Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments

TLDR

Cloud computing enables utility‑like provisioning of resources, but pay‑as‑you‑go models create new resource‑provisioning challenges that require cost minimization while meeting application needs. The study aims to develop and evaluate a framework that dynamically adapts cloud resource allocation for adaptive applications, maximizing application‑specific QoS within fixed time and budget constraints. The framework employs a multi‑input‑multi‑output feedback control model with reinforcement learning to adjust adaptive parameters and a trained resource model to modify allocations, ensuring optimal application benefit while respecting time limits and budget constraints. Evaluation on two real‑world adaptive applications shows the framework is effective and incurs very low overhead.

Abstract

The recent emergence of clouds is making the vision of utility computing realizable, i.e., computing resources and services can be delivered, utilized, and paid for as utilities such as water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed in a way to keep resource costs to a minimum, while meeting an application's needs. In this work, we focus on the use of cloud resources for a class of adaptive applications, where there could be application-specific flexibility in the computation that may be desired. Furthermore, there may be a fixed time-limit as well as a resource budget. Within these constraints, such adaptive applications need to maximize their Quality of Service (QoS), more precisely, the value of an application-specific benefit function, by dynamically changing adaptive parameters. We present the design, implementation, and evaluation of a framework that can support such dynamic adaptation for applications in a cloud computing environment. The key component of our framework is a multi-input-multi-output feedback control model-based dynamic resource provisioning algorithm which adopts reinforcement learning to adjust adaptive parameters to guarantee the optimal application benefit within the time constraint. Then a trained resource model changes resource allocation accordingly to satisfy the budget. We have evaluated our framework with two real-world adaptive applications, and have demonstrated that our approach is effective and causes a very low overhead.