Measurement-based Efficient Resource Allocation with Demand-Side Adjustments

TL;DR

This paper proposes a measurement-based resource allocation method that leverages performance feedback and task flexibility to improve efficiency and fairness in resource distribution, especially under noisy measurement conditions.

Contribution

It introduces a generalized design approach for resource allocation using only performance measurements, accounting for noise and task adjustment capabilities.

Findings

The scheme achieves fair and efficient resource distribution.

Performance measurements can guide resource allocation effectively.

The approach is robust to measurement noise.

Abstract

The problem of efficient resource allocation has drawn significant attention in many scientific disciplines due to its direct societal benefits, such as energy savings. Traditional approaches in addressing online resource allocation problems neglect the potential benefit of feedback information available from the running tasks/loads as well as the potential flexibility of a task to adjust its operation/service-level in order to increase efficiency. The present paper builds upon recent developments in the area of bandwidth allocation in computing systems and proposes a generalized design approach for resource allocation when only performance measurements of the running tasks are available, possibly corrupted by noise. We demonstrate through analysis and simulations the potential of the proposed scheme in providing fair and efficient allocation of resources in a large class of resource allocation problems.