By Opeyemi Familade H Presented for the award of MSc. Heriot-Watt University

Transcription

1 MSc in IT BUSINESS 2012/2013 GREEN CLOUD: THE SIMULATION OF A CLOUD COMPUTING ECO-EFFICIENT DATA CENTER By Opeyemi Familade H Presented for the award of MSc. Heriot-Watt University 1

2 ACKNOWLEGDEMENT I want to thank God Almighty for seeing me through the entire masters program. I also want to thank my wife, my family and friends for all the moral support provided to me during this program. DECLARATION I,..., confirm that this work submitted for assessment is my own and is expressed in my own words. Any uses made within it of the works of other authors in any form (e.g., ideas, equations, figures, text, tables, programs) are properly acknowledged at any point of their use. A list of the references employed is included. Signed:... Date:... 2

3 TABLE OF FIGURES Figure 1: A diagram of people logging into the cloud Figure 2: Initial architecture of CloudSim Toolkit Figure 3: Current Layered CloudSim Architecture Figure 4: Gantt Chart showing the complete project plan Figure 5: Sample Excel sheet showing all calculations Figure 6: CASE 1 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 7: CASE 2 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 8: CASE 3 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 9: CASE 4 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 10: CASE 5 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 11: CASE 6 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 12: CASE 7 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 13: CASE 8 OUTPUT GRAPH OF ENERGY CONSUMPTION VALUES Figure 14: Initial Project Plan Figure 15: Revised Project Plan

4 TABLE OF CONTENTS ACKNOWLEGDEMENT... 2 DECLARATION... 2 ABSTRACT... 7 CHAPTER OBJECTIVES AND GOALS SCOPE STRUCTURE OF DISSERTATION... 8 CHAPTER LITERATURE REVIEW Introduction Aim of Research Relevance of Research to my Program History of Cloud Computing Cloud Computing Concepts What Business Challenges Led To Cloud Computing Advantages of Cloud Computing Disadvantage of Cloud Computing Cloud Deployment Models Private Cloud: Public Cloud: Hybrid Cloud: Cloud Computing Service Models Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) How the Cloud Works Cloud Storage: Cloud Architecture: Cloud Services: Virtualization: Operating Cost of Cloud deployment and Maintenance Green Cloud Management

5 2.11 A Scientific Model of Eco-efficient Data Center Management of Power Consumption Power and Thermal Management Dynamic Voltage and Frequency Scaling VM Allocation CLOUD SIM TOOLKIT Architecture of CloudSim Capabilities Limitations CHAPTER METHODOLOGY Introduction Methodology Steps REQUIREMENT ANALYSIS Problem Summary Evaluation Requirements PROFESSIONAL, LEGAL, ETHICAL AND SOCIAL ISSUE Professional Legal Ethics PROJECT MANAGEMENT Key Activities Risk Management and Change Management Plan Change Order Process Risk Management Risk Mitigation CHAPTER IMPLEMENTATION INTRODUCTION DVFS Mechanism CASE 1: INCREASING THE NUMBER OF HOSTS, VMS AND CLOUDLETS EVALUATION OF THE RESULT CASE 2: INCREASING THE NUMBER OF VMS ALONE

6 EVALUATION OF THE RESULT CASE 3 INCREASING THE NUMBER OF HOSTS EVALUATION OF THE RESULT CASE 4 INCREASING THE NUMBER OF CLOUDLETS EVALUATING THE RESULT CASE 5 INCREASING THE NUMBER OF HOSTS AND VMS EVALUATION OF THE RESULT CASE 6 INCREASING THE NUMBER OF HOSTS AND CLOUDLETS EVALUATION OF THE RESULT VM MIGRATION CASE 7 INCREASING THE NUMBER OF HOST, VMs, CLOUDLETS EVALUATION OF THE RESULT CASE 8 INCREASING THE NUMBER OF HOSTS EVALUATION OF THE RESULT CHAPTER PROJECT EVALUATION INTRODUCTION OBJECTIVES EVALUATION Design the simulated scenario of a cloud data center using a DVFS and later VM allocation mechanism Implement the design on a system using a simulation toolkit Collate and evaluate the simulation results Evaluate the mechanism used to improve the eco-efficiency of data center management in cloud computing METHODOLOGY EVALUATION IMPLEMENTATION EVALUATION CHAPTER CONCLUSION LIMITATIONS Future Work REFERENCES

7 ABSTRACT It is believed that the concept of Cloud Computing gained traction from Client Server Architectures and it is a major threat to data center adoptions by organisations. With cloud computing, organisations and businesses have now been provided with the option of either owning a network or using a network. This is achieved by creating several development layers for services, monitoring and options [1]. To set up and run this huge data centers either privately or publicly owned, consists of huge computing resources that require massive power capacity for operations. It has been said that in some cases a large cloud data center can consume the same amount of energy that an entire city consumes which translates to huge power bills and CO2 emissions [13]. This increases the energy impact on the environment. This dissertation with implement and evaluate 2 energy saving techniques namely Dynamic Voltage and Frequency Scaling (DVFS) and Virtual Machine (VM) migration, which can be deployed to reduce energy consumption levels of the data centers. The DVFS mechanism and VM migration will be compared and evaluated against a regular cloud data center environment that does not have any form of energy saving technique integrated on it also known as Non Power Aware (NPA) to determine the impact of DVFS mechanism and VM migration. A cloud data environment simulator will be deployed to conduct the different experiments which will be used to investigate the efficiency of DFVS and VM migration against NPA in a cloud data center. 7

8 CHAPTER OBJECTIVES AND GOALS The primary objective of this dissertation will be to evaluate how energy saving techniques like DVFS mechanism initially then VM migration with DVFS enabled can improve the eco-efficiency and performance of a cloud infrastructure. These would then be evaluated against a cloud environment without any power aware mechanism implemented on it. A number of goals have been outline to achieve the energy reduction and cost savings Understand different cloud architectures, cloud models, cloud importance to business and concept of virtualization Understand issues associated with cloud computing data center operations, maintenance and management Understand how the simulation toolkit will work in terms of capability, usability, compatibility and limitations to offer feasible evaluation of the green technologies used in data centers. Illustrate possible areas of Improvement on the current infrastructure by benefitting from virtualization and hardware abstraction. 1.2 SCOPE The scope of this project is to highlight how eco-efficiency techniques can improve the operational costs of the data center while still delivering the required service to the organisation. The Minimum Scope delivery will be thus Design the simulated scenario of a cloud data center using a DVFS and VM allocation mechanism Implement the design on a system using a simulation toolkit Collate and evaluate the simulation results Evaluate the mechanism used to improve the eco-efficiency of data center management in cloud computing 1.3 STRUCTURE OF DISSERTATION Chapter 1: Introduction 8

9 This chapter will look at the objectives and goals of this dissertation as well as the scope for this dissertation and finally it would explain the structure of the dissertation Chapter 2: Literature Review This chapter will review the literature and provide an understanding of the project. It will explain Cloud Computing concepts, history and deployments. It will then look at energy saving techniques that have been researched and are currently used in cloud data centers. Finally it will look at the tools to be considered for the implementation of this project Chapter 3: Methodology This chapter will detail all the methodology steps to be used for the project implementation. It will look at the requirement analysis and explain the project management processes included the risk analysis and mitigations. It will also look at the legal, ethical, laws and Social issues that could be affected by this project Chapter 4: Implementation This chapter will produce the results of all the implemented Cases and plot graphs highlighting the differences between the energy saving scenarios and the non-energy saving scenarios Chapter 5: Evaluation This chapter will evaluate the whole project in 3 phases. It would first perform an evaluation based on the set objectives. Then it would perform an evaluation based on the methodology used. The final evaluation would be based on the results of the implemented cases. Chapter 6: Conclusion This chapter would highlight conclusions to the project. All the rough patches and work around that were encountered during the implementation of this project. It would also look at the limitations which were encountered during the project and how I was able to work around them. It would also take a look into future works that will make the project more credible in achieving better eco-efficiency results with regards to testing. Finally it would provide some advice for future students who might want to write something similar to this. 9

10 CHAPTER 2 LITERATURE REVIEW We are going to look at various cloud computing service models and the other cloud computing offerings and look at the advantages and disadvantages and then look at techniques that we will use for eco-efficiency to reduce the energy consumption in the different cloud environments. 2.1 Introduction Cloud computing is the next stage in evolution of the internet [1]. The cloud in cloud computing can be defined as a collection of shared computing resources that are easily accessed and consumed at will [1]. Another definition is that cloud computing is a computing style which is elastic and scalable which allows IT service delivery to an organisation [2]. The most comprehensive one and probably the most accurate is from NIST, the cloud is defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources for example networks, servers, storage, applications, and services that can be rapidly provisioned and released with minimal management effort or service provider interaction [3]. Concepts of this new trend of computing started back in the early 60s mainly by telecommunications companies as point to point data circuits and from the 90s as virtual private networks. Network traffic and bandwidth efficiency were one of the major reasons for the advent of cloud computing for server and infrastucture. Amazon, IBM, Google and many universities played very important roles in promoting and adopting private cloud computing [4]. The diagram below is just a brief example of clients logging into the cloud [5] 10

11 Figure 1: A diagram of people logging into the cloud The infrastructure requirements to implement cloud data centers with redundant pairs in several locations to guarantee availability, business continuity and infrastructure resilience are power demanding and incur huge operational costs. This paper will look at ways in which the ecoefficiency of data centers can be improved to add value to the operational and maintenance costs of organisations with regards to data centers. 2.2 Aim of Research To experiment on possible power saving techniques in Cloud data centers To implement the techniques using a cloud simulation toolkit To compare the result with the power saving techniques against no power saving techniques Suggestion on energy reduction for non IT related power equipments 2.3 Relevance of Research to my Program The relevance of this research to my degree program is to get a challenging project than can enhance my knowledge and skills in the field of Cloud Computing. Cloud Computing is a hot new trend in the field of IT and I will like to use this research to identify and evaluate cloud data center energy saving techniques which is important to the development of Cloud in the IT industry. 11

12 The study would build upon my knowledge of java which I learnt during my Software Engineering Class and it would test the knowledge to a higher degree as I would be implementing and debugging the CloudSim toolkit for the simulation of this project on Eclipse IDE. Information Systems Methodology course taken last semester would also help in determining an appropriate system development approach for this research report. The steps and methodology to analyse, execute and evaluate will also be decided. 2.4 History of Cloud Computing Cloud computing infrastructure originates from Client-Server architecture framework [6]. This is a framework started in the 80s. Data and programs were stored in large machines called mainframes and users were connected to the mainframes servers through terminals which was used solely for the purpose of connectivity. The IT administrators were the custodian of the servers and was not user-centric or user-friendly. Access was provided one at a time which usual lead to bottle necks and delays. This connection type was referred to as master-slave relationship. The next technology to come after client-server architecture was the peer 2 peer computing (P2P) which basically is an interconnection of systems between each other. In this type of relationship, the control of resources and services are decentralized since each system acts as the client and server. The easiest and most notable example of this is the internet. The next technology to come after P2P was a subset of it called distributed computing. This model takes advantage of idle machine resources by provisioning them to generate more computing power. A practical example of this model was implemented at the DEC research center [6] The next technology in the client-server history was group collaboration by allowing multiple users simultaneously access and control on the same system based project. This used the internet cloud to make this happen and this is what really gave birth to cloud computing Companies like google and yahoo started development of large farms of servers which really helped promote cloud computing and open its possibilities to the world. This expanded into webbased services most notably the services which is indeed cloud computing acting as Software 12

13 as a Service [5]. By 2007, cloud computing has gained worldwide recognition and adoption to become an accepted computing technology. 2.5 Cloud Computing Concepts What Business Challenges Led To Cloud Computing Business invest huge sums of money on IT equipment, software and consulting to help with decision making, strategy, operations optimization, profit generation, forecasting, and competitive advantage amongst many other reasons [6]. Delivering of these services to the business is faced with many regular challenges from the business perspective which include Delays from IT department Unfulfilled requests Cost overruns Unusable systems All these tend to become a roadblock for businesses or an obstacle to business growth which led to businesses looking for alternatives. Many of these problems happen due to some factors which include Constant time pressure to deliver service No clear requirements IT complexities Security and Compliance [5] Another major issue that could be attributed to the rise of cloud computing services is the evolution of devices such as smart phones, tablets and phablets, which are capable of performing almost all required services a bigger and heavier laptop would normally perform. The best part was that they had a longer battery life than the laptops, which makes working while on the long 8 hour flight, or train or road trip much easier [1]. 13

14 Cloud computing concepts revolves round the fact all computing related activities are service delivery oriented be it infrastructure layer, application layer or collaboration layer. Cloud computing is an economical and business model for operations and maintenance of data centers due to its elasticity and flexibility. The elasticity means capacity can be provisioned and deprovisioned at will, flexibility means you can customize it to your computing needs. You can make required CPU utilization for users, storage requirements and provide testing environments as required Advantages of Cloud Computing Rapid Elasticity: Capacity planning has always been a major issues with IT planning for organisations. A hike in users can occur at any time and the network would suddenly seem under provisioned and over utilized. On the other hand a massive retrenchment can also occur and the network would seem over provisioned and underutilized. With rapid elasticity, you can add and remove capacity at will Self Service: With self-service, the power to request for resources is now automated and priviledge can be given to non-it staffs. This increases productivity as you don t need to rely on IT for assistance all the time which reduced the overall workflow process. Cost Saving: A massive reduction in huge investments on large hardware to appease medium sized services and applications. Reuse of existing infrastructure to be cloud compatible also helps with mass cost reduction. This assists with proper capacity planning for IT department Billing and Metering of Services: Cloud computing has built-in facilities that enables it to bill users for services. It does this by metering and tracking usage of IT. This allows exco to have a proper idea of the cost of IT and calculate if IT is generating profit for the organisation [7] Disadvantage of Cloud Computing Internet Connection: The Cloud relies on constant regular internet connectivity which is very risky as connectivity could be unavailable at any time due to various reason. The cost of loss of 14

15 connectivity might be huge or low depending on when the loss occurs and the duration of the downtime [1]. Security: This applies to public clouds mostly as clients take a leap of faith in trusting the cloud service provider s security policies. Storing your organisations critical data and services on the cloud and exchanging this data over the internet though encrypted is a huge risk. The service provider could also be breached which would give access to the clients data Compliance and Legal Issues: For organisations that have presence in several countries, it is important for the IT administrator to understand the IT laws and regulations of all countries where they have operations. Would government have access to companies critical data in certain places, what kind of data are acceptable in certain areas and general IT compliance? Skill Shortage: Since cloud computing services are relatively new, the required expertise would be limited in most organisation and hence business might need to bring in contractors and consultants which would add to the total cost of IT 2.6 Cloud Deployment Models There are 3 major cloud computing deployment Models available currently Private Cloud: This report is about simulating a cloud. The private cloud has the following properties Managed by the organisations IT staff Fully customized for the organisations needs Either be on-site or off-site Security, Regulatory compliance, IT governance are all the responsibility of the company SLA here would be between IT department and the Organisation Major Disadvantages of a private cloud include costs, time to deploy, skill gap 15

16 2.6.2 Public Cloud: The Public Cloud is owned by a service provider and differs from the private offering in the following ways Owned and Managed by a service provider Quickly deployable Off-site Customization to the business exact needs is not certain Disadvantages of a public cloud include security of data, IT governance and compliance of the service provider, billing discrepancies Hybrid Cloud: The hybrid cloud is a mix between a public cloud and a private cloud which is gaining popularity currently since it has all the advantages of the private cloud and disadvantages of the public cloud and vice versa. Here are the characteristics of the hybrid cloud A mix of public and private cloud deployment Organisation determines what goes to the public cloud and what goes to the private cloud It has all the strengths of the public cloud and the weaknesses of the private cloud, and vice versa It is important to note there is also a 4 type called the Community Cloud. This is a cloud shared by many organisations and is mainly used in collaborative scientific researches [1]. The reason why it is not officially recognized is because it uses both the private and public cloud for deployment which makes it either a private cloud, a public cloud or a hybrid cloud. 2.7 Cloud Computing Service Models There are several Cloud Computing models currently available but we would be focusing on 3 major ones which are widely used. Understanding the service models go a long way in allowing organisations pick the best service to adopt for their business strategy to align with IT service delivery. 16

17 2.7.1 Software as a Service (SaaS) This is the first know service to be deployed under cloud computing with the most common example of this being the services. Business applications are hosted by the service provider and delivered as service to clients. This service model is typical pay per use Platform as a Service (PaaS) In this Service model, Customer created applications are deployed into the cloud. This is viewed as an evolution to web hosting and organisations usually use it to create cloud ready business applications. The major drawback from this service model is that you cannot customize the underlying infrastructure Infrastructure as a Service (IaaS) This is the underlying level which we would be deploying for this project. This layer offers storage and computing resources/hardware including CPU utilization, operating system, networking and hardware requirements that IT organisations use to deliver business solutions. This is typically pay per use and often contains SLAs either between the organisation and the service provider or between business and IT 2.8 How the Cloud Works The Cloud similar to client server architecture is about generation of computing power by combining of available resources in the pools. This are the basic components that are in a cloud Cloud Storage: This is Data Storage in the cloud which historically is the start of cloud computing. Data are stored in cloud storage as against network storage or local storage which makes the data available even outside the network. The data is stored across the several cloud servers and is duplicated to ensure data recovery and business continuity are not at risk at any point in time. This eliminates the need for dedicated network storage which are financial expensive to purchase and maintain over time as data increases in the organisation 17

18 2.8.2 Cloud Architecture: This is the most impressive aspect of cloud computing which capitalizes on virtualization technology. The user sessions are intelligently managed to connect users to several network resources in the back end without any knowledge from the user. The user requests go through the system management and are assigned the adequate resources required and provisioned for the user and the user session can begin and monitored to add or reduce computing resources based on performed task or pre-configured settings by the administrator Cloud Services: This are software or better defined as services which are deployed to the users via the internet and hosted in the cloud. The user connects to the cloud and gains access to the services by launching a session on the cloud for that service. The application is displayed on the user client but the storage and processing for the application are hosted on the cloud server. This has a huge advantage in that companies can now use thin clients (monitors, keyboard and mouse) for their staffs who can access all their required content, applications and data on the cloud which would reduce costs significantly for the organisation. No need to worry about buying complete systems, or running operations and maintenance on them, or applying patches or security features on them Virtualization: Most people make the mistake of calling virtualization cloud computing. Virtualization has its own unique introduction to the IT world, but it has blended so well with cloud computing to create a perfect combination which has worked out well for both parties. Virtualization is now being applied to so many aspects of data centers now in networks, storage, applications, operating systems and hardware among others. Virtualization has the following features Partitioning Isolation Encapsulation There are several types of virtualization apart from full virtualization. There are hardware assisted virtualization, paravirtualization, hybrid virtualization, operation system-level virtualization and partial virtualization amongst others [11]. 18

19 2.9 Operating Cost of Cloud deployment and Maintenance The Total Cost of Ownership of a cloud encompasses cost of IT infrastructure and the cost of operations, maintenance and administration of the cloud [12]. This includes human resources cost of skilled IT staffs or consultants, the hardware and software that make up the cloud and cost of optimization of the cloud to improve service availability and delivery. Since cloud service providers are looking for the cheapest way to deploy cloud services for their clients while maintaining service levels and quality of service, we would be looking at how ecofriendly cloud deployment can help to achieve this Green Cloud Management Green Cloud management is about how to operate and manage a data center in an environmentally friendly manner. This means efficient or reduced energy consumption as well as reduction in carbon dioxide emissions. This eco-efficiency mechanisms and policies in Cloud management are essential to reduce the effects of greenhouse emissions on global climate. According to Smarr, carbon emission from the ICT sector is expected to triple from 2000 to 2022 [13]. Cloud infrastructure are powered by loads of equipment housed in data centers that generate huge amount of heat emissions when working. To mitigate the heat, cooling systems are installed in the data centers which consume huge energy. Therefore, the need to reduce energy consumption in data centers is high. Another major benefit from this is the reduction in operating cost for the vendor A Scientific Model of Eco-efficient Data Center We will be looking at some metrics that can be used to measure the eco-efficiency of a cloud data center. One of such metrics is Power Usage Efficiency, which is used to define how eco-efficient the infrastructure of a cloud data center is. Power Usage Efficiency calculates how much of the power is consumed by the server in contrast to the overhead in order to measure how data centers uses its power efficiently [14][15]. According to the Green Grid Organisation, Power Usage Efficiency is calculated by dividing the Total Cloud data center energy usage by the IT energy 19

20 usage. The total Cloud data center energy usage includes IT energy, Fire extinguishers, Cameras, lighting, security equipments, heating and cooling and any equipment that consumes energy in the data center. According to Gillian [16], the average Power Usage Efficiency in most data centers is approximately 2.0. If the Power Usage Efficiency increases above this then it means the data center infrastructure is becoming more inefficient but if the Power Usage Efficiency reduces lower than 2.0, then it means the data center infrastructure is considered to be more efficient. Minimizing the energy consumption by IT and other non IT equipments is what Power Usage Efficiency Optimization is all about. The closer the power consumption by all other non IT equipments are to zero, the greener the Power Usage Efficiency is. Another metrics that will be used with the Power Usage Efficiency is the Energy Reusable Efficiency. This metric will be used along with the Power Usage Efficiency to determine the ecoefficiency of a cloud infrastructure. The Energy Reusable Efficiency is defined as measuring how efficiently the wasted energy that leaves the cloud data center is reused in adjacent non-cloud data center spaces [17]. The energy recycled or reproduced could be used somewhere else in a different facility example could be converting heat generated from the data centers into electricity using reverse heat engine technology. The reason for using the energy in a different location is so as not to affect the Power Usage Efficiency. Power Usage Efficiency assesses the efficiency of the energy used by the data center while the Energy Reusable Efficiency assesses the efficiency of altering the wasted energy that leaves the cloud data center to be reused in other places outside the boundaries of the cloud data center [18]. Using the Power Efficiency and Energy Reusable Efficiency metrics is not enough for this project for us to say a data center is eco-efficient because the Power Usage Efficiency measure all non IT equipments and does not take into consideration IT efficiency or IT load distribution [19][20]. And this project focuses on reduction of both IT and non IT energy consumption to give achieve a better green rating for the data center infrastructure. To achieve energy saving on IT infrastructure, virtualization discussed above can be used as well as migration of server to newer models which 20

21 are faster, stronger and have smaller footprints thereby reducing the total number of running servers delivering the same IT workload. Thus the definition of a scientific model of eco-efficient cloud data center depends on how efficiently energy is consumed and reused in a data center with minimal or no impact on the environment in terms of environmental hazard emissions. The different measurement metrics would help drive the data center managements towards being greener Management of Power Consumption Energy saving techniques have become one of the new trends of IT business because of the benefits gained from environmental protection and huge cost benefits from economical perspective. The techniques can be deployed to monitor and control energy consumption. The effects of this technique leads to huge energy and financial savings for organisations which helps organisation to comply with government regulations on reduced environmental impact. A Data center can said to be eco-efficient if it consumes less power than another data center while processing the same or more workload [21]. We will look at some of the techniques that are currently being researched and implemented on reduction of power consumption in cloud data centers without degradation of performance in the data center Power and Thermal Management A Study on the importance of Power and Thermal Management to improve the energy efficiency of data centers was conducted by Pakbaznia et al [22]. This is an approach that requires a prediction of the incoming workload in terms of requests per seconds by using Workload Monitor for this purpose. It then uses the data from Workload Monitor to make decisions for the number of servers and placements of the servers. It also maintains the supplied cold air temperature in order to reduce the power consumption for both the servers and the cooling units Dynamic Voltage and Frequency Scaling Dynamic Voltage and Frequency Scaling is a technique that observes the workload offered and then alters the CPU s power consumption proportionately. Performing this action subsequently alters the performance level and is widely used to reduce the energy consumed in computing servers [23]. A DVFS technique is designed in a way that decreases the energy consumption of 21

22 the CPU by controlling and reducing the supply of voltage and frequency. Kim, Buyya and Beloglazov [28] have conducted similar simulation experiment to show how data center provider can increase their profits and conform to government environmental regulations by reducing power consumption by deploying DVFS techniques VM Allocation The aim of this technique is to reduce power consumption of cloud data centers by replacing physical machines with virtual machines according to a study by Corradi et al [21]. This techniques aims at allocating more Virtual Machines on less physical machines to allow maximum utilization of the running physical machine without degradation on performance. This technique allows switching off of idle servers and reducing the total power consumption in the cloud data center 2.13 CLOUD SIM TOOLKIT For this research, the key is experimentation, via simulation, to determine extent of power consumption and saving. The particular service we would be focused on here is energy reduction/saving techniques in cloud deployment. By performing experiments in a controlled environment, the organisation can identify performance bottle necks, pre-test expected outcome of implementation using different scenarios and develop the best implementation technique to be used based on the simulated experiments and analysis. The CloudSim toolkit is one of such simulation frameworks that has been developed to fulfil these requirements by modelling real life cloud scenarios [30] Architecture of CloudSim CloudSim is a simulation framework that allows simulation, experimentation and modelling of cloud infrastructure and application services. The first toolkit consisted of 4 layers SimJava, GridSim, CloudSim and UserCode. This was later revised and the layers were reduced to 3 layers with SimJava layer being scrapped and GridSim was merged into a layer called CloudSim Core simulation engine [9]. Figure 2 and 3 below show the initial and revised CloudSim toolkit layers. 22

23 Figure 2: Initial architecture of CloudSim Toolkit 23

24 Figure 3: Current Layered CloudSim Architecture The bottom layer SimJava provides the base engine of the simulation which supports the implementation of core functionalities essential for the higher-level frameworks of the simulation like administration of the simulation clock, queuing and processing of events and interaction between different components. This layer was scraped in the revised version and replace with the CloudSim Core simulation engine due to the limitations of SimJava including Not allowing the simulation to programmatically reset at run-time Not supporting new simulation entities to be created at run-time Causing performance overhead with the increasing size of the system due to its multi thread nature The CloudSim core simulation enables more advanced options unavailable in SimJava and overcomes most of the limitations highlighted above. 24

25 Just above the SimJava layer is the GridSim layer which supports modelling fundamental and highlevel grid components like information services, datasets and resources. The next layer is the CloudSim layer which extends the functionality of the GridSim layer. It supports modelling virtualized cloud-based data center environments like VM, bandwidth, memory and storage. The top layer as mentioned previously is the User Code layer, which reveals the configuration of functionality for system components by the developer like number of hosts, specification of the hosts. It is at this layer the developer configures and input different test scenarios supported by CloudSim in order to evaluate and capture statistics on cloud infrastructure and applications [10] Capabilities CloudSim comes packed with some extensive features and capabilities for modelling cloud computing environment and infrastructures. CloudSim can support simulating from large-scale data centers environment to small-scale deployments with very minimal consumption of memory and test infrastructure. It offers flexibility and applicability with minimal time and effort to support initial performance testing according to Calherios et al [9]. CloudSim comes with a virtualization engine for simulation of virtualized services on independently managed on a single node of the data center. The key components that would be used for this research from CloudSim architecture will be CloudCoordinator, VMM and Sensor. This will be used for managing the energy conscious techniques in the simulated data center. It has support for the energy-awareness techniques we will be focusing on DVFS and VM allocation. The Sensor component is used by the CloudCoordinator to monitor required performance parameters. In this case the parameters will be energy consumption and resource utilization. The sensors work with CloudCoordinator to send real-time information of the active Virtual Machines to the VMM. The VMM uses this data which include the load conditions and processing share to implement DVFS and resizing of the Virtual Machines. CloudCoordinator also issues migration commands to the Virtual Machines based on the current 25

26 utilization of resources to change the power state of nodes as Virtual machines are turned on and off Limitations As with most packages currently in the market, CloudSim also has some limitations for simulation and modelling Cloud environments. A learning curve is likely incurred to install CloudSim accurately on a local machine as it is not a ready-to-use tool. This includes writing and debugging java codes to access the library. Secondly, some of the functionalities of CloudSim are limited and requires installing extensions. For example, DynamicCloudSim extends CloudSim simulation toolkit by adding dynamism to cloud environments like heterogeneity in the performance of computational resources, uncertainty in and dynamic changes to the performance of virtual machines and general failures during task executions. Thirdly, you need to install 3 rd party applications like Ant to compile the CloudSim Classes which also requires a learning curve to understand Path creation and java structures. 26

27 CHAPTER 3 METHODOLOGY 3.1 Introduction Three different methodologies were analyzed to implement this project, direct experiment, Mathematical modelling and Simulation which was eventually picked. Here is a breakdown of why simulation was chosen. Direct Experiment is the most ideal scenario to use to get the best and most accurate output from the research since it would involve working on a live data center environment. This can be done by running DVFS and VM allocation mechanism on the data center and evaluate the results against normal operations without any energy saving mechanism implemented. But this approach would be time and man power consuming and it would involve huge costs to setup if there is no access to a test lab. Due to the limited time scale for this research, this method is not appropriate for this. Mathematical Modelling is the second approach considered for this research. This involves using mathematical equations to evaluate the energy and performance/degradation trade off in a cloud data center environment. This is more suitable for this research report but this works in collaboration with either the direct experiment of simulation to get the required figures needed to calculate and analyse. This method is used to check and compare results with benchmarks. The third option is Simulation of the desired environment to measure the impact of energy saving techniques in a cloud environment. Most simulation tools come with inbuilt mathematical models so they offer the test and analysis phase as a framework. Due to this option, the simulation method was chosen for this project. The environment would be designed based on the desired environment to be experimented on and the results would be outputted after mathematical modelling has been done by the simulation tool. CloudSim as discussed previously, has been selected as the simulation tool for this project but there was another option that was considered for this research. GreenCloud which sits on top of NS2 and uses C++ for programming would have been the perfect tool for this project as it measures 27

28 the details of consumed energy in the components of a cloud data center [26]. The major limitation with GreenCloud is that it is time consuming to implement and requires a high amount of memory for simulation hence the scalability for GreenCloud would only work with a small data center and large deployments can t be simulated. As explained previously, CloudSim is relatively easy to implement if you can get pass the learning curve to install and it has low simulation overhead which requires minimal memory for deployment. CloudSim also has several toolkit support which will be used for eco-efficiency techniques which are planned for execution for this research. 3.2 Methodology Steps The approach to be used using the simulation toolkit would be Analysis, design of scenario for cloud Simulate scenario Result evaluation and analysis Designing and running the scenario would be an iterative process, hence several tests would be performed to ensure the appropriateness of the simulated data center scenarios. An iterative scenario also helps to get better accuracy for the simulated environment figures by using mathematical formulas to calculate standard deviations, mean values. Another advantage of using iterative scenario is that it gives the research more flexibility to alter figures and re-run test quickly without having to design from scratch again. This allows to quickly go back and change figures for different scenarios that would be tested for the project The analysis stage will be used to set the objectives of the simulation scenario and generate figures to be tested. This involved understanding how CloudSim toolkit will function and run in order to implement the experiments. The design stage will then setup the CloudSim toolkit, running and collection of data from the experiments according to the objectives outlined in the analysis phase. The data is then verified and interpreted to ensure it falls in line with the expected objectives. The final step would be to evaluate the result and see if indeed the eco-efficiency techniques actually save energy consumption or not 28

29 3.3 REQUIREMENT ANALYSIS The requirement analysis is used as an evaluation criteria for pre and post DVFS implementation, to report the success or failure of the eco-efficiency technique in saving energy. The report will tackle compatibility, constraint, usabilities and other constraints and considerations for the project Problem Summary Cloud data centers are equipped with large energy consuming equipments which brings a major challenge to operating and controlling the power consumed by this equipments. An average data center consumes about 10 to 100 times more energy per square foot than any regular office structure or even the same or more energy than a city [24][25]. The energy consumption cycle is generated from the servers which generate huge amount of heat which requires a larger cooling systems to cool them down which sets off CO2 emissions and huge energy consumptions. This increases the operation costs of running the data center. In addition to the two problems highlighted above, government policies around the world are now hacking down on cloud providers to reduce that carbon emissions and implement green technologies to help reduce energy consumptions in the cloud centers. Most data centers now implement eco-efficient techniques like DVFS and VM allocation to help reduce the energy consumption and enhance Total Cost of Operations of the data centers. The main deliverable from this project will be to recommend green techniques that can be used for management of data centers by evaluation of eco-efficiency techniques. In order to do this, there will be implementation of java classes on CloudSim which will help with the simulation of the experiments. The source code for the java classes will be provided at the appendix Evaluation Requirements After going through all the functionalities, CloudSim 3.0 will be the requirement used for this research project due to its functionalities that supports eco-efficiency techniques as against previous versions that does not have support for this. It consists of several classes that support various scenarios that we need to test for example Cloudlet and hostclass are responsible for 29

30 forming cloud based applications like business workflow, social networking and content delivery for cloudlet, while hostclass models the physical resources like compute servers or storage. Another important class that makes CloudSim appropriate for this research project is VM Class which models the virtual machine components which is hosted by a cloud host component which stores the related characteristics like memory, processor and storage size. The typical characteristics of a data center are what will be measured for this experiment, 1 st without DVFS and 2 nd with DVFS. The parameters are the number of hosts, VMs and Cloudlet. The number of host specifies no of servers in the data center, the VMs specifies number of virtual machines running in the cloud data center while the cloudlet specifies applications running in the data center. These 3 parameters have already been preset in CloudSim and will be kept as their typical values in CloudSim toolkit as experimented on by Calheiros et al to quantify CloudSim efficiency in simulating Cloud Data environments [9]. The experiment will be run using several test scenarios and combinations for host, VMs and cloudlet using DVFS and later DVFS with VM allocation and will be analyzed against the default data without using DVFS to see if energy consumption is reduced, increased or unaffected. The values discusses represent the typical settings found in most data centers for Hosts, VMs and Cloudlets The examples of the values are Host Max Power 250 watts Static Power 70% RAM 10000MB Storage MB Bandwidth Mbps MIPS {1000, 2000, 3000} VMs Number of CPUs 1 RAM 128MB 30

31 Bandwidth 2500 Mbps Image Size 2500MB MIPS rating {250, 500, 750, 1000} Cloudlets Length Number of CPUs 1 File Size 300 Output Size 300 So, provisioning of each Virtual Machine will require 1 CPU core, 128MB of RAM and 2500MB of Storage. These are the requirement that will be used for the experiment to produce the results by doubling incrementally increasing the value starting from the default value for the VMs, Hosts and Cloudlet till we reach the maximum value a data center can cater for. As stated above, experiments will be conducted increasing all values, individual values and combination of values at different stages to capture the energy usage with DVFS implemented and without DVFS implement. 3.4 PROFESSIONAL, LEGAL, ETHICAL AND SOCIAL ISSUE Professional, legal, ethical and social issues are very important for executing this project as all laws and ethics governing cloud computing deployment will be adhered to actively Professional All the research analysis and design will follow full project management practice and some sort of prototype software development process whereby an iterative development process will be used Legal All hardware and software used for this research report are all legal and don t violate any laws in deploying or using them. The system that will be used runs an authentic windows operating system and has a valid Microsoft excel and Microsoft word installed on it for the use of the research report and result gathering and calculations of the mean and standard deviation. Eclipse IDE was download free of Charge online legally and does not violate any local laws running it on the 31

32 system. The CloudSim toolkit is an open source software and is free to download from the developer team site Ethics This research will be conducted following full IT ethical methods. Proper citations and referencing will be used for all works sourced from journals and online media. All data that will be retrieved from this research would all completely be my work and will not use data from any outside source for the evaluation criteria without referencing the data source. 3.5 PROJECT MANAGEMENT A project management approach will be used for this research to assist with the scope, time control and risk management. The project management plan will help to track the progress of the research from the start to the completion stage highlighting several milestones to be reached at different stages. It will also ensure there is no scope creep and it would aid with time control for the research project Key Activities Initiating Stage: Like all project, this involves all stakeholders meeting, in this case my supervisor, to discuss the idea for the project and agree on the project scope and minimum deliverables Planning Stage: The project is analysed and an execution and risk management plan is developed and sent to the supervisor for confirmation. The Literature review, history of cloud computing and implementation technique to be used are explained in details here. Execution: The project is executed using CloudSim toolkit according to the plans following proper change management and risk management processes during the execution. Evaluation: This stage is not an official project management stage but is vital for this research report. The results from the execution stage are evaluated using the eco-efficiency techniques Closing: This stage involved reporting final findings from the research, completing the dissertation using proper referencing and submitting all data and reports used for this project. Status Report: This is not part of the project management stage but is done weekly to update my supervisor on the progress of the project 32