Efficient Virtual Machine Management Based on Dynamic Workload in Cloud Computing Environment Seema Vahora, Ritesh Patel, Hetal Patel and Sandipkumar Patel Department of Computer Engineering C.S.P.I.T., CHARUSAT, Changa, Gujarat., India Contact author:
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Abstract— In recent times cloud computing is transpiring as a new model for hosting and delivering user services through internet media. Cloud computing offers computing resources in the form of Virtual Machines (VMs) on demand and payment are made on the basis of the amount of resources used by user application.These unique feature attracted more number of users to host their requirements on Cloud Provider which has increased number of VM in data ce nte rs . This creates an issue of proper management of VM such that resources are efficiently utilized. Efficient utilization of resources has realized VM Consolidation which leads to efficient management of VM on as few hosts as possible, switching idle hosts (physical machines) into a power saving mode. Noteworthy research have been done in the area of efficient VM consolidation to reduce power utilization. VM migration is powerful utility to achieve VM Consolidation. But VM migration involves cost of Bandwidth and Resources between two machines. It leads to tradeoff between energy utilized in migration vs energy utilized during workload. Our solution in this paper describes how to reduce trade off by efficiently migrating VM to proper Machine i.e. number of migration can be reduced.
are efficiently utilized. To accomplish above feature of data centers, massive amount of energy consumed and make higher outlays in cloud computing. According to statistics, each data center in world consumes as much energy as 250,000 households on average [13]. And in another report by McKinsey in 2010, the overall assessed power bill for data centers is $11.5 billion and power costs in a typical data center double every five years [17]. Hence, to improve energy efficiency of data centers is one of the most challenging issues with the rapid growth of computing application. For that proper management of VM is required in data centers. Data centers consumes tremendous energy mainly due to the underutilization of resources. So the objectives of cloud computing are to make high use of cloud resources and improve energy efficiency of data centers. The focus of this work is to improve utilization of resources and design VM management techniques for it.
Keywords— Virtual Machine (VM); millions of instruction per second (MIPS); VM management; Energy efficient
I. INT RODUCT ION Cloud computing supplies virtualized resources (storage, bandwidth), development environment to run your application and also provide on demand software as a service on a subscription basis. These services are known as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) in cloud computing world. The significance of the cloud services was emphasized in a recent report from the University of Berkeley as: ‘The main vision of Cloud computing is to provide computing as a service which has the capability to change a large part of the information technology business and making software even more interesting as a service’ [3]. Modern data centers are requested to be able to deal with a different type of applications. Also, data centers allocate resources to different types of application in a flexible manner and also assure that resources
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The subsequent sections are organized as follows: section II presents the related works on VM management techniques in cloud computing; section III presents the tool used for simulation; section IV presents the proposed algorithm; section V shows the experiments setup and results analysis; section VI presents the brief conclusive remarks. II. RELAT ED W ORK With the growing demand of internet based computing in every field large enterprise need to balance cost and efficiency of data centers. It means data center should be able to make high use of available resources. Nathuji and Schwan [11] have proposed power aware approach for virtualized data centers. They divide approach in two parts: local level and global level. At local level they manage guest OS and apply this strategy at global level to decide if there is need for reallocation of VM. However author not proposed specific approach for management of resources at global level.
Kusic et al. [9] have presented an energy and dynamic resources provisioning method using Limited Look-ahead Control (LLC) in heterogeneous virtualized environment. However this method is not appropriate for infrastructure provider as it based on application. Also algorithm complexity is not appropriate for real scenario of cloud computing environment. Kumar et al. [10] have proposed method for dynamic server consolidation. They assume that proposed VM reallocation is remain operational in future for some period of time. Also, they assume that resource utilized by application is follows normal distribution. But as we know that cloud application has more complex resource requirement and they are varying with respect to time so they doesn’t follows simple probabilit y distribution. Beloglazov et al. [2] have implemented an energy aware resource allocation heuristic for VMs consolidation in CloudSim [13]. They divide dynamic VM consolidation (to combine VM on as few hosts as possible) problem into four major steps. First of all they check if host is over-loaded or not. Second, if host is over-loaded then it select one or more VM for migration. Third, selected VM is placed (reallocate) to another suitable host. Finally they check if host is underutilized or not. When host is under-utilized they migrate all VM from host to another host and switched of host. Furthermore, four different VM allocation algorithms are presented to judge over utilized hosts, three VM selection algorithms are proposed to select VMs for migration and one VM placement algorithm to place selected VM to another host. There are different VM management techniques which shown in figure 1. The result shows that prediction based local regression (LR) method for VM allocation and Minimum Migration Time (MMT) method for VM selection are collectively performed better with respect to power and SLA Violations (ESV). But those algorithms have failed to get small VMs’ migrations in virtualized data centers. To solve these problems, novel VM allocation, VM selection and VM placement (reallocation) algorithm is presented and we evaluate the performance of algorithm with the data collected from more than a thousand planetlab [13] VMs, the results illustrate that our algorithms attain a little higher SLA violation, but it minimize energy consumption and less VMs’ migrations than the algorithms presented in [2]. Zhibo Cao et al [18] have implemented energy and SLA aware algorithm for dynamic VM consolidation. They proposed VM allocation to find over utilized host in which they use mean and standard deviation with dynamic safety parameter to find utilization of host at each time frame. Also they proposed VM selection to select VM for migration from over loaded host. In VM selection they use positive correlation algorithm. The positive correlation indicates that two variable X and Y increase or decrease simultaneously in most cases.
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Experiment shows that proposed algorithm perform better than existing one with respect to SLA but little worse performance with respect to energy consumption. Pushtikant Malviya et al [12] have implemented an energy aware resource allocation heuristic for VMs in Cloudsim. They use mean and standard deviation to find upper threshold of utilization to judge number of over utilized host. This strategy is used for re-arrangement of virtual machines in a cloud environment to save the power of data server. Jing Huang et al [8] have proposed an energy and SLA aware algorithm for reallocation of VM on other appropriate host. They proposed best fit host algorithm for reallocation of VM in which they use host which has highest predicted utilization without exceeding the over utilization threshold after VM migration. Also they proposed best fit VM algorithm in which they use concept of dynamic programming 0-1 knapsack problem for each host. They evaluate the performance of algorithm with respect to energy consumption, number of VM migration, number of rebooted hosts, number of host shutdowns and SLA violation. Experiment shows that proposed algorithms are performed better than existing ones in terms of energy consumption but little worse in terms of SLA violation caused by overloaded server. III. TOOL USED FOR SIMULAT ION Cloud application have complex configuration, resources and deployment requirements. Evaluating the application performance in repeatable and controllable manner under different workload models is not easy to accomplish. Cloud computing is interconnected network of a huge number of computing and storage physical machines for providing on-demand services (IaaS, PaaS, and SaaS). Such infrastructures in conjunction with a cooling system may consume massive amounts of electrical power resulting in high operational costs and its release lots of carbon dioxide (CO2) to the environment which directly effect on greenhouse [17]. All these problems need the improvement of effective energy aware techniques at each level of the cloud architecture. There are different cloud simulators are available [4] e.g. CloudSim [13], Cloud Analyst [5], Green Cloud [6], NetworkCloudSim [14], iCanCloud [1], GroudSim [16]. CloudSim used for VM management [15], Cloud Analyst used for geographically distributed area to choose a proper data center for servicing user request and balance the load across data centers [7], Green Cloud is used to find energy consumption at any particular data center components such as link, switch, gateway etc., NetworkCloudSim is used for building networking protocols , iCanCloud used to modify core hypervisor class, GroudSim is used for building and testing scientific application for both green computing and cloud computing.
2015 IEEE International Advance Computing Conference (IACC)
Fig. 1. VM Management T echniques
To overcome the challenges, we use CloudSim. CloudSim is toolkit for resource provisioning and VM management techniques. Cloudsim is a simulation framework it provide the facility for simulating and modeling of cloud environment in repeatable and controllable manner.
TABLE I. Host Type s T ype 1
Cloudsim support for simulation and modeling of huge scale data center and manages cloud resources. For that it provide different management techniques. This generic management techniques can be extended with easy and limited effort. Cloudsim support different types of host and VM configuration which is shown in table 1 and table 2 [13]. We simulate our algorithm with 800 heterogeneous physical nodes. Also we use different configuration for VM which is type of Amazon EC2 instance [13].
T ype 2
HOST CONFIGURATION
Cores
Capacity (MIPS)
Ram(M B)
Storage(GB)
2
1860
4096
1
2
2660
4096
1
T ABLE II. VM Type s T ype 1 T ype 2 T ype 3 T ype 4
Bandwid th (Gbit/s) 1 1
VM CONFIGURATION
Cores
Capacity (MIPS)
Ram(MB)
Storage(GB)
1
2500
870
2.5
1
2000
1740
2.5
1
1000
1740
2.5
1
500
613
2.5
2015 IEEE International Advance Computing Conference (IACC)
Bandwi dth (Mbit/s) 100 100 100 100
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IV. PROPOSED A LGORIT HM Our proposed algorithm mainly divided in three major step first, how to detect pull of overloading host in virtual environments; second, what kind of principles or methods should we follow to select VMs from those overloading hosts for migration and finally, where to place these selected VMs. These three steps leads to proper management of VM such that overall energy used by data center as well as number of VM migration is reduced and utilization of resources is increased. The high level structure of algorithm is given below. Overall Algorithm: Efficient VM Management
Input: No of VMs, No of Hosts, % CPU utilized by VM Output: No of VM migration, Energy consumption For each host do Step 1: Detection of over-utilized hosts Calculate average utilization of host and predicted utilization of host using local regression for each time frame. Calculate the utilization difference between average utilization and predicted utilization. Multiply safety parameter 1.5 with utilization difference called as Pu. If Pu >1 host is over-utilized then Step 2: Selection of migrated VM from over-utilized hosts Select appropriate VMs which has minimum utilization and minimum migration time from that host until that host is not over-utilized Add selected VMs to MigratingVMList Step 3: Placement of VMs on hosts Sort VMs in MigratingVMList by descending order of current CPU Utilization For each VM in MigratingVMList do For each host in AvailableHos tList do If host has sufficient resources for VM then Find host with maximu m utilization If maximum utilization >1 OR maximum utilization
