January / March 2015

 

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Cloud-Link: Special Issue on Green Cloud Computing

In recent years, there has been considerable interest in green technologies for environmental protection and sustainable development (for example, to reduce greenhouse gas emissions). This issue of Cloud-Link is about green cloud computing. Nine articles have been selected to cover different aspects of this important topic.

As data centers provide the core infrastructure for supporting cloud computing and they consume much energy, there is a need to study green technologies for data centers.  The article “A Stochastic Approach to Analysis of Energy-Aware DVS-Enabled Cloud Datacenters” proposes to reduce energy consumption by controlling supply voltage and operating frequency based on a stochastic model. The article “Harnessing Renewable Energy in Cloud Datacenters: Opportunities and Challenges” studies the use of renewable energy to support data center operation. The article “Low-Emissions Routing for Cloud Computing in IP-over-WDM Networks with Data Centers” investigates how to combine routing and renewable energy to enhance the energy efficiency of data centers. With the popularity of mobile phones, there is also a need to study green solutions for mobile cloud computing. The article “A Model-Assisted Cross-Layer Design of an Energy-Efficient Mobile Video Cloud” studies a cross-layer and energy efficient protocol to support mobile video cloud applications. The article “Toward Transcoding as a Service: Energy-Efficient Offloading Policy for Green Mobile Cloud” investigates an offloading policy to support mobile cloud computing in an energy efficient manner. The article “Cloud Gaming: A Green Solution to Massive Multiplayer Online Games” specifically studies green cloud gaming. One way to allocate cloud resources for enhancing energy efficiency and satisfying various constraints is by formulating a bin-packing problem. Both the articles “Adaptive Resource Provisioning for the Cloud Using Online Bin Packing” and “Automatic Scaling of Internet Applications for Cloud Computing Services” study bin-packing problems for supporting green cloud computing. Last but not least, the article “Downlink and Uplink Energy Minimization through User Association and Beamforming in C-RAN” studies energy efficient techniques for enhancing the recently proposed cloud-based radio access network.

We hope that this issue of Cloud-Link can provide you with useful references to explore this important and interesting topic further. Articles have been selected based on various considerations (for example, variety, relevancy, anticipated reader interest) and unavoidably there are many other useful and insightful articles that have not been included. You are also encouraged to search through IEEE Xplore and other databases for further reading.

We are looking for topics for upcoming issues. If you have any suggestions, please email them to hcbchan@ieee.org.

Henry Chan, Victor Leung, Jens Jensen, and Tomasz Wiktorski
Editor and Associate Editors

Articles in this issue

A Stochastic Approach to Analysis of Energy-Aware DVS-Enabled Cloud Datacenters

Yunni Xia, MengChu Zhou, Xin Luo, ShanChen Pang, and Qingsheng Zhu

Published in IEEE Transactions on Systems, Man, and Cybernetics, January 2015

With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. This article presents a novel stochastic framework for energy efficiency and performance analysis of DVS-enabled cloud. This framework uses virtual machine request arrival rate, failure rate, repair rate, and service rate of datacenter servers as model inputs. Based on a queuing-network-based analysis, this article gives analytic solutions of three metrics. The proposed framework can be used to help the design and optimization of energy-aware high performance cloud systems.

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Harnessing Renewable Energy in Cloud Datacenters: Opportunities and Challenges

Wei Deng, Fangming Liu, Hai Jin, Bo Li, and Dan Li

Published in IEEE Network, January/February 2014

The proliferation of cloud computing has promoted the wide deployment of large-scale datacenters with tremendous power consumption and high carbon emission. To reduce power costs and the carbon footprint, an increasing number of cloud service providers have considered green datacenters with renewable energy sources, such as solar or wind. However, unlike the stable supply of grid energy, it is challenging to utilize and realize renewable energy due to the uncertain, intermittent and variable nature. In this article, the authors provide a taxonomy of the state-of-the-art research in applying renewable energy in cloud computing datacenters from five key aspects, including generation models and prediction methods of renewable energy, capacity planning of green datacenters, intra-datacenter workload scheduling, and load balancing across geographically distributed datacenters. By exploring new research challenges involved in managing the use of renewable energy in datacenters, this article attempts to address why, when, where, and how to leverage renewable energy in datacenters, also with a focus on future research avenues.

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Low-Emissions Routing for Cloud Computing in IP-over-WDM Networks with Data Centers

M. Gattulli, M. Tornatore, R. Fiandra, and A. Pattavina

Published in IEEE Journal on Selected Areas in Communications, January 2014

Cloud computing (CC) services are rapidly catching on as an alternative to conventional office-based computing. As cloud computing adoption increases, the energy consumption of the network and of the computing resources that underpin the cloud is growing and causing the emission of enormous quantities of CO2. Research is now focusing on novel “low-carbon” cloud-computing solutions. Renewable energy sources are emerging as a promising solution both to achieve drastic reduction in CO2 emissions and to cope with the growing power requirements of data centers. These infrastructures can be located near renewable energy plants and data can be effectively transferred to these locations via reconfigurable optical networks, based on the principle that data can be moved more efficiently than electricity. This article focuses on how to dynamically route on-demand optical circuits that are established to transfer energy-intensive data processing toward data centers powered with renewable energy. The authors’ main contribution consists in devising two routing algorithms for connections supporting CC services, aimed at minimizing the CO2 emissions of data centers by following the current availability of renewable energies (for example, coming from sun and wind). The trade-off with energy consumption for the transport equipment is considered. The authors also compare three different IP-over-WDM network architectures. The results show that relevant reductions, up to about 30% in CO2 emissions can be achieved using the presented approaches compared to baseline shortest-path-based routing strategies, paying off only a marginal increase in terms of network blocking probability.

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A Model-Assisted Cross-Layer Design of an Energy-Efficient Mobile Video Cloud

A. Lombardo, C. Panarello, and G. Schembra

Published in IEEE Transactions on Multimedia, December 2014

In the last decade, one of the main goals in wireless telecommunications has been to reduce energy consumption of mobile devices. However, making a network device green can cause performance deterioration. The target of this article is to propose a cross-layer approach for the design of a mobile video cloud for the uplink transmission toward the Internet. The proposed approach is adaptive in both the video sources and the wireless transmitter. A source Rate Controller is applied to compensate transmission bandwidth reduction due to the energy saving policies. Energy saving in wireless transmission on the mobile cloud cellular channel is achieved by introducing an energy-efficient ARQ protocol. This protocol can apply different transmission laws, in order to exploit the correlation of the cellular channel behavior. An analytical model of the system is defined to compare the transmission laws, and provide some design guidelines to choose one of them and design its parameters.

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Toward Transcoding as a Service: Energy-Efficient Offloading Policy for Green Mobile Cloud

W. Zhang, Y. Wen, and H.-H. Chen

Published in IEEE Network, November/December 2014

In this article the authors investigate energy-efficient offloading policy for transcoding as a service (TaaS) in a generic mobile cloud system. Computation on mobile devices can be offloaded to a mobile cloud system that consists of a dispatcher at the front end and a set of service engines at the back end. Particularly, a transcoding task can be executed on the mobile device (that is, mobile execution) or offloaded and scheduled by the dispatcher to one of the service engines in the cloud (that is, cloud execution). The authors aim to minimize the energy consumption of transcoding on the mobile device and service engines in the cloud while achieving low delay. For the mobile device, they formulate its offloading policy under delay deadline as a constrained optimization problem. The authors find an operational region on which execution mode, that is, mobile execution or cloud execution, is more energy efficient for the mobile device. For the cloud, they propose an online algorithm to dispatch transcoding tasks to service engines, with an objective to reduce energy consumption while achieving queue stability. By appropriately choosing the control variable, the proposed algorithm outperforms alternative algorithms, with lower time average energy consumption and time average queue length on the service engines. The proposed offloading policy can reduce energy consumption on both mobile devices and the cloud jointly, which provides guidelines for the design of green mobile cloud.

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Cloud Gaming: A Green Solution to Massive Multiplayer Online Games

Seong-Ping Chuah, Chau Yuen, and Ngai-Man Cheung

Published in IEEE Wireless Communications, August 2014

Advanced video gaming is a computationally intensive application. Sophisticated graphics renderings are employed in computer games to produce realistic scenes and smooth actions. As a result, video gaming often requires powerful hardware that is beyond the capability of many mobile devices or even personal computers. Meanwhile, playing a high-quality game while on the move is highly desirable with the growing popularity of high-speed mobile and broadband Internet, and mobile devices such as smartphones and tablets. Instead of equipping mobile devices with powerful but battery-hungry computation engines, cloud gaming, which utilizes cloud computing for gaming, offers an emerging green solution to bring the high-quality immersive gaming experience to thin or mobile clients. Cloud gaming leverages communication infrastructures to shift heavy computation to cloud servers. In this article, the authors provide an overview of cloud gaming from a green media perspective (in addition to the conventional energy perspective). The authors argue that cloud gaming can lead to less software maintenance, more economical scaling, and longer service life spans of hardware equipment. They also briefly present a novel scheme, layered coding, which leverages the increasing graphics processing capability of a mobile client to reduce the bit rate of game streaming. The authors then discuss green designs of major cloud gaming subsystems: a cloud data center, graphics rendering, video compression, and network delivery. They review existing services and a testbed for cloud gaming. They also identify potential research challenges of cloud gaming in achieving green media for the future.

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Adaptive Resource Provisioning for the Cloud Using Online Bin Packing

Weijia Song, Zhen Xiao, Qi Chen, and Haipeng Luo

Published in IEEE Transactions on Computers, November 2014

Data center applications present significant opportunities for multiplexing server resources. Virtualization technology makes it easy to move running application across physical machines. In this article, the authors present an approach that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers actively used. They abstract this as a variant of the relaxed online bin packing problem and develop a practical, efficient algorithm that works well in a real system. They adjust the resources available to each VM both within and across physical servers. Extensive simulation and experimentation results demonstrate that this system achieves good performance compared to the existing work.

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Automatic Scaling of Internet Applications for Cloud Computing Services

Zhen Xiao, Qi Chen, and Haipeng Luo

Published in IEEE Transactions on Computers, May 2014

Many Internet applications can benefit from an automatic scaling property where their resource usage can be scaled up and down automatically by the cloud service provider. The authors present a system that provides automatic scaling for Internet applications in the cloud environment. They encapsulate each application instance inside a virtual machine (VM) and use virtualization technology to provide fault isolation. They model it as the Class Constrained Bin Packing (CCBP) problem where each server  is a bin and each class represents an application. The class constraint reflects the practical limit on the number of applications a server can run simultaneously. The authors develop an efficient semionline color set algorithm that achieves good demand satisfaction ratio and saves energy by reducing the number of servers used when the load is low. Experiment results demonstrate that this system can improve the throughput by 180% over an open source implementation of Amazon EC2 and restore the normal QoS five times as fast during flash crowds. Large-scale simulations demonstrate that the presented algorithm is extremely scalable: the decision time remains under 4 s for a system with 10,000 servers and 10,000 applications. This is an order of magnitude improvement over traditional application placement algorithms in enterprise environments.

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Downlink and Uplink Energy Minimization through User Association and Beamforming in C-RAN

Shixin Luo, Rui Zhang, and Teng Joon Lim

Published in IEEE Transactions on Wireless Communications, January 2015

The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs) to improve mobile data rates, has been recently proposed. However, the high density of active APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, for example, network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this article, the authors propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, that is, group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve the problem. Leveraging on the celebrated UL-DL duality result, the authors show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two interrelated subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.

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