Residual Resource Defragmentation Based on ECRC (Enhanced Cloud Resource Consolidating)

Abstract In cloud computing, server consolidation is the part where very few persons go through the same. By consolidating the unused server space, memory can be reused for another data allocation. The objective of this paper is to improve resource utilization. Residual resource fragmentation refers to the state of the data center where sufficient amount of residual resources are available for any new VM allocation. To achieve this, there are three methods followed here. Active physical servers are identified. Then the maximum utilization of the resources is found out. Finally the resources are allocated and scheduled using the developed algorithm. In this work, we have proposed a new algorithm enhanced cloud consolidating algorithm. This algorithm improves some of the qualities of the cloud consolidating algorithm. Here the allocation technique is based on the cost and the memory

Analysis of power consumption in heterogeneous virtual machine environments

Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the difficulty degree, when trying to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster

Algorithmes d’ordonnancement des tâches dans un environnement Cloud

Les systèmes distribués à grande échelle comme les Grilles ou les Nuages (Clouds) [8] sont fondamentalement dynamiques et instables, et il est également réaliste de considérer que certaines ressources vont subir des défaillances pendant leur utilisation. La panne d’une ressource peut affecter l’entière exécution des applications qui nécessitent la disponibilité de plusieurs ressources en même temps. Afin de pouvoir gérer des plates-formes dynamiques à grande échelle, il faut se tourner vers des algorithmes d'ordonnancement et d'équilibrage de charge décentralisés, de telle sorte que le système puisse passer à l'échelle, sans que les performances de la plate-forme soient limitées par celle du noeud en charge de l'ordonnancement. Dans ce papier, nous présentons un état de l’art sur les algorithmes d'ordonnancement et d'équilibrage de charge destinés pour les Clouds. Nous proposons comme synthèse une classification de ces algorithmes sur la base de critères et de dimensions que nous avons définis à cet effet

ANÁLISE DO CONSUMO DE ENERGIA DE MIGRAÇÃO DE MÁQUINAS VIRTUAIS EM NUVEM IAAS

Um dos principais custos variáveis de um data center é a energia elétrica. Só em 2013 os data centers da Google consumiram cerca de 260 milhões de Watts de energia, o equivalente a 0,01% da energia consumida mundialmente. Visto pelo aspecto financeiro e associado ao consumo de energia, o custo energético de manter uma máquina ociosa (≤ 10%) ativa é alto se comparado com o consumo desta mesma máquina a 100%. Investimentos em técnicas de consolidação se tornaram um aliado na redução de custos, pois otimizam a capacidade de recursos de data centers e possibilitam o desligamento de máquinas ociosas. No contexto deste trabalho, tem-se por objetivo mensurar o impacto gerado pela aplicação da técnica de consolidação, com foco na operação de migração de máquinas virtuais em nuvens computacionais IaaS, no consumo de energia. Se por um lado, a decisão de consolidar visa reduzir o consumo de energia, por outro, o provisionamento com reorganização das máquinas virtuais nos recursos disponíveis pode gerar consumo adicional. É fato que a migração gera consumo adicional de energia decorrente do aumento de operações na infraestrutura de rede e no efetivo deslocamento do serviço. Portanto, quantificar e analisar estes aspectos são as principais contribuições deste trabalho

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required