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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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Journal ArticleDOI

A Q-learning-based approach for virtual network embedding in data center

TL;DR: Simulation results demonstrate that the proposed VNE algorithm for data center topology based on the Q-learning algorithm can improve the resource utilization ratio and obtain a better revenue/cost ratio of the substrate network compared with the traditional heuristic algorithms.
Proceedings ArticleDOI

Using the Multiple Knapsack Problem to Model the Problem of Virtual Machine Allocation in Cloud Computing

TL;DR: This work consists of an improvement, using the modeling of the multiple knapsack problem, with a mechanism for allocating resources called Lago Allocator, which addresses the issue of energy saving and compares the proposed solution with the original mechanism to evaluate the performance modification.
Journal ArticleDOI

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

TL;DR: A multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the e -dominance-based multi- objective artificial bee colony algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements.
Patent

Scalable metering for cloud service management based on cost-awareness

TL;DR: In this article, service management operations for managing a cloud computing platform include mediation and rating operations for revenue management of the computing platform, e.g., for revenue maximization, mediation, rating, etc.
Journal ArticleDOI

An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds

TL;DR: An adaptive individual-assessment scheme based on evolutionary states is proposed to handle the constraints in multi-objective optimization problems and it achieves better optimization ability when it is applied to solve Cloud workflow scheduling problem.
References
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Journal ArticleDOI

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

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