Investigating Energy Consumption and Performance Trade-Off for Interactive Cloud Application

Hasan, Sabbir; Alvares, Frederico; Ledoux, Thomas; Pazat, Jean-Louis

doi:10.1109/tsusc.2017.2714959

Cited by 27 publications

(17 citation statements)

References 37 publications

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“…Besides, an execution of a few control loops based application controllers to fulfil distinctive execution (i.e., reaction time, accessibility and experience) and resource measurements (i.e., nature of energy). Besides, our examination confirms that the energy utilization goes astray as less as when their methodology is scaled utilizing a few physical hubs [3]. Upgraded two existing methods is by improving the energy productivity and presenting SLA attention to minimize the general SLA infringement.…”

Section: Related Worksupporting

confidence: 56%

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Kumar¹,

Kumar²

2019

IJEAT

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Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.

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Section: Related Worksupporting

confidence: 56%

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Kumar¹,

Kumar²

2019

IJEAT

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“…SR(Service duration, Life-time)=SR 1 (8, 10), SR 2 (9, 10), SR 3 (6,20) We have two processors and need to schedule the given SRs. The LLF strategy is not sufficient to handle this type of scenario.…”

Section: Time-partitioned Based Approach Versus Greedy Approachmentioning

confidence: 99%

“…Due to the wide variety of performance demands from the users, cloud service providers often need to be very flexible in terms of service delivery. Hence, the primary focus of recent cloud computing architecture is to exploit the heterogeneous processing architecture in order to achieve higher computing performances [6], [7]. In recent past, existing scheduling strategies for real-time service requests on cloud mainly focused on the optimization of computation time to meet the deadlines and to enhance the throughput [8].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Majumder

Saha

Chakrabarti

et al. 2022

IEEE Trans. Sustain. Comput.

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Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called "Efficient Resource Allocation of Service Request" (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90% resource utilization with only 26% SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17% reduction in SR rejection rate and 21% reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.

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“…Even as excellent as Google, it is getting harder and harder to further reduce the PUE value. As after a certain degree, the interaction between refrigeration and electrical systems and complex feedback loops make it difficult to accurately use conventional engineering formulas to derive and optimise DC efficiency [4, 5]. Thus, developing innovative methods or strategies for reducing PUE, that is minimising EC and operational cost eventually, deserves our endeavours.…”

Section: Introductionmentioning

confidence: 99%

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Yang

2020

IET Communications

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Investigating Energy Consumption and Performance Trade-Off for Interactive Cloud Application

Cited by 27 publications

References 37 publications

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Power consumption model based on feature selection and deep learning in cloud computing scenarios

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