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Journal ArticleDOI

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

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TLDR
The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Abstract
Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.

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Journal ArticleDOI

A scheduling scheme in the cloud computing environment using deep Q-learning

TL;DR: A novel artificial intelligence algorithm, called deep Q-learning task scheduling (DQTS), that combines the advantages of the Q- learning algorithm and a deep neural network is proposed, aimed at solving the problem of handling directed acyclic graph tasks in a cloud computing environment.
Journal ArticleDOI

EMUSIM: an integrated emulation and simulation environment for modeling, evaluation, and validation of performance of Cloud computing applications

TL;DR: The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model, which was able to accurately model such application via emulator and use the model to supply information about its potential performance in a Cloud provider.
Journal ArticleDOI

Quality of service approaches in cloud computing

TL;DR: In this paper, the authors conducted a systematic mapping study to find the related literature, and 67 articles were selected as primary studies that are classified in relation to the focus, research type and contribution type.
Journal ArticleDOI

Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

TL;DR: An energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm, that migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers.
Journal ArticleDOI

Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers

TL;DR: A virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers and reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.
References
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A view of cloud computing

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Book

The Grid 2: Blueprint for a New Computing Infrastructure

TL;DR: The Globus Toolkit as discussed by the authors is a toolkit for high-throughput resource management for distributed supercomputing applications, focusing on real-time wide-distributed instrumentation systems.
Journal ArticleDOI

Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.
Journal ArticleDOI

The GRID: Blueprint for a New Computing Infrastructure

TL;DR: The main purpose is to update the designers and users of parallel numerical algorithms with the latest research in the field and present the novel ideas, results and work in progress and advancing state-of-the-art techniques in the area of parallel and distributed computing for numerical and computational optimization problems in scientific and engineering application.
Journal ArticleDOI

GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing

TL;DR: This work states that clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing and introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics.
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