Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Abstract

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

Dynamic Cloud Resource Allocation: A Broker-Based Multi-Criteria Approach for Optimal Task Assignment

“…The main objective of their study was to reduce costs, energy consumption, and execution time. Based on the VIKOR approach, Radi et al [21] provided a service broker policy for choosing the optimal data center in cloud computing. Through consideration of variables including cost, response time, and data center processing time, the policy sought to maximize user priorities.…”

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“…This approach aims to effectively balance the workload of hosts and improve the efficiency of the consolidation process. Last but not least, the work contributed by [5] has proposed the Hybrid VM allocation and placement policy (HVMAP) for VM consolidation in cloud data centers. The HVMAP strategy combines an improved host overload detection algorithm with migration control to identify overloaded hosts and the PEBFD placement algorithm to determine the appropriate destination host for migrating VMs.…”

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