Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

TL;DR

Prepartition is a proactive load balancing method for cloud data centers that partitions VM requests into sub-requests to improve resource utilization and stability, outperforming reactive methods in simulations.

Contribution

It introduces Prepartition, a novel proactive load balancing approach that partitions VM requests for better resource management and stability in cloud data centers.

Findings

Prepartition improves load balancing metrics by 10-20%.

It outperforms existing algorithms in simulations.

Proactive partitioning enhances stability and resource utilization.

Abstract

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines after allocation is the traditional way for load balancing and consolidation. However, reactive migration is not easy to obtain predefined load balance objectives and may interrupt services and bring instability. Therefore, we provide a new approach, called Prepartition, for load balancing. It partitions a VM request into a few sub-requests sequentially with start time, end time and capacity demands, and treats each sub-request as a regular VM request. In this way, it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal, which supports the resource allocation in a fine-grained manner. Simulations with real-world trace and synthetic data show that Prepartition for offline (PrepartitionOff) scheduling has 10%-20% better performance than the existing load balancing algorithms under several metrics, including average utilization, imbalance degree, makespan and Capacity_makespan. We also extend Prepartition to online load balancing. Evaluation results show that our proposed approach also outperforms existing online algorithms.