Measurement-based Efficient Resource Allocation with Demand-Side Adjustments
For system administrators managing computing resources, this work provides a practical method to improve allocation efficiency without requiring explicit demand models.
This paper proposes a measurement-based resource allocation framework that uses feedback from running tasks to adjust demand, achieving fair and efficient allocation in noisy environments.
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.