Resource Utilization Prediction with Multipath Traffic Routing for Congestion-aware VM Migration in Cloud Computing

Abstract

[Load Balancing (LB) is the most essential challenge in cloud computing for assigning the workload equally among each node that avoids the state of nodes being overloaded when others are under-loaded. To tackle this challenge, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction (OH-BAC-FUP) algorithm has been proposed that considers both current and future utilizations of resources for choosing the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). On the other hand, the possibility network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, the resource utilization efficiency was degraded when the number of congestions was high. A less-than optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Therefore in this article, OH-BAC-FUP with Multipath Traffic Routing (OH-BAC-FUP-MTR) algorithm is proposed to increase the efficiency of LB and minimize the possibility of congestion occurrence in cloud data centers. Initially, OH-BAC-FUP algorithm is performed for choosing the most appropriate VMs to be migrated to the most suitable PMs. If any congestion exists due to high bandwidth use or traffic flows, then MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the maximum load on any link is applied as a measure of congestion. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes without considering the past utilization of the links. Finally, the simulation outcomes demonstrate the efficiency of OH-BAC-FUP-MTR algorithm compared to the OH-BAC-FUP. ]