Today, cloud computing is a new norm because it is cheap-pay as use service. Virtualization ... Google, one of the leading cloud computing providers, controls.
Kenga Mosoti Derdus Energy-Aware Virtual Machine Placement in the Cloud Using Visual Cognition Research Concept Paper
Contents Abstract........................................................................................................................................................ 3 Background ................................................................................................................................................. 3 Research Gap .............................................................................................................................................. 5 Problem Statement...................................................................................................................................... 6 Aim ............................................................................................................................................................... 7 Objectives..................................................................................................................................................... 7 Research questions ...................................................................................................................................... 7 Research Methodology ............................................................................................................................... 8 Conclusion ................................................................................................................................................... 8 References .................................................................................................................................................. 10
Abstract Today, cloud computing is a new norm because it is cheap-pay as use service. Virtualization, which allows Virtual Machine (VM) to be placed in Physical Machines (PMs), is the technology that packs cloud computing. This process of mapping VM to PM is called VM placement. While VM placements takes place, many factors have to be considered such as energy consumption, Quality of Service (QoS) and costs and achieving Service Level Agreement (SLA). As today’s cloud centers have many physical machines, the process of VM placement has become a global optimization problem because VM placement has to find the most appropriate PM to hold the incoming VM. Because past statistics show that a larger part of budget goes to power bills, the main concern of this research is to design a VM placement algorithm that leads to the least energy consumption in a cloud center. To solve the global optimization problem, we will develop a visual cognition based algorithm, which solves the inefficiency of pairwise comparison-it is time consuming. The algorithm will be tested in two phases; theoretically and practically over cloudSim cloud simulator. Positive results, which are expected from this research, will be of benefit in that it will reduce power consumption by cloud providers and in turn decrease carbon footprint. Keywords: cloud computing, visual cognition, cloudSim, optimization problem, algorithm
Background While there are many definitions of cloud computing, it can be defined using the components of its anatomy as internet-based data exchange and access of low cost computing and application characterized by on-demand self-service, pooled resources, elastic capacity usage based-billing and internet accessibility (KPMG, 2011). Cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) whereas cloud deployment models include private, public, community and hybrid (Rajkumar , James & Andrzej , 2011). As cloud computing continue to be accepted by most businesses, two events, which took place in 2006, are said to be the catalysts of all this; Google CEO Eric Schmidt’s announce of cloud computing and Amazon’s Elastic Cloud Computing (EC2) as part of their web service (Osei & Cudjoe-Seshie, 2013). According to Institute of Engineering and Technology, there were 625 million cloud storage subscribers by 2013, and it was estimated to grow to over 700 million by 2014 and 1300 million by 2017 (Amar & Birendra, 2014). On overall and globally, 240 million will be using integrated
cloud services (Manaswini, 2015). Google, one of the leading cloud computing providers, controls 2% of the 1 million servers worldwide (Manaswini, 2015). On the other hand. Microsoft estimates that it will have 1 billion Windows 10 devices on the market and $20 billion in annual cloud revenue by the middle of 2018 (Matt, 2015). According to a study by Goldman Sachs, IaaS global revenues will hit $106 by 2016, which is a 21% increase from IaaS revenues generated in 2015. Besides, spending on cloud computing by organizations and individuals will grow by 30% from 2013 to 2018 (Columbus, 2015). These figures shows that cloud computing has been accepted and that there is enormous migration from traditional server computing to cloud environments. In cloud environments, only computing resources required are used leading to less energy consumption and reduced carbon print. In fact, using cloud computing results in 30% energy consumption and carbon emissions that using on-site servers (Amar & Birendra, 2014). By moving operations to the cloud, therefore means moving the burden to the cloud providers to manage it for you. But what is the implication of this? It has emerged that because of the wireless cloud, a lot of energy is still consumed because the cloud centers are overworked leading to increased emission of carbon (Bell Labs, 2013). On the other hand, the largest portion of cloud center operations costs goes to energy consumption in the cloud. The amount of energy consumed by a cloud center depends on the number of processes taking place and how efficient they are executed. Cloud computing is made possible by many underlying technologies and one of the most important one is Virtualization. Virtualization has been around but as it applied in cloud computing, it makes scheduling a new concept; VM scheduling. VM scheduling is the process of locating a physical machine, in which an incoming VM will be place otherwise known as VM placement. An incoming VM must be placed in the most appropriate physical machine and as the number of physical machine continue to increase, locating the most appropriate PM becomes a global optimization problem (Jiaqi, et al., 2014). Two instances that consume energy and are related to VM include incoming VM placement and VM migration. As cloud users continue to increase, cloud physical machines will be added to respond to user demands. This means that the biggest problem is locating the most appropriate PM to host the many incoming VM requests from users. One of the goals of VM placement is to save energy and in the past, it has been done by shutting down some physical servers that are not in use (Rajeev & Pateriya, 2014). Generally, VM scheduling in cloud centers is becoming a hot topic and
has challenged researchers. Because many factors must be considered while designing a solution, VM scheduling remains a challenge (Corentin, Giovanni, & Fabien, 2012):
Cloud data centers are growing, which creates complexity and have constantly changing configurations.
Data centers are heterogeneous; in terms of performance, energy efficiency and capabilities
Data center must satisfy the many users and the SLA constraints between the cloud provider and clients.
Typical user operations that are related to virtual machines in a cloud includes (Anton & Rajkumar, 2010);
A user reads cloud provider services provisions, like it and signs a SLA. The next thing is requesting a VM in the cloud provider’s PMs. The VM request’s parameters are disk space, Size of memory, CPU and bandwidth.
The requested VM is placed in the most appropriate PM.
User may decide to resize his VM- shrinking or expanding VM parameters. This may require migration if the current PM over qualifies/ under qualifies for new VM. This sometimes involves live migration.
Use may decide to move his VM to a new location, which results to VM migrations
If a user is no longer interested in the cloud provider service, the VM is killed.
Research Gap Research in task scheduling in cloud environment has been done in the past and one of the factors under consideration is energy consumption (Anton & Rajkumar, 2010; Jiaqi, et al., 2014). One of the strategies that were employed in past research was load balancing while scheduling VMs and shutting down PMs that are not in use (Anton & Rajkumar, 2010; Jiaqi, et al., 2014). Most research done with energy efficience has been optimization at hardware level. However, recents sudies have shown that software-driven energy management can increase energy savings. Furthermore, past research concertrated on incoming VM scheduling and forgot that VM migration and VM resizing is a similar task and needs to be optimised too. As cloud platforms continue to become bigger, closer to users and affordable, VM placement and migration, resizing will be frequent tasks in the cloud.
In some cases, strategies used to solve energy consuption problems have been too generic and only applied the concept of task scheduling just because VM scheduling is a task (Raja, Sanchita, & Abhishek, 2014). Besides, past researchers carried research according to their knowledge space and for purposes of VM scheduling, they applied certain aspects, which would not capture all the cloud computing characteristics. This is because of the vastness of cloud computing. As a result, this field remain to have many opportunities and possibilities of perfecting past solutions (Raja, Sanchita, & Abhishek, 2014; Chetan, Satish, & Vishal, 2013). As other fields of computing continue to mature, such as artificial intelligence, computational mathematics and simulation, there are many opportunities of applying them to achieve greater heights in VM scheduling in cloud computing environments. The process of selecting the best PM from the many PMs is shown to a Global Optimization problem (Jiaqi, et al., 2014). Also a number of Global optimization algorithms have been proposed and build and are based on visual cognition (Sun, 2011). Therefore, a solution that emulates how human eyes locate an object from a group by simply observing, will help locate a PM with ease, thus reducing energy consumption.
Problem Statement As cloud computing continues to grow, many businesses and individuals are moving to the cloud. This has transferred the burden of computing resources management to the cloud providers. Processes in a cloud center entails many tasks which consume a lot of energy and as it stands, 3% of global electricity consumptions is as a results of usage in cloud data centers (Rallo, 2014). The increased energy consumption has increased budget allocation to power bills and increase in global CO2 emission. A number of cloud providers such as Google and Apple have tried to utilize renewable energy to reduce carbon footprint but this may not have a global impact (Walsh, 2013). Current solutions are getting overwhelmed as cloud computing continues to grow and getting complex and so there exists a great opportunity to perfect current solutions (Raja, Sanchita, & Abhishek, 2014; Chetan, Satish, & Vishal, 2013). Areas of artificial intelligence is getting mature but no considerable attempts have been made to use them in arresting the situation. By using visual cognition techniques in scheduling tasks in cloud computing environments, especially VM placement, will result in a drop of energy consumption in cloud centers. This is the primary concern of this study.
Aim The aim of the research is to formulate an algorithm, which will facilitate efficient Virtual Machine placement in cloud’s Physical Machine to result to least electrical power consumption. This algorithm will use visual cognition techniques. This way, there will be reduced operational costs of running cloud centers in terms power bills-increased profits as well carbon footprint.
Objectives i.
To review characteristics of current cloud centers and Virtual Machine placement techniques.
ii.
To design energy efficient Virtual Machine placement algorithms using visual cognitive techniques.
iii.
To test the algorithm by simulation using CloudSim cloud Simulator.
iv.
Perform experiments using test data to validate the applicability of the designed algorithm in different conditions.
Research questions i. ii.
What are the techniques used in Virtual Machine placement? How can we design energy efficient Virtual Machine placement algorithms using visual cognitive techniques?
iii.
How can we test the designed algorithm using CloudSim?
iv.
In which way can we perform experiments to validate the applicability of the designed algorithm?
Research Methodology In this research, we will review materials related to cloud computing, green computing, visual cognition and task scheduling in cloud computing. We will then specifically review past models and algorithms that attempted to solve the problem in question. This will assist in equipping the researcher so as to attack the problem in hand with complete understanding. Based on the weaknesses identified from the material analysis, and the unexplored possibilities, a vision-based algorithm will then be designed and tested theoretically – computations. This will constitute our experimental research design. To evaluate the performance of our algorithm, the algorithm will be implemented in Java and tested in CloudSim simulator. During the tests, several physical machines, n, will be created and attempts to place incoming virtual machines will be made- further experiments. The sources of test data will be from cloud computing data banks, random generation as well as configurations on CloudSim using different value combinations to create varied characteristics of cloud centers. The tests will be an evaluation of the algorithm under several different conditions and those predicted in future clouds just to validate the designed algorithm. We choose experimental research design because it yields better results because there is chance to repeat procedures and check results over and over. We also choose CloudSim simulation for three reasons; - 1) Among all cloud simulators, CloudSim bests models experiments that investigates energy efficiency, 2) It is easy to integrate with commonly used compilers such as Eclipse and 3) a simulator rather than real infrastructure provides us with an opportunity to simulate different scenarios on different scales.
Conclusion From the forgoing, it is clear that cloud computing is becoming accepted and we must face its challenges and solve them. One of the challenges identified is task scheduling and virtual machine placement being one of them. Efficient task placement will lead to reduced energy consumption as a result, cloud providers will make good profits due reduced power bills. Carbon print will also come down. If we succeed scheduling a virtual machine successfully using visual cognition techniques, with least energy consumption, it will be a representative solution of all other cloud computing related tasks. Thereby, it goes without saying that positive results from this research
will of great benefits to areas of cloud computing, vision cognition, energy studies and cloud computing business.
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