MIRAGE: A consolidation aware migration avoidance genetic job scheduling algorithm for virtualized data centers

Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening in the first place. We have proposed a novel consolidation aware scheduling algorithm to minimize the number of migrations for batch processing systems by taking advantage of the prior knowledge of consolidation strategy and job information. We show the problem can be formulated as an integer linear programming (ILP) problem, and an effective heuristic solution can be found by a genetic algorithm. Both real and synthetic workload traces were used to evaluate our methods. Experimental results showed that, after comparing with two popular job scheduling algorithms, our approach has reduced the number of migrations by more than 25%. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study aims to minimize the number of migrations in batch processing systems through a novel consolidation aware scheduling algorithm. The experimental results demonstrate that the approach significantly reduces the number of migrations, improving energy efficiency.