Load Balancing in Cloud Computing

Transcription

1 Load Balancing in Cloud Computing Dr.V Asha 1, Bharath Kumar 2, Girish V 3 1,2,3 Department of Master of Computer Applications, New Horizon College of Engineering, Abstract - Cloud computing is an emerging technology which provides new drift for computing based on virtualization of resources. With the development of new applications on the cloud leads to increase the load on the servers. Due to the increase in the load, the resources are not utilized efficiently so for that reason load balancing has been introduced. The main goal of load alancing is to balance the load equally among the nodes such that no nodes will be overloaded or under loaded. This paper consists of a comprehensive overview related to load balancing algorithm in cloud computing and to resolve the issue related to the load balancing. Keywords : Cloud Computing, Load Balancing, Clustering, Throughput, Resource Utilization. I. INTRODUCTION Nowadays cloud computing is one of the proven and widelyused technology in the domain of information technology and information technology enabled services. The remarkable features and benefits such as high flexibility, scalability, and reliability of cloud computing technology have led several service providers and research scientists towards shifting to it. Fig. 1 Cloud Computing Scenario Cloud computing provides the resources to the customers as per their need. Let us understand how cloud computing helps in the industry. Suppose the system manager has been given the responsibility to satisfy the requirements of the company employees, such as PC/Software/equipment that they require to perform at their work. If the system manager purchase the assets, a particular individual is required to care about different matters, such as, framework, or equipment require to be setup, or programming to be introduced on person PC or the storage room as indicated by the prerequisite also. This will increase the cost and upkeep. The better answer for reducing the cost is to purchase the assets or framework. A system manager can utilize the cloud administration services to diminish the cost.although Cloud Computing has certain advantages, there are certain issues to deal with such as load balancing among the resources, scheduling of task, VM migration, security and many more. In this paper mainly emphasize on how load balancing among the resources can be taken place efficiently. DOI: /IJRTER VQPYQ 118

2 II. LOAD BALANCING IN CLOUD COMPUTING The main aim of the load balancing is to balance the load efficiently among the nodes in such a way that no nodes will be overloaded and under loaded. There are certain parameters for measuring the efficiency of the load balancing algorithm in Cloud Computing Environment. Fault tolerance: The algorithm must be capable of handlingthe fault tolerance correctly. In case of failure occurs in the one system, load balancing mechanism should not affect to all other system. Throughput: The algorithm make sure that increase in efficiency by executing maximum number of tasks within minimum completion time. Adaptability:The algorithm must be capable of handling the dynamic request of the users and provide allocation of task in lesser amount of time. Generally load balancing are classified into 2 parts which are as follows First is static load balancing and is Dynamic load balancing. A. STATIC LOAD BALANCING ALGORITHM Information about resources A pp lication Task Scheduler Load Information at Compile time Fig. 2 Working of Static Load Balancing. Static load balancing suitable for frameworks having low variations in load. In static load balancing the traffic is isolateduniformly among the servers. This calculation requires an earlier learning of system resources and the performance of the processors is determined at the start of the execution. As static load balancing allocates the task to the workstation where task will be performed by the scheduler based on the load distribution at compile time. However static load balancing algorithms had a downside that tasks can't be moved and its execution to another machine for balancing the load. Different Static load balancing algorithms are as follows 1)Round Robin In round robin algorithm, processes are partitioned between all processors in such a way that the work load between the processors are distributed equally. Moreover, distinctive process does not have similar processing time. At times some of the nodes might be vigorously loaded and others are lightly loaded in the web servers where http request are of comparative nature and conveyed similarly then Round Robin algorithm is utilized. The mathematical model for Round Robin algorithm is given All Rights Reserved 119

3 Create P1, P2; P1 = store ready process P2= store blocked process New process submitted to end of P1 If task time interval finished then Move to end of P1. If I/O request or swapped out request is made by process then Move process from P1 to P2. If I/O operation is completed or ready to move from blocked processes then Move process from P2 to P1. The mathematical model for the round robin algorithm are as follows : Terminologies used are N: Total number of process in ready queue. TATi: Turnaround time for the i th process. WTi:Waiting time for the i th process. BTi : Burst time for the i th process. TQ: Time Quantum. SB(i,j): Sum of the service time received by all the processes that came before process Pi and time quantum for execution until Pi finished it burst time completely. SA(i,j): Sum of the service time received by all the processes that came after process Pi and got time quantum for execution until Pi finished it burst time completely. NTi: Number of turns required for execution by i th process. CS: Total number of context switches AVG (TAT): Average turnaround time for all the processes. AVG (WT): Average waiting time for all the processes. Turnaround time of Round Robin algorithm is: TAT = Bti + i=1 j=1sb(i,j) + n j=i+1sa(i,j) where SB(i,j) = {Nti * TQ if Nti < NTj {BTj SA(i,j) = {(Nt-1) * TQ if Nti < NTj {BTj Nti = Bti/TQ AVG(TAT) = N i=1tat N WTi= TATi- BTi AVG(WT) = N i=1wti N 2) Min-Min Load Balancing It begins with an arrangement of all unassigned tasks. First of all minimum completion time for all tasks are found. The tasks having minimum execution time is firstly chosen. Secondly the execution time for all other tasks is redesigned for that machine. The fundamental issue of Min-Min algorithm is starvation. Min-Min algorithm are given All Rights Reserved 120

4 Procedure Minmin(Task Ti) { Find execution_time for each task Store the execution_completion_time of task Ti in orderqueue { for each task Ti in orderqueue { obtain minimum completiontime from orderqueue; assign task to vm; update the execution_completion_time; } Until orderqueue empty; } International Journal of Recent Trends in Engineering & Research (IJRTER) 3) Map Reduced Based Entity Resolution Model Map Reduced Based Entity Resolution Modelhas been divided into 2 parts that is Map () and Reduce (). Map () function performs the sorting and cleaning with the help of Part () method which results into the partition of the large datasets into the smallerdatasets. Comp() method is used to compare the similar taskand group it using Group() with the help of Reduce().Task overloading is reduced due to the parallel processing of the task using part() method. Input Map () Shuffle Map () Reduce output Map () Reduce output Map () Fig 3: Schematic Overview on Map-Reduce B. DYNAMIC LOAD BALANCING ALGORITHM Dynamic Load balancing algorithms are the algorithms which are used to handle the current request of the clients. Dynamic load balancing does the process while the job is in execution. Jobs are allotted to host or hub. Load at every post is figured (as number of process, structure of node, system data transfer capacity and so on.) All Rights Reserved 121

5 Information about the resources Application Task Scheduler Current request Fig.4 Working of Dynamic Load Balancing. Different Dynamic load balancing algorithms are as follows 1) Equally Spread Current Execution Equally Spread current execution is a dynamic load adjusting calculation, which handles the process with priority. It decidesthe priority by checking the span of the process. This algorithm disperses the load randomly by first checking thesize of the process and afterward exchanging the load to a Virtual Machine, which is lightly loaded. The load balancer spreads the load onto distinctive nodes, and thus, it is known as spread spectrum technique. 2) Throttled Load Balancing Load balancing calculation is based on concept of finding the appropriate virtual machines for doling out a specific occupation. In this algorithm, the job manager has rundown of every single virtual machine, utilizing this ordered show, it allocates the desired work given by client to the fitting machine. As per client request, if the job is well suited for a particular machine on the premise of size and accessibility of the machine, that job is assigned to the appropriate machine. If no virtual machines are accessible to acknowledge the jobs, the job manager queued the request. 3) Ant Bee Colony Optimization Ant Bee colony optimization algorithm works on the behaviour of the real ants. Main purpose of the Ant Colony optimization is to find out the optimized path from source to destination. Ants while searching the food have the special components called as pheromones. Based on this pheromones next ants will follow that same path. The intensity of pheromones consist of various factors such as quality of food source food distance etc [17]. Paths which consist of highest pheromone intensity is considered to be of the shorter distance between the source and destination. Basic algorithm for the Ant bee colony optimization is Step 1: Initialize the pheromone. Step 2: Placing all the ants at the beginning of the VMs. Step 3: Until all the ants have found food (solution) DO. Each ant should follow Do Choose a VM for new task Check for the pheromone intensity End Do End Do Find the best so far Update the All Rights Reserved 122

6 III. BENEFITS OF LOAD BALANCING 1) Scalability : The main advantages of the load balancing algorithm is that any number of servers can be added easily without causing any disturbance and application can be performed smoothly through load balancing the servers in the cloud. 2) Performance: An efficient load balancing helps to provide the cloud services and cloud applications to respond faster compare to the usual completion time. Moreover the execution time also get reduced to the greater extent through efficientcompression techniques, and caching mechanism. 3) Availability: Load balancing mechanism guarantees to provide the services efficiently. In the case of unavailability of the few servers, the load will be further distributed efficiently. 4) Reliability: The reliability of the cloud services are protected by the redundancy of the server through which an application can be hosted at any cloud hub in the world. Even in case of the failure the cloud serving resource will not stop functioning and the services will be redirected to any other cloud location. Round Robin Ant Bee Colony Min Min VM Allocation Dynamicity VM Type Uniformity Parameters Static Homogeneous Waiting Time Challenges Less Resource Utilization Dynamic Heterogeneous Throughput With each iteration the probability changes Static Homogeneous Response time Starvation Equally Spread Current Execution Dynamic Heterogeneous Throughput, Response time Vms are assigned randomly Throttled Load Balancing Central Load Balancing Decision Model Dynamic Homogeneous and Heterogeneous Throughput, Resource Utilization Requires maximum Vm and completion time is high. Static Homogeneous Throughput Provides wrong decision when it entered into the loop. Map Reduce Load Balancing Static Homogeneous Resource Utilization Table I Comparison Of Load Balancing s Requires higher computation time as it has been divided by Part() All Rights Reserved 123

7 IV. OPEN ISSUES RELATED TO LOAD BALANCING Although load balancing algorithms are helpful in balancing the load among the nodes efficiently but there are some open issues that are to be considered at the time of load balancing. Certain issues related to the load balancing are If the user demands will change gradually, which willresults into the decrease in the performance of thesystem. Most of the times the fair distribution of the cloudresources does not exist that results into theunderutilization and overutilization of the nodes. V. LOAD BALANCING CHALLENGES Overhead: It occurs due to more time taken to migratefrom the one VM to another VM or increase in the communication cost. A good load balancing algorithm should reduce the overhead. Performance: It deals with the efficiency of the system. Good performance ensures the satisfaction of the user. Performance includes following metrics: 1) Resource Utilization: A good performance deals with proper utilization of resources among the nodes. it help us to measure whether any node is overloaded or under loaded. 2) Response Time: It represents the time taken to respond by a load balancing algorithm to the user. A lesser respond time denotes good performance of the system. Fault Tolerance: It improves the system in such a way that single point of failure does not affect the whole system. Load balancing algorithm should be designed in such a way that if one of the node fails it should not affect the entire system. VI. PERFORMANCE METRICS FOR LOAD BALANCING In this section, we will consider some of the platforms and simulation tools which can be used for the performance evaluation of the load balancing. Performance Evaluation Metrics Real world Platform Simulation Eucalyptus Open Stack Amazon EC2 Cloudsim Real world Platforms: To execute the load balancing algorithm it basically consists of certain real world platforms which is used for the performance testing. 1) Eucalyptus: It is an open source platform which is used to manage the complexity and heterogeneity of large and distributed infrastructures. It consist of certain components such as cloud controller, cluster controller, and storage controller. Cloud Controller: For administrators, project managers and end users it acts as an entry point into the cloud.eucalyptus can occupy only ne cloud controller in the whole process. Cluster Controller: It basically runs on thehost machine. Cluster controller gathersinformation about the node controller andschedules virtual machine on the All Rights Reserved 124

8 Storage Controller: The main purpose of thestorage controller is to store the persistent dataand past VM termination. Walrus :Walrus is used to create, list,delete buckets or to delete the objects. 2) Open stack: It is an open source cloud computingsoftware that provides infrastructure as a service for public and private cloud. Several components of openstack are Nova, Swift, Keystone, Cinder, Quantum. Keystone: The main purpose of the keystone isused for authentication and authorization ofidentity. It manages the roles like operator,admin, tenants etc. Cinder: It provides the persistent storage in theform of volumes in the Virtual Machine.Cinder provides storage with high availability,fault tolerance. Quantum: It deals with the network services.quantum is responsible for the communicationbetween the interface devices. 3) Amazon EC2: It is a commercial web serviceplatform which provides resources to the customer on the rental basis. EC2 basically consist of storage, processing,web services offered to the customers.ec2 provides a computing environment which helps customers to use web service interfaces to operate different operating system by launching instances. VIII. CONCLUSION The main objective of this paper is to consolidate the existing methodologies for the load balancing in reference with the cloud. This paper mainly addresses the problem of resource allocation and load balancing along with the different techniques in cloud computing has been considered. In this paper we have also discuss about the different challenges related for the development of the efficient load balancing algorithm and also discussed about the pros and cons of the load balancing. The ultimate goal of load balancing in cloud computing is to maximize the profit for cloud service providers and to minimize the cost for the cloud consumers. IX. REFERENCES I. Reena Panwar, Prof. Dr. Bhawna Mallick Load Balancing in Cloud Computing Using Dynamic Load Management IEEE 2015 II. Ravindra A. Vyas, Hardik H. Maheta/Vipul K. Dabhi, H. B. Prajapati Load balancing using process migration III. for linux based distributed system IEEE Shang-Liang Chen, Yun-Yao Chen, Suang-Hong Kuo CLB: A novel load balancing architecture and algorithm for cloud services. IV. Surbhi Kapoor, Dr. Chetna Dabas Cluster Based Load Balancing in Cloud Computing IEEE V. Garima Gupta, Vimal Kr.Kumawat, P R Laxmi, Dharmendra Singh, Vinesh Jain, A Simulation of Priority Based Earliest Deadline First Scheduling for Cloud Computing System, IEEE VI. A.kumar Load Balancing in Cloud Data Center Using Modified Active Monitoring Load Balancer IEEE- All Rights Reserved 125