CAMIG: Concurrency-Aware Live Migration Management of Multiple Virtual Machines in SDN-enabled Clouds

TL;DR

This paper introduces CAMIG, a concurrency-aware method for managing multiple VM live migrations in SDN-enabled clouds, reducing interference and improving reallocation efficiency by considering resource dependencies.

Contribution

It proposes a novel migration selector based on maximal cliques and independent sets to optimize multiple VM migrations considering resource dependencies.

Findings

Reduces migration interference and SLA violations.

Shortens convergence time of VM reallocation.

Maximizes migration performance in dynamic resource management.

Abstract

By integrating Software-Defined Networking and cloud computing, virtualized networking and computing resources can be dynamically reallocated through live migration of Virtual Machines (VMs). Dynamic resource management such as load balancing and energy-saving policies can request multiple migrations when the algorithms are triggered periodically. There exist notable research efforts in dynamic resource management that alleviate single migration overheads, such as single migration time and co-location interference while selecting the potential VMs and migration destinations. However, by neglecting the resource dependency among potential migration requests, the existing solutions of dynamic resource management can result in the Quality of Service (QoS) degradation and Service Level Agreement (SLA) violations during the migration schedule. Therefore, it is essential to integrate both single and multiple migration overheads into VM reallocation planning. In this paper, we propose a concurrency-aware multiple migration selector that operates based on the maximal cliques and independent sets of the resource dependency graph of multiple migration requests. Our proposed method can be integrated with existing dynamic resource management policies. The experimental results demonstrate that our solution efficiently minimizes migration interference and shortens the convergence time of reallocation by maximizing the multiple migration performance while achieving the objective of dynamic resource management.