Analysis of Energy Efficiency in Clouds

AbdelSalam, Hady S.; Maly, Kurt; Mukkamala, Ravi; Zubair, Mohammad; Kaminsky, David

doi:10.1109/computationworld.2009.38

Cited by 39 publications

(29 citation statements)

References 2 publications

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“…Abdelsalem et al created a mathematical model for power management in a cloud computing environment that primarily serves clients with interactive applications such as web services. The mathematical model computes the optimal number of servers and the frequencies at which they should run in [9]. A new approach for dynamic autonomous resource management in computing clouds introduced by Yazir et al [10].…”

Section: Related Workmentioning

confidence: 99%

Deadlock Detection for Resource Allocation in Heterogeneous Distributed Platforms

Cuong¹,

Dang

Pham

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract.In this paper, we study the resource allocation at the infrastructure level, instead of studying how to map the physical resources to virtual resources for better resource utilization in a cloud computing environment. We propose a new algorithm to resource allocation for infrastructure that dynamically allocate the virtual machines among the cloud computing applications based on approach algorithm deadlock detection and can use the threshold method to optimize the decision of resource reallocation. We have implemented and performed our algorithm proposed by using CloudSim simulator. The experiment results show that our algorithm can quickly detect deadlock and then resolve the situation of approximately orders of magnitude in practical cases.Keywords: Cloud Computing, Resource Allocation, Heterogeneous Platforms, Deadlock Detection. Introduction"Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent computing resources on-demand, bill on a pay-as-you-go basis, and multiply many users on the same physical infrastructure. These cloud computing environments provide an illusion of infinite computing resources to cloud users that they can increase or decrease their resources. In many cases, the need for these resources only exists in a very short period of time"Since them system of information and communication technology (ICT) was introduced, and has played a significant role in the lives of smart cities, the role of information technology infrastructure virtualization has contributed significantly to the solution of the major problem of the succession system of distributed computing, grid computing and parallel computing. In particular, tend to use cloud computing as a key is distributing virtual servers.

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Section: Related Workmentioning

confidence: 99%

Deadlock Detection for Resource Allocation in Heterogeneous Distributed Platforms

Cuong¹,

Dang

Pham

et al. 2015

Advances in Intelligent Systems and Computing

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“…In [22], assuming that service workload profiles are available, the authors introduce a mathematical model to find the number of physical servers that ensures power efficiency. The proposed solution considers dynamic CPU frequency scaling and details formulas that supply the value of that parameter, but the model assumes that Grid jobs can be partitioned between different physical servers: this assumption, viable in specific scenarios, does not fit well the general VM allocation problem.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method to Improve the Traffic Cost and Energy Consumption of Grid Networks based on AHP

Hourali¹,

Hourali²

2015

IJAIS

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The problem of Virtual Machine (VM) placement in a compute grid infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the grid provider infrastructure. One of the most important objectives of the VM placement algorithm is determine the optimal location of virtual machines in physical servers, So that the minimum number of physical servers to be turned on for enhancing the overall performance of the grid environment. Efficient placement of VMs in PMs (Physical Machines) in grid environment improves resources utilization and energy consumption. In this paper, we employ AHP method to design an integrated VM placement algorithm, called AHP VM Placement (AHPVMP) which can reduce the number of running PMs and lower the energy consumption. Extensive simulation results in GridSim environment show that the proposed algorithm outperforms existing algorithms in terms of traffic cost, SLA, energy and migration.

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“…A similar approach is also presented in [42] where a workload power-aware on-line provisioning technique reduces the energy consumption by turning off subsystems that are not needed by VMs. Other resource management schemes, for example [92], [93], [76] have also discussed energy efficiency but have ignored the heterogeneity of clusters and workloads. Reducing the number of servers involve consolidation of current demand on fewer servers, and switching off the idle servers to save energy [76].…”

Section: Resource Managementmentioning

confidence: 99%