mishra,S., Mishra,S.,Alsayat,A.,Jhanjhi,N.,Humayun,M., Sahoo,K.,Luhach,A.:Energy-Aware Task Allocation for Multi-Cloud Networks, IEEE Access,8, (2020).

Carvalho,J., Vieira,D., Trinta,F.:Dynamic Selecting Approach for Multi-cloud Providers”, Springer International Publishing AG, part of Springer Nature , 37–51,(2018).

Antonio,P., Stefano,B.,Francisco,F.: Alessandro,G., Guido,O., Martina ,P.,Vincenzo,S.:Resource Management in Multi-Cloud Scenarios via Reinforcement Learning.Proceedings of the 34th Chinese Control Conference July 28-30(2015).

Kang,S., Veeravalli,B.,Aung,K.:Dynamic scheduling strategy with efficient node availability prediction for handling divisible loads in multi-cloud systems.J. Parallel Distrib. Computing, 113,pages 1-16(2018).

Chen,Z., Lin,K., Lin ,B., Chen ,X., Zheng,X., Rong,C.:Adaptive Resource Allocation and Consolidation for Scientific Workflow Scheduling in Multi-Cloud Environments.IEEE Access, 8, 2020.

Panda,S., Jana,P.:Efficient task scheduling algorithms for the heterogeneous multi-cloud environment. J Supercomput (2015) .

Farid ,M.,Latip ,R., Hussin ,M.,Hamid,N.:Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment.IEEE Access ,8(2020).

Subramanian,T.,Savarimuthu1,N.:Application-based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds. Vietnam J ComputSci,3(1) 57-70 (2016).

Thirumalaiselvan,C., Venkatachalam,V.: A strategic performance of virtual task scheduling in multi cloud environment.Cluster Comput (2019).

Grozev,N.,Buyya,R.:Regulations and latency-aware load distribution of web applications in Multi-Clouds. J Supercomput (2016).

Zhan,W.,Luo,C. Wang,J., Wang,C., Min,G., Duan,H., Zhu,Q.:Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing.IEEE Internet of Things Journal, 7(6), 5449-5465(2020).

Qi,Qi.,Wang,J., Ma,Z., Sun,H., Cao,Y., Zhang,L.,Liao,J.:Knowledge-Driven Service Offloading Decision for Vehicular Edge Computing.A Deep Reinforcement Learning Approach.IEEE Transactions on Vehicular Technology, 68(5),4192-4203(2019).

Wang,Y.,Liu,H., Zheng,W., Xia ,Y., Li,Y., Chen,P., Guo,K.,Xie,H.:Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning.IEEE Access, 7,39974-39982(2019.

Baer.S.,Bakakeu,J.,Meyes,R.,Meisen,T.:Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems.2019 Second International Conference on Artificial Intelligence for Industries ,22-25(2019)

Zhang,L., Wang,Q., Sun,H., Liao,J.:Multi-task Deep Reinforcement Learning for Scalable Parallel Task Scheduling.2019 IEEE International Conference on Big Data (Big Data), 2992-3001(2019).

Shetty,C.; Sarojadevi,H.,Prabhu,S.:Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud.Turkish Journal of Computer and Mathematics Education, 12(6),2565-2580(2021)..