Energy Saving Approaches for Green Cloud Computing - IEEE Xplore

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Abstract— Cloud Computing is an emerging technology and is being used by more and more IT companies due to its cost saving benefits and ease of use for ...
Proceedings of 2014 RAECS UIET Panjab University Chandigarh, 06 - 08 March, 2014

Energy Saving Approaches for Green Cloud Computing: A Review Bharti Wadhwa

Amandeep Verma

University Institute of Engineering and Technology Chandigarh-160014, India Email: [email protected]

University Institute of Engineering and Technology Chandigarh-160014, India Email: [email protected]

Abstract— Cloud Computing is an emerging technology and is being used by more and more IT companies due to its cost saving benefits and ease of use for users. But, it needs to be environment friendly also. Therefore, Green Cloud Computing is the requirement of the today’s world. This paper reviews the efforts made by various researchers to make Cloud Computing more energy efficient, to reduce the carbon footprint rate by various approaches and also discusses the concept of virtualization and various approaches which use virtual machines scheduling and migration to show how these can help to make the system more energy efficient. The summary of the main features of the proposed work of different authors that we have reviewed is also presented in it. Keywords—Cloud Computing; Energy efficiency; Green Cloud Computing; Virtualization

I. INTRODUCTION Cloud Computing is one of the most popular emerging technologies in today's world. It is a new and promising paradigm which delivers computing as a utility [1]. It provides software, data access, storage services and computation through the Internet and facilitates customers to rent resources based on the pay-as-you-go model. The customers are charged only for as much as they have used. The main advantage of Cloud Computing is that users can get their computing and data storage services on demand without much investment in the computing infrastructure. However, these growing demands of customers for computing services are encouraging the cloud service providers e.g., Google[2], Microsoft[3], Yahoo![4], etc. to deploy increasing amounts of energy hungry data centers [5]. For running these data centers, huge amount of power is required. Energy is required for console, monitors, and network peripherals, cooling fans of processors, light and cooling system and consequently, the energy consumption of information industry is increasing. As mentioned in [6], the total power consumption of the data centers in 2012 was around 38 Giga Watt (GW) and this is around 63% more than the power consumption of 2011. This total power could have been enough for fulfilling the energy requirements of all residential households of United Kingdom.

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According to McKinsey report [7],”The total estimated energy bill for data centers in 2010 is 11.5 billion and energy costs in a typical data center double every five years”. The fact that electricity consumption is set to rise 76% from 2007 to 2030 [8] and data centers are the main contributors of an important portion of this increase, emphasizes the importance of reducing energy consumption in clouds. Therefore energy efficiency is becoming increasingly important for data centers and cloud. Also, the environment is in danger due to the huge amount of carbon emissions from IT Industries. Hence, there is a need to implement more environmentally friendly computing called “Green Cloud Computing”. Green Computing is defined as the environmentally sustainable computing. It refers to the attempts to maximize the use of power consumption and energy efficiency and to minimize the cost and CO2 emission [9]. The main purpose of green computing is to investigate new computer systems, computing model and applications with the low cost and low power consumption and promote the sustainable development of economy and society [10] [11]. The amount of energy consumption and carbon dioxide emission in one Google search is presented below in Fig. 1 [12] and the amount of carbon dioxide emission and energy consumption in one month is shown in the Fig. 2. This large amount of carbon dioxide being emitted is increasing green house effect and warning us to make IT industry environmental friendly. There is a need to take quick steps in order to minimize this large amount of carbon dioxide emission. The research community has provided a range of software and hardware solutions to the problem of achieving energy efficiency and minimizing the carbon dioxide emission in the cloud operation. Some software solutions propose to use the technique called virtualization in the clouds. Virtualization can be used to get better resource isolation and reduce infrastructure energy consumption through resource consolidation and live migration [13]. The concept of virtualization is discussed in detail in section III.

Fig. 1 Energy consumption in one Google search [12]

Fig. 2 Energy consumption in monthly Google search [12]

The remainder of this paper is organized as follows. Section II presents a few approaches proposed for improving energy efficiency in clouds. Section III presents the concept of virtualization and approaches that are proposed for improving energy efficiency using virtualization. Section IV gives a summary of all the approaches presented in II and III in the form of a table. Section V concludes the paper.

Scheduling of workloads Another approach presented in [16] is to schedule workload across servers selected as a function of their cost to operate, in order to improve efficiency. The focus was to achieve the maximum utilization in a cost-efficient manner. The approach used queuing theory principles and also the relationship between packet arrival rate, service rate and response time. An exponential relationship between power cost and server utilization was found which was used for selection of the server to achieve maximum efficiency. It was also proposed that optimal efficiency can be achieved by maintaining server configuration with respect to the required utilization for handling the recommended workload.

II. ENERGY SAVING STRATEGIES Energy consumption and performance of the system depend on many factors. Some simple techniques provide basic energy management for servers in Cloud environments, i.e. turning on and off servers, putting them to sleep. Other techniques for saving energy include use of Dynamic Voltage/Frequency Scaling (DVFS) [14] and use of virtualization techniques for better resource utilization. Various researchers have put many efforts to reduce the energy consumption in clouds and data centers. In light of today’s requirements for green computing, we present latest research efforts that attempt to deal with them. Calculating energy consumption by considering single task as a unit In [15], the authors had presented an energy consumption model, associated analysis tool and empirical energy analysis approaches to calculate total energy consumption in Cloud environments based on different runtime tasks. A single task was considered as a unit and energy produced by the task under various configurations was measured. The analysis tool had taken the energy consumption model as input and characterized energy consumed by each task based on the parameters like the number of processes, the size of data to be processed, size of data transmitted and system configuration. It helped to identify the relationship between energy consumption and running tasks in cloud environments, as well as system configuration and performance. The analytical results correlated system performance and energy consumed which can be important for developing energy efficient mechanisms.

Efficient server allocation The authors in [17] had designed an algorithm for efficiently managing the network resources by allocating workload as a function of application traffic volume an speed of server. Efficiency was achieved by minimizing the packet loss and efficiently using the residual server capacity with respect to traffic patterns. It was proposed that by selecting the servers which can process the workloads at speed matching the packet arrival rate, optimization can be achieved. There is a point in server speed, at which optimal performance is achieved which cannot be achieved at speeds lesser or more than that. This is shown in the following graph in Fig. 3.

Fig. 3 Server Selection Strategy as Function of Speed [17]

III. VIRTUALIZATION AND LIVE MIGRATION One of the techniques that are being widely used in cloud environment is virtualization. Virtualization helps in decreasing the hardware and operating cost by virtually running multiple operating systems parallel on a single system. Live migration refers to moving the virtual machines from one physical server to another transparently. The migration of virtual machines is found to be a useful technique for making the systems more energy efficient. The major technique being used in virtual machine migration is Pre-Copy. It has 3 phases: i) Pre-Copy Phase ii) Pre-Copy Termination Phase iii) Stop-and-Copy Phase Migration is done in several rounds as shown in following Fig.4 [18].

Fig. 4 Live Migration algorithm performs memory transfer page wise in several rounds [18].

Fig. 5 Architectural Diagram for Green Cloud Computing [20]

Combination of allocation and migration of VMs The approach presented in [21] also focused on VM scheduling in order to get an energy efficient system. Authors had proposed a combination of two algorithms: allocation algorithm used for allocating the jobs and a migration algorithm for optimal migration of VMs considering minimum migrations and minimum energy consumption. The objective of allocation algorithm is to achieve minimum power consumption and it used bin packing problem approach and it was compared with best fit algorithm. The authors had proposed that by using this approach of combining the allocation algorithm with migration algorithm in a linear integer program, a significant amount of energy can be saved depending upon system loads. The model of system using this approach is presented in Fig. 6.

Allocation of VMs using DVFS and Active Cooling The algorithm proposed in [19] focused on minimizing energy consumption by scheduling virtual machines in the system efficiently. The main parameters that had been focused were DVFS and active cooling. Another features like shutdown of underutilized machines and migrations of workloads from the machines that are operating below a specific threshold were also used. The algorithm was capable of performing the scheduling of VMs in non federated, homogeneous and heterogeneous data centers. The algorithm was able to improve power consumption in clouds significantly when used in heterogeneous datacenters. Automatic Migration using preprocessed data Another energy aware provisioning approach is proposed in [20] which considered energy efficiency as a key factor. For automatic migration of VMs, the authors had proposed a new model with a component called trigger engine which uses preprocessed data for automatic live migration of virtual machines. The whole architecture of the model can be clearly understood by the Fig. 5. The key parameter used is utilization factor for a VM which was derived from no. of clients, memory used and server utilization.

Fig. 6 The System Model [21]

VM allocation and consolidation using Ant Colony Optimization (ACO) The approach proposed in [22] used ACO (Ant Colony Optimization) technique for workload (i.e. VMs)

consolidation. The problem was taken as an instance of Multidimensional Bin Packing Problem (MDBP) and the algorithm was compared with the commonly used greedy algo FFD for bin packing problem and found to achieve better energy savings comparatively. It was shown that it was able to work in a fully distributed environment and required lesser machines. It achieved better server utilization and energy saving. One more approach that uses Ant Colony Optimization (ACO) for energy conservation and allocation of VMs in clouds was proposed in [23]. It used a centralized approach for providing information based on expected behavior of each user i.e. dynamic user requests were considered. It used ACO to minimize energy usage by allowing minimum number of servers in ON state. VM allocation focusing on energy and carbon efficiency VM allocation and migration can improve the energy efficiency of the cloud systems but if the techniques are not used properly, it can lead to high energy usage and consequently high carbon dioxide emission. The authors in [24], had proposed a VM placement algorithmic approach for reducing the power consumption and carbon dioxide emission. This approach considered data centers that are distributed and have different carbon footprint rates and energy sources. The main parameters that had been taken into account are different types of VM requests, data center’s power usage effectiveness (PUE), and physical server’s proportional power usage. The approach was compared with other algorithms and was found to be able to reduce a significant amount of carbon footprints and also power consumption. The system architecture using this approach is presented in Fig. 7.

Cost of VM migration The energy efficient approaches for green computing discussed in [20-24] focus on virtual machines allocation and migrations. But migration and scheduling of virtual machines also incur some cost which cannot be neglected. An effort was made by authors in [25] for classification of migration costs involved and the parameters influencing these based on the previous work done in this field. The authors had categorized the migration costs as in the Fig. 8.

Fig. 8 Categorization of Migration Cost [25]

The parameters involved on which the migration cost depends are presented in the Fig. 9.

Fig. 9 Categorization of Parameters of Migration Costs [25]

Power and energy consumption in VM migration The authors in [26] focused on the power and energy consumption in the migration process for various types of workloads by doing experimental study in which it was found that migration can cause an increase in power consumption by approximately 10%. Also in some applications the power consumption can be reduced by reducing the duration of migration. Various types of workloads which are suitable for migrating and ways to efficiently migrate them were also discussed. V. SUMMARY OF THE VARIOUS APPROACHES

Fig. 7 Energy and Carbon-Efficient(ECE) Cloud Architecture[24]

We have discussed many approaches proposed by various researchers in order to achieve green cloud computing. The approaches discussed in section II do not make use of virtualization while those discussed in section III have used virtualization technique to make cloud systems more efficient. Table I gives the summary of all the approaches discussed above. The approaches, their main parameters and basic techniques being used are mentioned in it. The tools used for implementing the proposed approaches are also mentioned.

Table I Title of paper

Basic Technique used

An Energy Consumption Model and Analysis Tool for Cloud Computing Environments[15]

Energy consumption modeling, and analysis approaches

Energy Aware Scheduling across ‘Green’ Cloud Data Centers[16] An Energy Aware Network Management Approach using Server Profiling in ‘Green’ Clouds[17] Power-Aware Virtual Machine Scheduling on Clouds Using Active Cooling Control and DVFS [19]

Workload Scheduling

Energy Aware Cloud Service Provisioning Approach For Green Computing Environment[20] Energy Efficient VM Scheduling for Cloud Data Centers: Exact allocation and migration algorithms[21] Energy-Aware Ant Colony Based Workload Placement in Clouds [22] An Intelligent, Energy Conserving Load Balancing Algorithm For The Cloud Environment Using Ant’s Stigmergic Behavior [23] Energy and CarbonEfficient Placement of Virtual Machines in Distributed Cloud Data Centers [24]

Main Parameters considered The number of processes, the size of data to be processed, size of data transmitted and system configuration.

Tool used Not implemented

Visualization used (Yes/No) No

Packet arrival rate, service rate, response time, power cost and server Utilization Packet arrival rate, traffic volume and speed of server

Java based dedicated tool

No

Opnet and NS-2

No

VM Scheduling and migration

DVFS, MIPS, energy consumed by server

CloudSim

Yes

Energy aware model for automatic VM migration

Pre-processed data about the service usage, no. of clients, memory used and server utilization.

Not implemented

Yes

VM Scheduling and migration

No. of requested VMs, no. of servers, power consumption of server and VMs

Dedicated java based Simulator

Yes

VM consolidation by using Ant Colony Optimization

No. of VMs and no. of hosts

Java based simulation toolkit

Yes

VM allocation by using Ant Colony Optimization

Power consumption, remaining processing power, remaining memory

Not implemented, proposed tool CloudSim

Yes

VM allocation

Different types of VM requests, data center’s power usage effectiveness and physical server's proportional power usage

CloudSim

Yes

Workload Allocation

VI. CONCLUSION An effective and efficient use of computing resources in cloud can help in achieving Green Cloud Computing. In this paper, we have discussed various approaches proposed in previous research works in this field. Some approaches used server profiling, workload allocation and scheduling without use of virtualization while other made use of virtualization technique. Virtualization can help in better utilization of resources in Clouds. VM scheduling and migration is important but the cost and power consumption of migration processes should also be considered in evaluating system performance.

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