&lt;italic&gt;EnReal&lt;/italic&gt;: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment

Xu, Xiaolong; Dou, Wanchun; Zhang, Xuyun; Chen, Jinjun

doi:10.1109/tcc.2015.2453966

Cited by 140 publications

(91 citation statements)

References 36 publications

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“…And the tasks in the private clouds are also deployed on the PMs in P. The energy consumption in the private cloud for the execution of the privacy-aware applications mainly refers to the energy consumed by the PM base power, active VMs, and the unused VMs, and the energy consumption due to data transferring. The PMs in the sleep mode also consume a certain amount of power, but it is far less than the energy consumed by the active PMs in the order of magnitude, that could be neglected [16,17].…”

Section: Access Time and Energy Consumption Analysis In Privatementioning

confidence: 99%

“…Generally, the resource capacity of PMs and the resource requirements from tasks and datasets are specified by the amount of the resource units, that is, the VM instances [16]. For many public cloud vendors, such as Amazon, they provide many types of VM instances, including CPUintensive instances and I/O optimized instances.…”

Section: Vm Identification For Data Placementmentioning

confidence: 99%

“…= MAX , = 1 (8) while ≤ | V | do (9) if V , can hold then (10) Calculate the total cost TC by (10) (11) if TC < then (12) = TC (13) else = + 1 (14) end if (15) end if (16) end while (17) U p d a t e with cost C (18) end if (19) PM 2 separately, and the data transferring between these two VMs only employs the switch 4 .…”

Section: Inputmentioning

confidence: 99%

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Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Zhao

Ruan

et al. 2017

Security and Communication Networks

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Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.

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Section: Access Time and Energy Consumption Analysis In Privatementioning

confidence: 99%

Section: Vm Identification For Data Placementmentioning

confidence: 99%

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Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Zhao

Ruan

et al. 2017

Security and Communication Networks

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“…12,13 The specification of these four types of PMs is listed in Table 1. In the simulated cloud environment, 1000 PMs are created and the amount of each type of the PMs is 250.…”

Section: Experimental Contextmentioning

confidence: 99%

“…And based on the HP operation documents, the peak power consumption of type 3 and type 4 PMs for each processor is set to 80 and 95 W, separately. 12,13 As the PM consumes nearly 60% of the power, the power consumption of type 3 and type 4 PMs is set to 192 and 342 W in this article, separately. 12,13,17,18 There are five basic parameters in our experiment.…”

Section: Experimental Contextmentioning

confidence: 99%

Fair energy-efficient virtual machine scheduling for Internet of Things applications in cloud environment

Xing

Xiang

et al. 2017

International Journal of Distributed Sensor Networks

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With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.

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An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Dou

Meng

et al. 2016

Concurrency and Computation

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Summary Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on‐demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first‐class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy‐aware manner. In this paper, we propose an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

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<italic>EnReal</italic>: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment

Cited by 140 publications

References 36 publications

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Fair energy-efficient virtual machine scheduling for Internet of Things applications in cloud environment

An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data

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