Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2

TL;DR

This paper proposes an adaptive pre-allocation strategy for cloud computing resources using ART-2 neural networks to classify request streams and improve resource management under unpredictable request patterns.

Contribution

It introduces a novel approach employing ART-2 neural networks for auto-classification of cloud requests to optimize resource pre-allocation strategies.

Findings

Enhanced request classification accuracy

Improved resource utilization efficiency

Reduced response time variability

Abstract

One of the major challenges of cloud computing is the management of request-response coupling and optimal allocation strategies of computational resources for the various types of service requests. In the normal situations the intelligence required to classify the nature and order of the request using standard methods is insufficient because the arrival of request is at a random fashion and it is meant for multiple resources with different priority order and variety. Hence, it becomes absolutely essential that we identify the trends of different request streams in every category by auto classifications and organize preallocation strategies in a predictive way. It calls for designs of intelligent modes of interaction between the client request and cloud computing resource manager. This paper discusses about the corresponding scheme using Adaptive Resonance Theory-2.