A Self-adaptive Agent-based System for Cloud Platforms

TL;DR

This paper presents a self-adaptive multi-agent cloud system that dynamically allocates resources based on QoS and runtime data, improving energy efficiency and user satisfaction through simulation validation.

Contribution

It introduces a novel self-adaptive cloud architecture using a MAPE-K based multi-agent system for dynamic resource management.

Findings

Reduces energy consumption in cloud resource allocation.

Maintains QoS satisfaction despite dynamic environments.

Effective simulation validation of the proposed system.

Abstract

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS.