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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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Citations
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A Hybrid Approach for VM Load Balancing in Cloud Using CloudSim

TL;DR: A hybrid approach has been proposed for virtual machine level load balancing using concepts of two classical algorithms for load balancing, Round Robin Algorithm and Throttled algorithm, which was found to be efficient in case of same data size per request as well as for different data sizes per request.
Journal ArticleDOI

iThermoFog: IoT‐Fog based Automatic Thermal Profile Creation for Cloud Data Centers using Artificial Intelligence Techniques

TL;DR: An artificial intelligence (AI) based automatic scheduling method that creates a thermal profile of CDC nodes using an integrated Internet of Things (IoT) and Fog computing environment called iThermoFog, which outperforms the current state‐of‐the‐art thermal‐aware scheduling method.
Journal ArticleDOI

Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment

TL;DR: Enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines are suggested and are suggested to perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines.
Proceedings ArticleDOI

A reactive fault tolerance approach for cloud computing

TL;DR: The proposed model tolerates the faults by using replication and resubmission techniques, then it decides which the best virtual machine depending on the reliability assessments, and reschedules the task once the failure occurs to the highest reliability processing node instead of replicating this task to all available nodes.
Book ChapterDOI

Techniques to Achieve Energy Proportionality in Data Centers: A Survey

TL;DR: There are several other physical elements such as cooling management and power budgeting that interact with the computing elements, thusly making a data center to exhibit both a cyber and a physical behavior.
References
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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.
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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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