LOAD BALANCING TECHNIQUE IN CLOUD COMPUTING ENVIRONMENT

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 12, December 2017, pp , Article ID: IJMET_08_12_057 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed LOAD BALANCING TECHNIQUE IN CLOUD COMPUTING ENVIRONMENT Ramesh A Assistant Professor, Dept of CSE, Vardhaman College of Engineering, Hyderabad Raghunadha Reddy T Associate Professor, Dept of IT, Vardhaman College of Engineering, Hyderabad Neeraja K Professor, Dept of IT, MLR Institute of Technology, Dundigal, Hyderabad ABSTRACT In general, some of the resources abundantly overloaded by the tasks or workload in computing environments. Therefore there is a need of shifting load of one resource to another. In this context, three general operations need to be performed such as load balancing, resource discovery and workload migration. Load balancing tests the workload of all the resources. Resource discovery operation discovers the suitable resource in the list of resources which is having less workload. Workload Migration shifts the workload to identified resource in the resource discovery operation. It was proven that the system obtained better performance when these three operations completed in less time. The load balancing algorithms offer possibilities for improving the performance of large scale applications and computing systems by redistributing the workloads on different resources and guaranteed that to avoid the possibility of overload, maximizing utilization of resources and minimization of response time. The existing algorithms failed to distribute the workload among the resources properly. In this work, a new load balancing algorithm is proposed to solve the problems of load distribution and results shows that our algorithm performs better than most of the existing algorithms. Key words: Cloud Computing, Load Balancing Algorithms, Static LBA, Dynamic LBA, Load Balancing Metrics. Cite this Article: Ramesh A, Raghunadha Reddy T, Neeraja K, Load Balancing Technique in Cloud Computing Environment, International Journal of Mechanical Engineering and Technology 8(12), 2017, pp INTRODUCTION Cloud computing is a used to establish a communication between multiple computers and provides on-demand services to the users through networks. Various researchers and studies proposed different types of techniques and different types of solutions to the problems of editor@iaeme.com

2 Ramesh A, Raghunadha Reddy T, Neeraja K cloud computing environment based on the fundamental ideas derived from grid computing and distributed computing. In cloud computing, there no need of knowing the technology implemented in cloud to users and the web browsers access the data that were stored in the servers. Cloud computing provides easy access to remote services through internet. There are many types of cloud services such as Cloud Software as a Service (SaaS), Cloud Platform as a Service (PaaS) and Cloud Infrastructure as a Service (IaaS). Cloud Software as a Service (SaaS) is the capability of exploiting the provider's applications which are running on a cloud infrastructure. The provider is responsible for managing the underlying cloud infrastructure. Cloud Platform as a Service (PaaS) is the capability given to the consumer for deploying acquired applications onto the cloud infrastructure using tools supported by the provider. Cloud Infrastructure as a Service (IaaS) is the capability given to the consumer to get storage, networks, and other computing resources that the consumer can deploy, which includes any software, whether operating system or application. The clouds were deployed in different models such as private cloud, public cloud, community cloud and hybrid cloud. In private cloud, the infrastructure of the cloud can be only operated for an organization, and it can be managed by the organization itself or by a third party. In public cloud the infrastructure is available to the public and is owned by a certain organization that sells services. In community cloud model, the cloud infrastructures are shared between certain organizations, which support a certain community that has shared concerns. In hybrid cloud model, two or more clouds (private, community, or public) compose the cloud infrastructure. This paper is organized in 6 sections. The existing work in cloud computing was explained in section 2. The problem of load balancing was described in section 3. Section 4 presents various load balancing algorithms proposed by the different researchers in cloud computing. The proposed load balancing algorithm was explained in section 5. The conclusions of this work were described in section LITERATURE REVIEW Several types of research were carried out on the area of cloud computing including load balancing, resource scheduling and resource provisioning. Rathore et al., explained [1] a detailed classification based on analysis of existing approaches in grid and different parameters such as research focus, compared model, contribution, gap, strength, suitability for usage in a dynamic grid environment. They also proposed a load balancing technique along with migration of tasks and also discussed the research gaps in existing work. Kalra et al., presented [2] an analysis of different scheduling algorithms for grid and cloud environments depending on five major meta heuristic techniques including genetic algorithm, ant colony optimization, particle swarm optimization, BAT algorithm and league championship algorithm. They also explained comparison of different algorithms when these techniques were applied. Mao et al., presented [3] a mechanism that dynamically scales cloud infrastructure up and down based on deadline and budget information. The proposed mechanism scales using a virtual machine (VM) that takes into consideration the performance and budget of the application. In terms of the performance, the mechanism provides enough VM instances to finish submitted jobs within the defined deadline. In terms of budget, the mechanism runs VM types 4 based on the applications needs. For example, if the submitted job does not need a huge processor, the mechanism will provide a small VM type instead of a large one, and vice versa editor@iaeme.com

3 Load Balancing Technique in Cloud Computing Environment Dean. et. al proposed [4] a programming model called MapReduce which is more closely relates to this project. MapReduce is a software framework which is introduced by Google and it supports distributed computing on large data sets on computer clusters. MapReduce processes data that is distributed across servers in a cluster using three operations. The first is called Map, which is a set of tasks that process in parallel by each node within a cluster separated from other nodes within that cluster. Users determine a map function, which processes a key/value pair to generate a set of intermediate key/pair values. Every map task is assigned a part of the input file that is called a split. Each split has a single HDFS block by default. The second involves data that is distributed across all nodes within the cluster, and the third, known as reduce, is a set of tasks that each node executes in parallel. Here, the values that are associated with same key are merged. Venugopal et. al used [5] Session Initiation Protocol (SIP) features, in addition to using the Amazon Elastic Compute Cloud service (EC2), to implement a mechanism which scales a VoIP-based call center to respond to emergency calls. They developed a control system that dynamically scales up or down according to the increasing or decreasing call volumes. Their results show that the control system was able to respond to increasing call volumes by providing additional severs as needed. 3. LOAD BALANCING IN CLOUD COMPUTING Load balancing has been a major issue for cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by providing the resources to cloud on-demand of users basis. By implementing appropriate scheduling criteria load balancing may prioritize users. Fig. 1 shows the functionality of the load balancer in cloud environment Figure 1 Block Diagram of a Load Balancer in cloud architecture The needs of load balancing are to distribute local workload to all the nodes of cloud in efficient way, to continue the provisioning of services in case the system fails, to increase user satisfaction, to enhance the overall performance of the system, to make response time lesser and to achieve optimized resource utilization. Several measures were used by the researchers to evaluate the load balancing algorithms in cloud computing environment. Some of the measures are response time, resource utilization, scalability, throughput, fault tolerance, overhead, migration time, performance and energy consumption. Response Time is the amount of time taken by a load balancing editor@iaeme.com

4 Ramesh A, Raghunadha Reddy T, Neeraja K algorithm to allocate a task to a server in a distributed system. The response time must be less. Resource Utilization measures the efficiency of load balancing algorithm to utilize the resources in effective way. Scalability is good means the load balancing algorithm able to balance a system with any number of nodes when the number of tasks was increased. Throughput is used to compute the number of tasks completed their execution in a specified time. To maintain load balancing and improvement of system performance, the throughput should be high. The load balancing algorithm was able to maintain a uniform load balancing in case of server failures or link failures. In order to improve system performance and user satisfaction, a good load balancing algorithm supports fault tolerant techniques. Overhead measure determines the amount of burden experienced by the system while implementing load balancing algorithm. This overhead is composed due to the movement of tasks between servers, inter-process and inter-processor communication of tasks. This metric should be less for good load balancing technique. Migration time is the time taken by the load balancing algorithm to shift the resources or tasks from one node to another. In order to increase the efficiency of a system, this metric should be less. Performance of a good load balancing algorithm is high when it is capable of improving the efficiency of a system at reasonable cost. Energy Consumption computes the energy consumption of all the resources in a system. Good load balancing algorithm maintains same energy consumption of all resources. 4. LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING The load balancing algorithms were divided into static and dynamic models based on the resource utilization and system topology. Static models collect prior information about the resources and requirements of applications. These models have no way to change the environment and requirements of application in the middle of the execution of applications. In dynamic models, the load balancer was able to modify the application requirements during run time, redistribution of tasks between resources and load adjustment Static LBA Round Robin LBA (RRLBA) In the RRLBA [6] a time slot is allotted to each process to be executed and it follows in a ring manner and in addition, a balance technique is followed in order to balance the process in a group till all processes completes their task. The processes are executed in round robin fashion till the all processes complete their task Shortest Job Scheduling Algorithm The shortest job is selected first in this algorithm [7]. This approach completes execution of shortest jobs first to utilize the resources completely for longer jobs. Shortest job has advantage of having shorter waiting time Min-Min Load Balancing In this approach all the information related to the job is available in prior to its implementation. Min-Min algorithm [8] starts with a set of jobs waiting in a queue. Firstly, time required to complete a task is estimated and then job with minimum execution time is selected for execution. The job with the shortest execution time is executed first and it may happen that some jobs experience starvation Two-Phase (OLB + LBMM) Load Balancing Algorithm To achieve better executing efficiency, the author in [9] has merged Opportunistic Load Balancing (OLB) and Load Balance MinMin (LBMM) scheduling algorithms. The OLB editor@iaeme.com

5 Load Balancing Technique in Cloud Computing Environment algorithm puts each and every node in working condition so as to achieve goal of cloud computing whereas LBMM scheduling algorithm is used to minimize the execution time of the tasks on node which reduces overall completion time. Combining these two algorithms help achieve efficient utilization of all resources and improves performance efficiency of the network of multiple processors as whole Dynamic Load Balancing Algorithms Fuzzy Active Monitoring Load Balancing (FAMLB) A Load Balancing algorithm is proposed by Srinivas Seth et al. [10] based on fuzzy logic. The processor speed and load on virtual machine are two main parameters of this algorithm. The authors in [11] have introduced a dynamic load balancing algorithm known as fuzzy Active Monitoring Load Balancer (FAMLB) in which bandwidth usage, memory usage, disk space usage and virtual machine status are additional parameters considered Throttled Load Balancing The author in [12] has proposed an algorithm where client initiates the process by requesting the load balancer to find a suitable virtual machine to process the incoming tasks. There are multiple instances of virtual machine working in Cloud Computing environment handling various types of requests. As per client s request, the load balancer searches to find a group ready to process the request to be assigned it Honeybee Foraging Behavior A load balancing algorithm proposed in [13] performs similar to honey bees in finding and reaps their food. In dynamic load balancing, the services are assigned dynamically as per users changing demands. The virtual servers are grouped to form cluster wherein each virtual server has its own virtual service queue. Each server also estimates a profit or reward corresponding to amount of time that the CPU spends on the processing of a request Active Clustering This load balancing algorithm forms various groups of similar jobs and then does group wise execution, thus enhances the performance. This algorithm performs poorly in increasing diversity in system as described in [14]. A node initiates the process of selecting a node called matchmaker node from its neighbours based on certain criteria. The processes of a group are executed one by one till all processes get executed. The match maker algorithm makes connection between matchmaker node and its neighbor similar to initial node. After processing, matchmaker node disconnects itself from the initial node Biased Random Sampling The author M. Randles et al. [15] has proposed scheme which dynamically on random basis samples system data to acquire self organization and this way it balances the load in all system of node. Here a virtual graph is made showing the connectivity of those nodes showing load on the server regarding job execution and completion time in the network. Whenever a node completes a job it deletes an incoming edge also frees the resources. According to information from random samples the jobs are added or deleted. The process tarts at any one node and in each step a neighbor is selected randomly and the node added in last is selected for assigning the load. Alternatively, a node for load allocation, is selected based on certain criteria such as computing efficiency etc.. The other way is to select a node for load allocation which is under loaded. The load balancing scheme performs in fully decentralized manner and thus making it appropriate for large network systems like in a cloud editor@iaeme.com

6 Ramesh A, Raghunadha Reddy T, Neeraja K 4.3. Challenges of Load Balancing in Clod Computing With a vast and extensive discussion on various load balancing algorithms proposed for cloud. It is found that research particularly in cloud load balancing is still in developing stage and some scientific challenges remain unsolved some of them are discussed below Automated Service Provisioning Elasticity is important feature of cloud computing in which resources are dynamically assigned or released to cloud node as per demand of clients automatically. This is very important issue for implementing load balancing and demands for much more advanced techniques Virtual Machines Migration Virtualization, a very important feature of cloud where in, the whole machine seems to be a set of files and to unload a heavily loaded machine, its files (load) are moved from one virtual machine to another physical machines. This distributes the load in a data-centre or set of datacentres in cloud. Thus, dynamically distributing the load during the execution state may cause bottlenecks in Cloud computing systems so must be efficiently implemented [16] Energy Management Widely adopted technology of cloud computing is for the economy of scale. Energy saving is a challenging issue which advocates global economy. Load balancing algorithms should use resources efficiently to optimize energy usage and complex algorithm should be avoided and is a challenge to the algorithm designer [17] Stored Data Management In previous years data storage across the network has exponentially boomed. With the emergence of cloud technology, the commercial sector private or public is demanding (daas) data storage as service to store data for their individuals and the management. Thus management of data storage becomes a major challenge for cloud computing. Distributing data to the cloud for optimizing computing storage while managing fast access is the todays challenge [18] Emergence of Small Data Centres for Cloud Computing Small data centres are cheaper, beneficial, consume lesser energy than large data centre. Small service providers provide cloud services leading towards geo-diversity computing. To ensure reasonable response time with optimal distribution of resources is a challenge to scientific community. 5. PROPOSED LOAD BALANCING TECHNIQUE In this work, a new technique namely, Priority based Preemptive and Distributive Load Balancing (PPDLB) is proposed to balance the load in cloud computing environments. In this technique, four modules such as cloud status, distribution, priority and preemption were used to control the burden of the cloud computing environments. The first module cloud status module, it checks the status of the cloud virtual machines. This module maintains a VM status table to store the information of all the virtual machines in the cloud computing environment. This table contains one record for each virtual machine. This record contains the information of the name of the virtual machine, the capacity in terms of speed, how many users are connected to the VM, currently which task is in execution and what are the tasks are waiting for that virtual machine. Every virtual machine maintains a fixed length queue to store the requests of different users. This module maintains a program which updates the status of each virtual machine in a VM status table. This program editor@iaeme.com

7 Load Balancing Technique in Cloud Computing Environment continuously checks the status of the VMs and if it found any heavy workload for VMs, then it conveys the same information to other modules to solve the problem. The distribution module, distribute the task to different servers, when the size of the task is large or the completion time of the task more. This module listens the instructions of the cloud status module and act accordingly. Priority module finds the priority of the tasks based on the size of the task, completion time of the task, preference of the user, importance of the task etc. This module If a new task is identified by the load balancer, first it checks the priority of the task, if high priority task is identified then the load balancer search in the VM status table to know which virtual machines are free and which machines are executing less priority tasks. Preemptive module is used to forcibly remove the task from the execution of a virtual machine. In priority module, if no virtual machine is free then the module calls the preemptive module to preempt the tasks which are having less priority. In this work, a CloudSim frame work used to test the efficiency of our technique using four virtual machines. The load balancing metrics were tested on CloudSim using our technique. It was observed that our technique performance was good. Our technique achieved more throughputs by using concept of distributed, priority and preemptive. Response time is also less in this approach, because we allocate the VM immediately to the tasks which are having high priority. Resource utilization is also more, because it uses all the resources by dividing the task into multiple sub tasks, if the task need more time to finish or if it is a large size task. Scalability is more by dividing. Priority and preemption techniques we can handle the more load also with available virtual machines. Fault tolerance also more in this technique, because we maintain one centralized table which contains the details of currently executed tasks in all virtual machines. If one of the machines fails also we can allocate that task to other systems by verifying VM status table. There is a secondary table to store the VM status table information periodically to avoid the problem of failure in VM status table. Overhead is also less because if more complex task came it is distributed to other virtual machine which is free. 6. CONCLUSIONS The load balancing techniques improve the performance of the cloud computing environment by distributing the load to all the VMs in that cloud. In this work, a new technique was proposed and tested on CloudSim framework with four virtual machines. It was observed that our technique efficiency is good with respect to the measures of load balancing and existing load balancing algorithms. REFERENCES [1] Rathore,N.,Chana,I.,2014.Loadbalancingandjobmigrationtechniquesingrid:a survey of recent trends. Wirel. Pers. Commun.79(3), [2] Kalra, M.,Singh,S.,2015.A review of meta heuristic scheduling techniques in cloud computing. Egypt. Inform.J.16(3), [3] M. Mao; J. Li; M. Humphrey. Cloud auto-scaling with deadline and budget constraints, Grid Computing (GRID), th IEEE/ACM International Conference, pp.41-48, Oct [4] Dean, J. and Ghemawat, S., Mapreduce: simplified data processing on large clusters, Commun. ACM, 51, 1, pp , editor@iaeme.com

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