Modeling of the resource allocation in cloud computing centers

Modeling of the resource allocation in cloud computing centers

ژورنال:Computer Networks

سال: November 2015

قیمت اصلی:35.95$

Abstract

Cloud computing offers on-demand network access to the computing resources through virtualization. This paradigm shifts the computer resources to the cloud, which results in cost savings as the users leasing instead of owning these resources. Clouds will also provide power constrained mobile users accessibility to the computing resources. In this paper, we develop performance models of these systems. We assume that jobs arrive to the system according to a Poisson process and they may have quite general service time distributions. Each job may consist of multiple numbers of tasks with each task requiring a virtual machine (VM) for its execution. The size of a job is determined by the number of its tasks, which may be a constant or a variable. The jobs with variable sizes may generate new tasks during their service times. In the case of constant job size, we allow different classes of jobs, with each class being determined through their arrival and service rates and number of tasks in a job. In the variable case a job generates randomly new tasks during its service time. The latter requires dynamic assignment of VMs to a job, which will be needed in providing service to mobile users. We model the systems with both constant and variable size jobs using birth–death processes. In the case of constant job size, we determined joint probability distribution of the number of jobs from each class in the system, job blocking probabilities and distribution of the utilization of resources for systems with both homogeneous and heterogeneous types of VMs. We have also analyzed tradeoffs for turning idle servers off for power saving. In the case of variable job sizes, we have determined distribution of the number of jobs in the system and average service time of a job for systems with both infinite and finite amount of resources. We have presented numerical results and any approximations are verified by simulation. The results of the paper may be used in the dimensioning of cloud computing centers.

KeywordsCloud computing, Queuing systems, Resource allocation, Markov process

Optimising resource costs of cloud computing for education

Optimising resource costs of cloud computing for education

ژورنال:Future Generation Computer Systems

سال: February 2016

قیمت اصلی:19.95$

Highlights•

Context-aware algorithm for allocating computing resources for class- rooms.

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Experiment setup based on real-world school data.

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Evaluation analysis considering security margin, costs, and QoS.

Abstract

There is a growing interest around the utilisation of cloud computing in education. As organisations involved in the area typically face severe budget restrictions, there is a need for cost optimisation mechanisms that explore unique features of digital learning environments. In this work, we introduce a method based on Maximum Likelihood Estimation that considers heterogeneity of IT infrastructure in order to devise resource allocation plans that maximise platform utilisation for educational environments. We performed experiments using modelled datasets from real digital teaching solutions and obtained cost reductions of up to 30%, compared with conservative resource allocation strategies.

KeywordsCloud computing, Education, Resource allocation, QoS

Resource management in cloud computing: Taxonomy, prospects, and challenges

Resource management in cloud computing: Taxonomy, prospects, and challenges

ژورنال:Computers & Electrical Engineering

سال:October 2015

قیمت اصلی:39.95$

Abstract

Cloud computing has emerged as a popular computing paradigm for hosting large computing systems and services. Recently, significant research is carried out on Resource Management (RM) techniques that focus on the efficient sharing of cloud resources among multiple users. RM techniques in cloud are designed for computing and workload intensive applications that have different optimization parameters. This study presents a comprehensive review of RM techniques and elaborates their extensive taxonomy based on the distinct features. It highlights evaluation parameters and platforms that are used to evaluate RM techniques. Moreover, it presents design goals and research challenges that should be considered while proposing novel RM techniques.

KeywordsCloud computing, Resource management, Resource allocation,Virtual machine migration, Hybrid cloud, Mobile cloud computing