A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article]

Abstract

C loud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

“…No entanto, o processo de provisionamento é complexo [28]. De acordo com [29], isto requer a definição das melhores configurações de software e hardware para garantir que os SLAs sejam cumpridos, além de maximizar a eficiência e a utilização do sistema. Há diversos desafios relacionados ao provisionamento de recursos e ao atendimento das requisições utilizando a infraestrutura de uma nuvem.…”

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

“…In 4 , the DRN is connected with the storage requester and sends the data. After all data to be stored are sent, the transmission complete state is sent in 5 , and the storage job is finished.…”

“…First, to measure the clustering delay time, the number of desktops was increased to 100, 200, 300, 400, 500, 1000, 2000, 3000, 4000, and 5000, as shown in Figure 16. Clustering was performed after the number of clusters was increased to 5,10,15,20,25, and 30 at each increasing point. The clustering speed at each increasing point was determined by averaging the performance speeds obtained from 50 cycles.…”

“…As shown in Figure 15, this process was more efficient and cost effective than the clustering test using 5000 actual desktops and the related cost of labor, electricity, and time. clusters was increased to 5,10,15,20,25, and 30 at each increasing point. The clustering speed at each increasing point was determined by averaging the performance speeds obtained from 50 cycles.…”

“…Although traditional on-premise computing environments continue to be built, the need remains for improving the total cost (TC) of ownership and maintenance costs of cloud systems [1][2][3][4][5][6][7][8]. The only solution to this problem, however, is to discard surplus resources, which are connected to traditional on-premise systems, or to introduce virtualization technology for all surplus resources.…”