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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 dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing

TL;DR: A dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment is proposed.
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EDoS-ADS: An Enhanced Mitigation Technique Against Economic Denial of Sustainability (EDoS) Attacks

TL;DR: EDoS-ADS is the first known technique that effectively prevents an EDoS attack from blocking an entire NAT-based network from accessing the cloud, and successfully differentiates between legitimate and attacker clients even when they belong to the same NAT- based network.
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Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

TL;DR: A novel Self Adaptive Particle Swarm Optimization SAPSO algorithm is proposed to solve the intractable nature of the above challenge of power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.
Journal ArticleDOI

A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud

TL;DR: A novel job scheduling scheme based on reinforcement learning is designed to minimize the makespan and Average Waiting Time under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning algorithm.
Proceedings ArticleDOI

Scheduling Latency-Sensitive Applications in Edge Computing

TL;DR: This work proposes a score-based edge service scheduling algorithm that evaluates both network and computational capabilities of edge nodes and outputs the maximum scoring mapping between services and resources.
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