A QoS aware Novel Probabilistic strategy for Dynamic Resource Allocation

TL;DR

This paper introduces a probabilistic, game-based resource allocation strategy for service computing that forecasts outcomes and balances load by classifying demand factors and estimating system parameters.

Contribution

It presents a novel two-player game approach that classifies demand factors and uses probabilistic estimation for dynamic, load-balanced resource allocation in cloud and grid environments.

Findings

Effective classification of demand factors

Improved load balancing through probabilistic forecasting

Enhanced resource utilization and reduced delay

Abstract

The paper proposes a two player game based strategy for resource allocation in service computing domain such as cloud, grid etc. The players are modeled as demand/workflows for the resource and represent multiple types of qualitative and quantitative factors. The proposed strategy will classify them in two classes. The proposed system would forecast outcome using a priori information available and measure/estimate existing parameters such as utilization and delay in an optimal load-balanced paradigm. Keywords: Load balancing; service computing; Logistic Regression; probabilistic estimation