Verifying dynamic power management schemes using statistical model checking

Kumar, Jayanand Asok; Vasudevan, Shobha

doi:10.1109/aspdac.2012.6165023

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Currently, various approaches have been proposed to optimize and manage energy consumption in cloud data centers. Some approaches have investigated techniques such as processor speed controls [11], voltage adjustments [12], service migration [13], switching on servers, and hibernation or sleep modes [14]. Among these techniques, the popular scheme of switching idle servers to sleep mode or powering them down was studied by many researchers and helped provide good improvements in the energy efficiency and power consumption within data centers.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

Chunxia

Liu

Jin

2020

Mathematical Problems in Engineering

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In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

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Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

Chunxia

Liu

Jin

2020

Mathematical Problems in Engineering

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“…Nowadays, various approaches have been introduced to optimize and manage the electric energy cost of virtualized data centers. Some methods have studied techniques such as processor speed control, 11 voltage adjustment, 12 service migration, 13 shutdown of the server, and sleep modes 14 . Among these technologies, many researchers have studied popular schemes for shutting idle physical servers down or turning these servers to sleep mode, which helps improve energy utilization and power consumption in virtualized data centers.…”

Section: Introductionmentioning

confidence: 99%

A virtualized data center energy‐saving mechanism based on switching operating mode of physical servers and reserving virtual machines

Yin

Liu

Jin

2020

Concurrency and Computation

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The energy consumption of virtualized data centers has grown very fast in last several years. Because a large number of hosts are running in an idle state, virtualized data centers waste a large amount of electric energy. To save more energy for virtualized data centers, an energy-saving mechanism is proposed based on switching operating mode of physical servers and reserving virtual machines (VMs). The main idea is that when the amount of idle VMs reaches twice of a specified threshold, half of these idle VMs are reserved to process the new task that is about to arrive, and the physical server which hosts the other half of the idle VMs is switched to the sleep mode. From the perspective of task arrival rate and sleep parameters, we use two-dimensional Markov processes to analyze the proposed energy-saving mechanisms. By using matrix geometry solutions, we theoretically estimate energy consumption and response performance. According to the numerical experiments, the proposed energy-saving mechanism obviously cut down electric energy consumption and ensures response performance. Finally, the specified threshold for the amount of VMs is optimized by building a cost function. K E Y W O R D Scost function, energy-saving mechanism, Markov chain, virtualized data center INTRODUCTIONCloud data center is a paradigm of providing network, storage, and computing functions as a service for users. 1 The cloud computing has experienced rapid growth, which results in increased size and amount of virtualized data centers. 2 The number of hosting servers in data centers is growing rapidly. Google and Microsoft have more than 1 million servers in their data centers. 3 According to estimates by International Data Corporation, the total number of virtualized data centers deployed worldwide in 2017 reached 8.6 million. 4 As a result, the global electric power consumption of these large data centers has rapidly increased. In 2010, the electric power consumption of all of data centers accounted for roughly 1.3% of that of the world.5 The total electric power consumption of all of the data centers is increasing dramatically every year. Recently, according to the 2017 report of the Forbes Technical Committee, the electric power consumption of all of data centers accounted for roughly 3% of that of the world.6 In addition to the huge cost of electricity, the energy use of virtualized data centers brings many environmental issues. As a result, energy efficiency has become a major concern for data center operators today, even more important than traditional availability and security considerations. 7 One of the main reasons for low energy efficiency is the wasted power supply when data center servers provide computing and storage functions at low utilization rates. 8 The task scheduling algorithms have a significant impact on the energy consumption of these large data centers. 3 Green cloud computing has become a trend in the development of current data centers. It is very important to solve the energy efficient resource scheduling problems...

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Formal Quantification of the Register Vulnerabilities to Soft Error in RTL Control Paths

2015

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Verifying dynamic power management schemes using statistical model checking

Cited by 7 publications

References 12 publications

An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

A virtualized data center energy‐saving mechanism based on switching operating mode of physical servers and reserving virtual machines

Formal Quantification of the Register Vulnerabilities to Soft Error in RTL Control Paths

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