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«DYNAMIC ENERGY MANAGEMENT IN CLOUD DATA CENTERS: A SURVEY T.Veni1 and S. Mary Saira Bhanu2 Department of Computer Science and Engineering National ...»

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International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013

DYNAMIC ENERGY MANAGEMENT IN CLOUD DATA

CENTERS: A SURVEY

T.Veni1 and S. Mary Saira Bhanu2

Department of Computer Science and Engineering

National Institute of Technology Tiruchirappalli

Tiruchirappalli-620015, India

{406111001, msb}@nitt.edu

ABSTRACT

Cloud data centers have become indispensable infrastructure for computing and data storage that facilitate the development of diversified services offered by the cloud. These data centers consume enormous amounts of electrical energy to process the cloud services resulting in large amount of CO2 emissions, high operational cost, and affecting the lifetime of hardware equipments. This necessitates the development of efficient energy management techniques in cloud data centers for both economic and environmental standpoints. Energy management at data centers can be static or dynamic. Energy consumption can be reduced by employing dynamic energy management techniques both in hardware and software levels at cloud data centers. This paper surveys various issues related to dynamic energy management in cloud data centers.

KEYWORDS

Cloud Data Center, Virtualization, Dynamic Energy Management, & Server Consolidation

1. INTRODUCTION Data Centers have emerged as a back-bone infrastructure, housing large number of IT equipments such as servers, data storage, network devices, power and cooling devices etc. that facilitate the development of wide variety of services offered by the cloud [1] Currently, several service providers such as Amazon, Google, Yahoo, Microsoft, IBM and Sun, have their own data centers to provide the scalable services to a large customer base [2-3].With the rapid development of IT industry and increasing demand for cloud services, the number of data centers have increased.

These data centers consume enormous amount of energy to process its services resulting in increased energy consumption. The surging energy consumption of these data centers has become a serious concern from both economic and environmental standpoints.

According to McKinsey report [4], the energy consumption of data centers is $11.5 billion in 2010 and it doubles every five years. Gartner [5] also estimated that world wide IT infrastructures are responsible for 2% of global CO2 emissions and energy related costs account for the 12% of the total economical expenditures. The excessive energy consumption at data centers leads to high operational cost, large amount of CO2 emission and falling lifetime of hardware equipments.

DOI : 10.5121/ijccsa.2013.3402 13 International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013 Hence, it is necessary to design energy-efficient data centers not only for ensuring system reliability but also reducing environmental impact and operational cost.

Energy management techniques at the data centers can be static or dynamic. The static energy management techniques fail to address the run time adaptation of data centers in response to workload changes. The dynamic energy management techniques configure the data center at both hardware and software levels dynamically based on workload variability.

Further, the energy conservation can be achieved by efficient utilization of data center resources.

Virtualization technology is one such powerful technology to address this energy inefficiency by increasing resource utilization [6].This technology allows multiple virtual machines (VMs) to share the resources on a single physical machine (PM). The features such as VM isolation and VM migration along with dynamic resource provisioning can be used either to consolidate virtual machines on fewer physical servers or to balance the load across physical servers in data centers, thereby ensuring applications’ performance. This paper surveys the various techniques and issues related to dynamic energy management in cloud data centers.

The rest of this paper is organized as follows. Section 2 explains taxonomy of energy management mechanisms in virtualized data centers; section 3 presents an overview of dynamic energy management techniques in cloud data centers and finally, section 4 presents the conclusion.

2. TAXONOMY OF ENERGY MANAGEMENT TECHNIQUES IN CLOUD DATA

CENTERS

It is necessary to understand the background terminologies involved in the context of energy management techniques. The energy consumption (E) at cloud data center is defined as a total amount of power (P) consumed over a period of time (T) while performing the work [7], i.e.,

E=P*T (1)

Thus, the energy conservation at the cloud data centers can be achieved by controlling average power consumption (P) over the period of time. As per taxonomy [7], the energy management approaches in cloud data center can be classified into static and dynamic energy management techniques as shown in Fig.1.

International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013 Fig.1. Taxonomy of Energy Management Techniques in Cloud Data Centers Static Energy Management (SEM) technique [8] uses low-power hardware components at the data centers for energy savings. The request for the cloud services running in the data center is volatile in nature. This technique fails to address the runtime adaptation of data centers in response to the service demand to avoid resource wastage and it is also a very expensive technique for energy conservation.





3. OVERVIEW OF DYNAMIC ENERGY MANAGEMENT TECHNIQUES

Dynamic energy management (DEM) techniques dynamically reconfigure the system based on current resource requirements of the requested services. It utilizes the power scalable hardware components and software approaches to optimize the energy consumption at data centers.DEM techniques are classified into hardware and virtualization assisted techniques based on the level of their applicability.

3.1. Hardware Level Energy Management Techniques The energy management techniques need to be applied on each hardware components such as the processors, network equipments, and storage devices to reduce overall hardware level energy consumption at cloud data centers.

3.1.1. Processor Level Energy consumption of a processor consists of two components such as static power and dynamic power depends on the usage of various resources at server [7]. The dynamic power consumption of a CMOS-based processor is given by

–  –  –

Where A is the percentage of active gates, F is clock frequency of the processor, C is capacitance load of the processor, and V is the voltage supplied. The techniques such as Dynamic Voltage and Frequency Scaling (DVFS), Network Level Clock Gating and Supply Shutdown are used for power management in processor level.

International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013 3.1.2. Network Level As the size of the data center and network infrastructure is exploding, it is necessary to address the energy consumed by the network devices such as routers, switches, NIC, etc. Switches form the basis of interconnection fabric that delivers job requests to the computing servers for execution. The energy consumption of a switch depends on the type of switch, number of ports, port transmission rates and employed caching solutions. The energy consumed by a switch can be generated by the following equation;P Switch = P Chassis + N Line cards * P Line card + ∑Ri=0 N Ports * Pr (3) Where P Chassis is the power consumed by the switch base hardware, P Line card is the power consumed by the active line card and P r is the power consumed by the active ports. The techniques such as Link State Adaptation (LSA), Idle Elements Shutdown are used for power savings in network level.

3.1.3. Storage Level The storage devices constitute a significant fraction of the overall energy budget. The storage level energy management techniques [38] can be divided as follows: Hardware based techniques increase the disk power conservation by maintaining storage hierarchy to strike the right balance between performance and power consumed by storage resources  Disk management techniques introduce the new disk management layer on top of the file system, which controls disk configuration and data layout to achieve power optimal disk access patterns  Caching techniques reduce the power consumption at storage level by allowing large fractions of the storage system to remain idle for longer periods of time and switch to lower power modes International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013 3.1.4. Summary of Hardware Level Energy Management Techniques

–  –  –

International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013 For efficient energy conservation, the hardware energy management techniques should consider all resources such as Processor, Network and Storage etc. The above mentioned hardware techniques are applicable at individual server level for energy conservation. Hence, these hardware level energy management techniques do not contribute much in total energy conservation at cloud data centers. The energy consumption can be further reduced by applying energy management techniques at software (virtualization level) at cloud data centers.

3.2. Virtualization Level Energy Management Techniques

The energy consumption problem has been addressed partially by making improvements in the physical infrastructure of modern data centers. According to Open Compute project report [33], 93% of the data center’s energy consumption depends upon efficient utilization of computing resources at data centers. Virtualization is a key technology that facilitates the better use of available data center resources using the technique called server consolidation [13]. This technique involves consolidating multiple physical server workloads into single physical server to increase the resource utilization. Thus, it allows for reduction in the total number of physical server used, minimizes the server sprawl as well as total data center space requirements. The server consolidation can be performed either statistically or dynamically.

 In static consolidation, VMS are placed on physical servers for a long time period and not migrated even if workload changes.

 In dynamic consolidation, VMs are placed on physical servers at runtime and migrations of VMs performed automatically in response to the current workload demands. This helps in utilizing the data centers resources efficiently.

The steps for server consolidation (Fig.2.) are as follows:

 Determining the capacities of VM (VM sizing) for running applications and placing of VMs on PM based on their requirements  Monitoring and profiling the resource utilization for hotspot detection (overloaded and under loaded PMs)  Resize and remap the VM to another PM.

The VM placement and VM migration act as backbone to VM consolidation process. The challenges for efficient VM consolidation are: Finding the proper VM- to-PM mappings to minimize the acceptable cost function  Dynamic hotspot detection  Performing VM migration process with minimal service downtime and resource consumption during migration process International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.3, No.4, August 2013

–  –  –

3.2.1. Virtual Machine Placement VM placement [14] is the process of mapping VMs to PMs. The placement approach should consider multiple resources such as CPU, memory, disk storages and network bandwidth to reduce the energy consumption at data centers and also needs to maintain the energy performance tradeoff.

The VM placement involves two main steps:

 Provisioning of resources for the virtual machines according to the capacity requirements of corresponding applications (VM sizing)  Actual placement of VMs onto PMs.

VM placement problem is a NP –Hard problem [34] and no optimal solution exists for it. A set of heuristics is used to solve VM placement problem.



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