An energy-saving strategy based on multi-server vacation queuing theory in cloud data center

Chunxia, Yin; Jin, Shunfu

doi:10.1007/s11227-018-2513-4

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…The CAN-SEAT strategy is also applicable to the server cluster, and it can be combined with the server's energy-saving approach described below. Yin et al [8] proposed a strategy that switches servers between on and sleep in groups based on multi-server vacation queuing theory. Kuehn et al [9] provided a model to manage the energy consumption of the server via load-dependent server activation and sleep modes.…”

Section: Background and Motivationmentioning

confidence: 99%

Client-Aware Negotiation for Secure and Efficient Data Transmission

Wang

Heng

2020

Energies

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In Wireless Sensor Networks (WSNs), server clusters, and other systems requiring secure transmission, the overhead of data encryption and transmission is often not negligible. Unfortunately, a conflict exists between security and efficiency in processing data. Therefore, this paper proposes a strategy to overcome this conflict, called Client-Aware Negotiation for Secure and Efficient Data Transmission (CAN-SEAT). This strategy allows a client with different security transmission requirements to use the appropriate data security transmission without modifying the client. Two methods are designed for different clients. The first method is based on two-way authentication and renegotiation. After handshakes, the appropriate data security transmission scheme is selected according to the client requirements. Another method is based on redirection, which can be applied when the client does not support two-way authentication or renegotiation. For the characteristics of different architecture, this paper classifies and discusses symmetric key algorithms, asymmetric key algorithms, and hardware encryption instructions. In four application scenarios, the CAN-SEAT strategy is tested. Compared with the general transmission strategy, when only software encryption is used, the data processing and transmission cost can be reduced by 89.41% in the best case and by 15.40% in the worst case. When supporting hardware encryption, the cost can be reduced by 85.30% and 24.63%, respectively. A good effect was produced on the experimental platforms XiLinx, FT-2000+, and Intel processors. To the best of our knowledge, for Client-Aware Negotiation (CAN), this is the first method to be successfully deployed on a general system. CAN-SEAT can be easily combined with other energy-efficient strategies.

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Section: Background and Motivationmentioning

confidence: 99%

Client-Aware Negotiation for Secure and Efficient Data Transmission

Wang

Heng

2020

Energies

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“…Each physical server can host at least 5 VMs and up to 20 VMs. According to a previous study, 2 8 Table 2 shows the parameter set of the numerical experiments.…”

Section: Numerical Experimentsmentioning

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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“…They modeled the system as an M/M/k 3/4 queue. Chunxia and Shunfu proposed an energy‐saving strategy based on the multiserver vacation queuing theory that switches servers between “on” and “sleep” in groups. They modeled the data center as an M/M/c vacation queuing system.…”

Section: Primary Studies and Classification Schemesmentioning

confidence: 99%

Applying queue theory for modeling of cloud computing: A systematic review

Ghomi

Rahmani

Qader

2019

Concurrency and Computation

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The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost-efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority-based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority-based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area. KEYWORDScloud computing, cloud modeling, queuing systems, queuing theory, systematic review INTRODUCTIONCloud computing is the recent evolution in the information technology and is growing very fast due to the extensive use of mobile devices, such as PDAs, cell phones, and tablets. 1 To emphasize the goals of cloud computing, we refer to the definition of NIST (National Institute of Standards and Technology); a cloud model is composed of five characteristics, three service models, and four deployment models. 2 Service models of cloud computing are as follows: (1) Software as a Service (SaaS), such as CRM (Customer Relationship Management), virtual desktop, communications, and games; (2) Platform as a Service (PaaS), such as databases, web servers, and deployment tools; and (3) Infrastructure as a Service (IaaS), such as virtual machines (VMs), storages, servers, and load balancers. Deployment models include private clouds, public clouds, community clouds, and hybrid clouds. The characteristics of a cloud are on-demand self-servicing, elasticity, broad network access, resource pooling, and measured service. 1 A data center in cloud computing consists of several servers that are organized in racks and connected through communicati...

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An energy-saving strategy based on multi-server vacation queuing theory in cloud data center

Cited by 18 publications

References 13 publications

Client-Aware Negotiation for Secure and Efficient Data Transmission

Client-Aware Negotiation for Secure and Efficient Data Transmission

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

Applying queue theory for modeling of cloud computing: A systematic review

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