Welcome to the upgraded BRAC University Institutional Repository. We are currently organizing collections after a recent system upgrade. Homepage category counters may temporarily show lower numbers while syncing, but over 27,000 repository items remain safe and accessible. Please use the search bar to find theses, scholarly outputs, and institutional documents.

Consolidating virtual machines by using Ant colony system algorithm for green cloud computing

Citation

Abstract

Cloud computing is an internet based computing system enabling convenient, flexible, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, services) that can be rapidly provisioned and released with minimal management effort or service provider interactions. Although cloud is a virtual and non-physical network-based concept, its implementation includes physical and wired connectivity among servers, hard-drives and other network devices resulting in large number of data centers. As such power and energy consumptions increase total carbon footprint which is very hazardous for the environment. Green Cloud Computing is a concept of consuming less physical power and saving energy by making the infrastructure more virtual. With the help of the Ant Colony System (ACS) algorithm, it is possible to consolidate the Virtual Machines and make Cloud Computing more virtual than it already is. This algorithm describes a mechanism of indirect coordination, through the environment, between agents or actions e.g. communication about paths between ants with the help of pheromones. Using this technique, the Virtual Machines (VMs) that constitute Cloud Computing can be amalgamated and consolidated. Thus, Green Cloud Computing can be easily globalized.

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-45).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.

Publisher Link

Type

Thesis