Stochastic Service Placement

TL;DR

This paper explores a stochastic resource allocation strategy for cloud services that reduces costs by matching resource provisioning to demand variability, outperforming traditional overprovisioning methods through analysis and simulation.

Contribution

It provides a comprehensive analysis and simulation demonstrating the effectiveness of stochastic service placement over conventional overprovisioning in cloud resource management.

Findings

Stochastic placement reduces costs compared to overprovisioning.

Analysis shows effectiveness for normal distributed demands.

Simulation confirms advantages on synthetic data.

Abstract

Resource allocation for cloud services is a complex task due to the diversity of the services and the dynamic workloads. One way to address this is by overprovisioning which results in high cost due to the unutilized resources. A much more economical approach, relying on the stochastic nature of the demand, is to allocate just the right amount of resources and use additional more expensive mechanisms in case of overflow situations where demand exceeds the capacity. In this paper we study this approach and show both by comprehensive analysis for independent normal distributed demands and simulation on synthetic data that it is significantly better than currently deployed methods.