Analysis of Energy Saving Approaches in Cloud Computing using Ant Colony and First Fit Algorithms

Abstract

Abstract-Cloud computing is a style of technology that is increasingly used every day. It requires the use of an important amount of resources that is dynamically provided as a service. The growth of energy consumption associated to the process of resource allocation implemented in the cloud computing is an important issue that needs to be taken into consideration. Better performance will be acquired by allowing the same required workload to be performed using a lower number of servers, which could bring to important energy savings. So it is a requirement to adopt efficient techniques in order to save and minimize energy consumed clouds such as virtual machines migration. This paper analyzes two algorithms: First Fit and Ant Colony which address the use of virtual machine migration approaches to improve the cloud performance in terms of reducing the consumed energy.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

“…The best fit algorithm flow is as follows: first, all the free areas are sorted from small to large, and then the order is compared with the size of the required resources, the free area where the first available resource size meets the demand is the final selected free area. Compared to the first fit algorithm [29] and the worst fit algorithm [30], this method is more feasible. Because the available resource size allocated to the service every time is the most suitable for this service.…”

Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

“…Decision Value Algorithm [22] Response time, QoS SP GA [23] Resource wastage SP GA [24] Cost Load dispatching Greedy, GA [25] Cost Task distribution Mixed ILP [26] Network latency SP ILP [27] Power consumption SP Weight [28] Delay SP Markov [29] Resource usage Resource allocation Consensus [30] Energy SP First-fit [31] Delay SP Own algorithms Proposed scheme…”

Simulador con heurísticas de empaquetamiento para la asignación de memoria y despacho de procesos

“…En un trabajo reciente en Ahmed [11] se argumenta que la asignación de recursos (procesos, uso de la memoria y migración de procesos) sobre una granja de servidores en la nube hace que se aumente la carga de trabajo y por consiguiente el consumo de energía. Para resolverlo proponen el uso de dos algoritmos; uno heurístico y el otro una metaheurística, FF y colonia de hormigas, respectivamente.…”