On Non-Preemptive VM Scheduling in the Cloud

TL;DR

This paper introduces a non-preemptive VM scheduling algorithm for cloud data centers that balances throughput, delay, and complexity, outperforming prior methods through theoretical guarantees and extensive simulations.

Contribution

It proposes a novel non-preemptive scheduling algorithm with tunable parameters, achieving near-optimal throughput without requiring synchronization or high complexity.

Findings

Algorithm achieves a fraction of the throughput region based on Knapsack approximation

Tunable parameters allow balancing throughput, delay, and complexity

Simulation results verify improved performance over existing approaches

Abstract

We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service \textit{cannot} be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio $r$, our scheduling algorithm can provide $r\beta$ fraction of the throughput region for $\beta < r$. In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to $\beta<1$. The parameters $\beta$ and $r$ can be tuned to provide a tradeoff between achievable throughput, delay, and computational complexity of the scheduling algorithm. Finally extensive simulation results using both synthetic and real traffic traces are presented to verify the performance of our algorithm.