A virtual data center deployment model based on the green cloud computing

Lijun Xu1,2 Chunlin Li1 Layuan Li3 Yanpei Liu1 Zhiyong Yang1 Yunchang Liu1

1School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China 2 School of Computer and Information Engineering,Xinxiang University, Xinxiang, China

3 Wuhan Bioengineering Institute, Wuhan, China E-mail:xljkeyan@126.com

Abstract—Energy consumption is the main obstacle to the

green cloud computing, particularly with the global climate warming and data center scale expanding. The green computing is gaining more and more attention because energy consumption increased rapidly. We propose a cloud computing management framework in order to ensure energy consumption of cloud computing to be a minimum. The framework uses virtual date center instead of virtual machine service mode of traditional service providers, and partition the virtual data center used in the management framework is based on the network communications of virtual machines. Then the virtual data center partition is placed into corresponding green data center in order to maximize revenue of providers and minimize the carbon emissions.

Key words—green computing; cloud computing; Virtual Data Center; Energy Efficiency

I. INTRODUCTION

Cloud computing is the current popular new technology. With large data storage capacity, fast growth of the rapid development of emerging applications, system maintenance costs significantly higher than the hardware update and business value has increased. In order to solve such problem, Google, Amazon and other companies in 2006 put forward the idea of "cloud computing". Subsequently, cloud computing technology is emerging which is adopted by major IT giants. All the IT companies have agreed that the computer virtualization technology is the development trend of cloud computing and it has bright business prospects. The father of the grid, America senior scientist professor LanFoster[1] thinks "cloud computing is dragging economies of scale for external users on the Internet to provide a set of abstract, virtualization, dynamic scalable, manageable computing resources, storage capacity, a kind of large-scale distributed computing platform and services polymers". U.S. National Institute of Standards and Technology currently gives a more comprehensive definition of cloud computing[2] ： Cloud computing is capable of a convenient, on-demand, and the way of network, access to a shared pool of computer resources that can be configured (including network, servers, storage, applications and services, etc.), and the shared resource pool can be minimized by administrative overhead or minimize interaction with the provider, you can quickly configure, provide or release resources. Such a cloud computing model

not only enhance the utilization of resources, but also has five basic characteristics: on-demand self-service, ubiquitous network access, rapid elasticity capacity, measured service, shared resource pool; Three kinds of delivery: software as a service SaaS (Software as aService) - provides software to Internet users through a browser mode, Platform as a Service PaaS (Platform as aService) - the development environment as a service to users, Infrastructure as a Service IaaS (Infrastructure as aService) - The supplier of infrastructure as a service to users; Four kinds of deployment models: private cloud, community cloud, public cloud, hybrid cloud.

In recent years, domestic and foreign Internet companies have built their own data centers to provide a variety of cloud storage or computing services. Along with the expansion of the data center, the consumption of energy and emissions of greenhouse gases is also increasingly prominent. According to statistics[3, 4] , Energy consumption of data center has become a maximum cost for operation and maintenance. Energy issues become increasingly prominent in a serious impediment to the development and popularity of cloud computing technology. This contradiction led to the development direction and the fulcrum of the IT industry technology. Many businesses realize that the energy-saving technologies of data center have many advantages: First, reducing carbon emissions, environmentally friendly. Second, cutting energy costs, and achieving good economic results. Third, getting rid of excessive dependence on cooling facilities, which will help maintain the stability of the system. Therefore, many manufacturers are actively research green energy-saving technology, in order to quickly grab the commanding heights in the field of energy saving technology to extract the maximization of self-interest.

In order to realize the green cloud computing and realize the sustainable development of cloud computing, cloud computing environments need to determine the overall situation of energy consumption. More importantly, you need to understand how to minimize the energy consumption of the data center, efficiently realizing the balance between energy and performance. And these problems are currently very few research cloud computing efficiency[5-7]. This paper proposes a virtual data center management framework with a purpose of reducing carbon emissions to increase the profits of cloud computing infrastructure suppliers and optimize infrastructure providers external environment.

978-1-4799-4860-4/14/$31.00 copyright 2014 IEEE ICIS 2014, June 4-6, 2014, Taiyuan, China

The rest of the paper is organized as follows: In Section 2, we present works relevant to ours. We then describe the proposed management framework in Section 3. In section 4, we provide a VDC placement mathematical model. Section 5 we conclude the paper and present future works.

II. RELATED WORK Aiming at some of the limitations of the data center virtual

machine. Recent research proposals have advocated to offer Virtual Data Center (VDC) instead of VMs[8]. SecondNet is a data center network virtualization architecture that defines a virtual data center as an abstraction for resource allocation in data center environments. Guo et al[9] proposed a data center network virtualization architecture called SecondNet that incorporates a greedy algorithm to allocate resources to VDCs. The VDC embedding problem also shares many similarities with traditional virtual network(VN) embedding[10]. Houidietal[11] proposed a centralized approach where the SP first splits the request using Max-FlowMin-Cut based on prices offered by different InPs and then decides where to place the partitions. Similar to virtualization of wide area networks[12], two main entities cooperate with each other in virtualized data center networks : infrastructure providers (InPs), which owns the physical data center, and service providers (SPs), which in turn run applications on virtual data centers (VDC) built on the top of the physical substrate. VN embedding models differs from VDC embedding in that they only consider CPU and network resources, whereas in VDC embedding other resources such as memory and disk also need to be considered[13]. Amokraneetal[14] first divides a VDC request into partitions so that the inter-partition bandwidth demand is minimized and the intra-partition bandwidth is maximized. The aim of such partitioning is to embed VMs exchanging high volumes of data in the same data center.

III. VIRTUAL DATA CENTER MANAGEMENT FRAMEWORK Workloads in the cloud computing environment is dynamic

and continuously changing. In addition, the foundation of cloud services provider data center will also works along with the equipment, electricity, environmental factors such as climate, and change constantly in order to reduce human interaction, which needs automatic cloud management technology. This paper proposes a virtual data center automatic management framework which is shown in figure 1, mainly considering how to make use of a cloud infrastructure service providers distribution in different parts of the multiple data centers to earn more profit, to reduce operating costs and to reduce carbon emissions, so as to build a friendly external environment. Cloud infrastructure service providers provide services through distribution in different location of data centers, and each data center can use a kind of renewable energy (wind, solar, etc.). Of course, data centers have to be able to use the power grid electricity, because the weather or other causes of renewable energy can not be used when the data center can use the dynamic power operating normally. However, the use of data center power electricity will generate carbon emissions, improve data center operating costs, and improve unfavorable external environment.

When cloud infrastructure service providers provide virtual data center services, the distribution in different location data center resources are used up. The goal is to maximize revenue. To this end, we work hard to reduce the link between the virtual machine, reduce the network energy consumption and ensure the quality of service. We put the virtual data center according to the network communication situation split into multiple partitions. Then partition will be deployed into the center of each physical data, a partition must complete deployment to a physical data center.

In this paper the framework of a global monitoring management module periodically receive local data center monitoring module which is sent to the resource usage and Power Usage Effectiveness(PUE), electricity, climate, renewable energy utilization, network usage and deployment failures. And then it sends the information to the virtual data center partition module. In each global cycle, virtual data center partition module, by using the method of simulation analysis of each virtual data center partition the usage of the current and historical data to predict the future, and build a virtual partition data center deployment model. According to the result of prediction and judgment and a multi-objective optimization algorithm to obtain the global optimal solution of the virtual data center deployment, Combining virtual partition data center deployment situation formulate corresponding deployment strategy. Based on the deployment strategy, virtual data center deployment module set partitioning deployment to the physical data center configuration scheme and according to the deployment plan implementation deployment of virtual data center partition.

Each physical data center has a local controller. on the one hand, it constantly collects some information to the local center controller, such as Resource utilization, Power Usage Effectiveness (PUE), electricity, climate, utilization of renewable energy, and network usage sending a message to the center controller. On the other hand, it also accepts information from the deployment of the central controller, and then it put the virtual data center deployment to the data center. If the local physical data center cannot satisfy the virtual data center deployment problem such as power, resources, the local controller also has to send center controller deployment failure information, and the Center controller will choose other physical data center, and deploy partition of virtual data center.

IV. THE PARTITION DEPLOYMENT MODEL OF VIRTUAL DATA

CENTER

We assume that in a short period of time the state of the data center is stable, and the test parameters in the process of the whole place is constant, no longer considering time factor in the problem description below. We placed the virtual data center partition problem described as an integer linear programming problem. When there is a virtual data center need, the cloud infrastructure service providers place your demand in the distribution of the data center to maximize the profits.

max R C N− − (1) In equation 1, R said revenue, C said data center placement fees, N said the cost of virtual connection network place. We define the infrastructure service supplier to graph G（V，E） , Graph with n vertices V represents the data center, while E represents a physical connection to the network, and the edge weights are expressed bandwidth and network latency. A virtual data center is defined as graph ( , )G V E′ ′ ′ , For any vertex v corresponds to a virtual machine (Includes cpu, memory and hard drive

capacity and other information), For any e E′ ′∈ said virtual connection between virtual machines, ( )B e′ indicates the required bandwidth for the virtual connection, ( )D e′ represents a delay tolerance of the virtual connection. Each virtual data center revenues are associated with it to get the cpu, memory, disk and bandwidth related(Represented by a resource r ) Is:

Fig.1. Virtual data center management framework

1

( ) ( )x x

mr r

rv V e E

R O v B eα β= ′∈ ∈

′= × + ×∑∑ ∑ （2）

Here ( )rO v represents a certain amount of resources to

the virtual machine , rα represents an prices of such resources, β represents the price per unit bandwidth. To ensure that the formula (2) the maximum value, We still have to restrain condition:

(0,1), 1, ,i i xk k

v Vy y k V i V

∈

∈ = ∀ ∈ ∀ ∈∑ （3）

In equation 3, iky is equal to 1 indicates that the virtual

machine of virtual data center is placed data center i , otherwise it is equal to 0.

, ,(0,1), ( ) ( ),e e e ee E

x x B e SB e e E′ ′′′ ′∈

′∈ × ≤ ∀ ∈∑ （4）

In equation 3, ,e ex ′ value is equal to 1 indicates a virtual

connection e E′ ′∈ used the network connection e E∈ , otherwise the value is equal to zero, ( )SB e is the remaining bandwidth network connection.

, ( ) ( ),e ee Ex D e D e e E′

∈

′ ′ ′× ≤ ∀ ∈∑ （5）

The delay of the network connection shall meet the requirements of virtual connection network latency. Virtual data center is divided into multiple partitions by Center controller. Each partition is placed into data centre i, denoted

by ( , )i i iG V E′ ′ ′ , among them

{ }{ }

1 ,

( ) ( )

ii k

i i i

V k V y

E e E B e V and D e V

′ ′= ∈ =

′ ′ ′′′ ′ ′ ′= ∈ ∈ ∈ (6)

Next, we use the energy consumption and carbon emissions to place consumption calculation, by C

represented. We first calculate the virtual partition iG ′ placed

in the data center i consumption, denoted by iP′.

, ,i i g i pP P P′ ′ ′= + (7)

Wherein ,i gP′ represents use of local renewable energy

power, ,i pP′ represents the power grid is used, and ,i gP′ currently have less than the rest of the data center i for renewable energy electricity, using the U.S. dollar as the cost of placing the partition units.

, , ( )i i g i i p i iC P Pη μ λ′ ′ ′= × + × + （8）

Wherein iη represents the use of renewable energy upfront investment, maintenance and management costs prices.

iμ represents the purchase price of electricity power, in units

of ($/kWh) , iλ represents the price of treated carbon emissions, the unit ($ / kWh). the entire virtual data center Placement fee: i

i VC C

∈

′ ′=∑

Virtual connection network placement fees:

, ( )e e pe E e E

N x B e n′′ ′∈ ∈

′ ′= × ×∑∑ (9)

Among them, pn represents the cost of per unit of consumption of the network.

Cloud computing infrastructure services provider of income can be represented as in the end:

1

, , ,

( ( ) ( ) )

( ( )) ( ( ) )

x x

mr r

rv V e E

i g i i p i i e e pi V e E e E

I O v B e

P P x B e n

α β

η μ λ= ′∈ ∈

′′ ′∈ ∈ ∈

′= × + × −

′ ′ ′× + × + − × ×

∑∑ ∑

∑ ∑∑

We have found that the above formula to maximize income, and infrastructure service providers will be reasonable partition of the virtual data center, and will be placed into the appropriate data center. We can put this virtual data center partition placement problem as a packing problem, namely, to find the optimal partition the virtual data center to data center solutions, so that each virtual machine data center resources used does not exceed the data center ceiling, and it is ensured to use the renewable energy as much as possible, which will make it possible to optimize the external environment.

V. CONCLUSIONS

With the continuous application of cloud computing in business, many IT manufacturers at home and abroad have set up distribution in different location of data centers to provide users with a variety of cloud computing and cloud storage service. On the one hand, with the extension of data center,

energy problem has become on with the data center operations and maintenance, which seriously hinders the development of cloud computing technology. On the other hand, cloud computing infrastructure service providers rarely consider the energy consumption and greenhouse gas emissions in order to strive for more profits. In this paper, from the perspective of virtual data center, through the establishment of the virtual data center management framework, the virtual data center based on network communication is divided into several partitions. On the analysis of the data center energy consumption and renewable energy use, we try to place the partition to the relatively green data center, and achieve the infrastructure service providers benefit maximization and reducing carbon emissions by a balance, and making the infrastructure service providers have a green external environment.

In future work, we plan to design an algorithm to divide the virtual data center into a better partition, making the infrastructure service provider to maximize interests, reducing carbon emissions, and providing a green space. In addition, in the real cloud computing environment, there are lots of factors affecting the energy consumption and carbon emissions. So more precise management of cloud computing model is one of our future research work.

ACKNOWLEDGEMENTS

The work is supported by the National Natural Science Foundation (NSF) under grants (No.61171075), Special Fund for Fast Sharing of Science Paper in Net Era by CSTD (FSSP) No.2013014311021 ， Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20120143110014, Program for the High-end Talents of Hubei Province, and the Open Fund of the State Key Laboratory of Software Development Environment (SKLSDE-2013KF),Natural Science Foundation of Hubei Province (No: 2011CDB297) and Teaching Research Project of Hubei Province (No: (2011)32). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

REFERENCES [1] Foster I, Zhao Y, Raicu I, et al. Cloud computing and grid computing

360-degree compared[C]//Grid Computing Environments Workshop, 2008. GCE'08. Ieee, 2008: 1-10.

[2] Mell P, Grance T. The NIST definition of cloud computing (draft)[J]. NIST special publication, 2011, 800(145): 7.

[3] Yun D, Lee J, Yi Y. Research in green network for future internet[J]. Journal of KIISE, 2010, 28(1): 41-51.

[4] Andrew L L H, Lin M, Wierman A. Optimality, fairness, and robustness in speed scaling designs[C]//ACM SIGMETRICS Performance Evaluation Review. ACM, 2010, 38(1): 37-48.

[5] Talebi M, Way T. Methods, metrics and motivation for a green computer science program[C]//ACM SIGCSE Bulletin. ACM, 2009, 41(1): 362-366.

[6] Baliga J, Ayre R W A, Hinton K, et al. Green cloud computing: Balancing energy in processing, storage, and transport[J]. Proceedings of the IEEE, 2011, 99(1): 149-167.

[7] Buyya R, Beloglazov A, Abawajy J. Energy-efficient management of data center resources for cloud computing: A vision, architectural

elements, and open challenges[J]. arXiv preprint arXiv:1006.0308, 2010.

[8] Bari M, Boutaba R, Esteves R, et al. Data center network virtualization: A survey[J]. 2012.

[9] Guo C, Lu G, Wang H J, et al. Secondnet: a data center network virtualization architecture with bandwidth guarantees[C]//Proceedings of the 6th International COnference. ACM, 2010: 15.

[10] Chowdhury N M, Boutaba R. A survey of network virtualization[J]. Computer Networks, 2010, 54(5): 862-876.

[11] Houidi I, Louati W, Ben Ameur W, et al. Virtual network provisioning across multiple substrate networks[J]. Computer Networks, 2011, 55(4): 1011-1023.

[12] Chowdhury N M M K, Boutaba R. Network virtualization: state of the art and research challenges[J]. Communications Magazine, IEEE, 2009, 47(7): 20-26.

[13] Zhani M F, Zhang Q, Simon G, et al. Dynamic migration-aware virtual data center embedding for clouds[C]//Proceedings of the 13th IFIP/IEEE Integrated Network Management Symposium (IM 2013). Gent, Belgien. 2013, 92.

[14] Amokrane A, Zhani M, Langar R, et al. Greenhead: Virtual Data Center Embedding Across Distributed Infrastructures[J].