Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study

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1 85 Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study Amandeep 1, Vandana Yadav 2, Faz Mohammad 3 1,2 Dept. of CSE, Galgotia University, G. Noida 3 Asst. Prof., Dept. of CSE, MAIT, Ghaziabad Abstract Twenty first century is known as the era of technology. In the technology of computing it is an era of cloud computing. Cloud computing is the most recent technology in today's world of computing and its become more popular day by day due to its great feature (resource pooling, rapid elasticity, scalability, efficiency and on demand service). Cloud computing is built on the base of distributed computing, grid computing and virtualization. Cloud computing is defined as a large scale distributed computing paradigm that is driven by economics of scale in which a pool of abstracted virtualized dynamically. 1. INTRODUCTION Cloud computing is a model for delivering information technology services in which resources are retrieved from the internet through web-based tools and applications, rather than a direct connection to a server. Data and software packages are stored in servers. However, cloud computing structure allows access to information as long as an electronic device has access to the web. This type of system allows employees to work remotely. Cloud computing is so named because the information being accessed is found in the "clouds", and does not require a user to be in a specific place to gain access to it. In Gartner s report [2], considered cloud computing as the first among top 10 most important technologies and with a better prospect in successive years by companies and organization and cloud will change to the IT industry. Cloud computing is a term, which involves virtualization, distributed computing, networking, web services and software. A cloud consists of scalable, managed computing power, storage, platform and service are delivered on demand to external customer over the internet. The services are available to user in pay-per-useon demand model. Due to its great feature it is adopted by all type of user like industry, organization and institutional etc. There are many existing issues in cloud computing like load balancing, virtual machine migration, security,energy management and server consolidation etc. the increase in web traffic and different service are increasing day by day making load balancing a big research topic. in this paper we focus in different type of load balancing algorithm. Several elements are such as client, datacenters, and distributed server [3]. 2. DEFINITION OF CLOUD COMPUTING Many definitions are proposed for cloud computing. Here some of them are given here, 1) Cloud is a parallel and distributed computing system consisting of collection of the interconnected and virtual computers that are dynamically provisioned and presented as one or more unified computing resources based on the service-level- agreements (SLA) that are established through the negotiation between the service provider and customer [4]. 2) NIST definition of cloud computing- Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider inter- action [5]. 3) Cloud computing refers to both the applications delivered as services over the internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as software as a service (SaaS), so we use that term. The datacenter hardware and software is what we will call a cloud [6]. Features of Cloud Computing

2 86 1. Resource pooling: The provider s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. 2. Rapid elasticity: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time. 3. Scalability: Cloud computing provides resources and services for users on demand. The resources are scalable over several data centers. In order to achieve a highly scalable system, balancing of the loads when the load increases at a large extent and a cloud user demands more resources online rapidly is very important. 4. On-demand self-service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider. 5. Efficiency: An efficient cloud computing system should work for all the possible configurations of cloud where users are requesting the resources on any extent unknown to the cloud service providers providing the important features like rapid elasticity and high scalability with the much needed fault tolerance. A proper distribution of tasks among the processors can achieve these features for the cloud systems. 6. Dynamic Resource Allocation: Cloud computing systems are allocating the loads across the system either statically or dynamically. A dynamic resource allocation policy proves to be better than the static one to sustain the dynamic requirements of a cloud user. Cloud computing have three service model i.e. IaaS (infrastructure as a service ), PaaS (platform as a service) and SaaS (software as a service) and it have four deployment model i.e. public cloud, private cloud, hybrid cloud and community cloud [6]. Load Balancing and Virtualization: Load Balancing: Any strategy for load distribution among the computational element is known as load balancing. Cloud balancing is a computer networking method to distribute work load across multiple computer or a computer cluster, network link, central processing units, disk drivers or other resources to achieve optimal resource utilization, maximize throughput, minimize response time and avoid overload. Load balancing helps in preventing bottleneck of system. It also avoiding a situation where some of the nodes are heavily loaded while others nodes are ideal or doing very little work. Without load balancing cloud computing would very difficult to manage [7]. Virtualization: Virtualization means something which is not real but gives all the facility of a real. Virtualization is a software implementation of a machine which will execute different program like a real machine. Two type of virtualization found in case of cloud computing. 1. Para Virtualization: Para virtualization means the hardware allow multiple operating systems to run on single machine. It also allows efficient use of system resources such as memory and processor. In para virtualization all the services are not fully available rather the services are provided partially. 2. Full Virtualization: It means a complete installation of one machine is done on another machine. It will result in a virtual machine which will have all the software i.e. present in the actual server. It helps to sharing a computer system among multiple users. It provide emulating hardware on another machine.[8] Both virtualization and load balancing are closely related. Some of the feature of cloud computing system like fast response time, elasticity are achieved by load balancing technique through virtualization. Load Balancing Approach: Static and dynamic two type of load balancing approaches use in cloud computing. 1. Static Approach: This approach is mainly defined in the design or implementation of system. Static load balancing algorithm divides the traffic equivalently between all users. It uses only information about the average

3 87 behavior of the system. These are much simpler and ignore the current state or the load of the node in the system. 2. Dynamic Approach: In this approach considered only the current state of the system during load balancing decision. It is more suitable for widely distributed system such a cloud computing Dynamic approach has two parts Centralized Approach: Only a single node is responsible for managing and distribution within the whole system. Distributed Approach: Each node independently builds its own load vector. Vector collecting load information of other node. All decision is made locally using local load vector. Metrics for Load Balancing: 1. Throughput: - It is used to calculate the all tasks whose execution has been completed. The performance of any system is improved if throughput is high. 2. Fault Tolerance: It means recovery from failure. The load balancing should be a good fault tolerant technique. 3. Migration time: It is the time to migrate the jobs or resources from one node to other nodes. It should be minimized in order to enhance the performance of the system. 4. Response Time: It is the amount of time that is taken by a particular load balancing algorithm to response a task in a system. This parameter should be minimized for better performance of a system. 5. Scalability: It is the ability of an algorithm to perform Load balancing for any finite number of nodes of a system. Existing Load Balancing Algorithm: There are many load balancing algorithm which help to achieve better throughput, improve the response time, high resource utilization and better performance in cloud computing environment [10, 19, 20, 21]. 1. Task Scheduling Based On LB: This algorithm mainly consists two level task scheduling mechanism which is based on load balancing to meet dynamic requirements of users. It obtains high resource utilization. This algorithm achieves load balancing by first mapping tasks to virtual machines and then all virtual machines to host resources.it is improving the task response time.it also provide better resource utilization.[9] 2. Opportunistic Load Balancing: Opportunistic load balancing is to attempt each node keep busy, therefore does not consider the present workload of each computer. Opportunistic load balancing assigns each task in free order to present node of useful.the advantage is quite simple and reach load balance but its shortcoming is not consider each expectation execution time of task, therefore the whole completion time is very poor. 3. Active clustering: Active clustering is used in large scale cloud system. It provides better performance and high resource utilization. In this algorithm same type nodes of the systems are grouped together and they work together in groups. It works like as self-aggregation load balancing technique where a network is rewired to balance the load of the system. Systems optimize using similar job assignments by connecting similar services. it improve the throughput by utilizing the increased system resource. It will degrade as the system diversity increases. 4. Ant colony optimization: Ant algorithm is a multi-agent approach to difficult combinatorial optimization problem. In this algorithm when the request is initiated the ant start its movement. Movement of ant is of two ways: Forward Movement: Forward Movement the ant moving in forward direction continuously moving from one overloaded node to another node and check it is overloaded or under loaded,if ant find an over loaded node it will continuously moving in the forward direction and check each nodes. Backward Movement: If ants find an over loaded node the ant will use the back ward movement to get to the previous node, in the algorithm if ant finds the target node then ant will commit suicide, this algorithm reduced the unnecessary back ward movement. It is excellent in fault tolerance [11]. 5. Shortest Response Time First: The idea of this algorithm is straight forward. In this each process is assigned a priority which is allowed to run. In this equal priority processes are scheduled in FCFS order. The (SJF) algorithm is a special case of general priority Scheduling algorithm. In SJF algorithm is priority is the inverse of the next CPU burst. It means, if longer the CPU burst then lower the priority. The SJF policy

4 88 selects the job with the shortest processing time first. In this algorithm shorter jobs are executed before long jobs. In SJF, it is very important to know or estimate the processing time of each job which is major problem of SJF. 6. MIN-MIN Algorithm: It starts with a set of all unassigned tasks.in this minimum completion time for all tasks is found. Then after that among these minimum times the minimum value is selected. Then task with minimum time schedule on machine. After that the execution time for all other tasks is updated on that machine then again the same procedure is followed until all the tasks are assigned on the resources. The main problem of this algorithm is has a starvation. 7. Honeybee Foraging Behavior: It is a nature inspired Algorithm for self- organization. Honeybee achieves global load balancing through local server actions. The performance of the system is enhanced with increased system diversity. The main problem is that throughput is not increased with an increase in system size. When the diverse population of service types is required then this algorithm is best suited.[12] 8. A Fast Adaptive Load Balancing Method: This algorithm proposed a binary trees tructure that is used to partition the simulation region into subdomains. The characteristics of this fast adaptive balancing method are to be adjusted the workload between the processors from local areas to global areas. the region should be partitioned by using the binary tree mode. There were partition line between the binary tree and the indexes of the cells on the left are smaller that of right and the indexes on the top are smaller than the bottom. Calculate the workload based on the balancing algorithm. This algorithm has a faster balancing speed, less elapsed time and less communication time cost of the simulation procedure. Advantages are Relative smaller communication overhead relative smaller communication overhead, faster balancing speed, and high efficiency. It cannot maintain the topology of cells [13]. 9. Based Random Sampling: It is a distributed and scalable load balancing approach that uses random sampling of the system domain to achieve self-organization these balancing the load across all nodes of system. This algorithm is based on the construction of the virtual graph having connectivity between the all nodes of the system where each node of the graph is corresponding to the node computer of the cloud system. Edges between nodes are two types as Incoming edge and outgoing edge that is used to consider the load of particular system and also allotment the resources of the node. It is very good technique to balance the load [14]. 10. Max-Min Algorithm: Max-Min algorithm is almost same as the min-min algorithm. The main difference is following: In this algorithm first finding out minimum execution times, then the maximum value is selected which is the maximum time among all the tasks on any resources. After that maximum time finding, the task is assigned on the particular selected machine. Then the execution time for all tasks is updated on that machine, this is done by adding the execution time of the assigned task to the execution times of other tasks on that machine. Then all assigned task is removed from the list that executed by the system. 11. Randomized: In this algorithm the process allocation order is maintain for each processor independent of allocation from remote processor. This is static in nature. In this algorithm a process can be handled by a particular node n with a probability p. When all the processes are of equal loaded then this algorithm work well. Problem arises when loads are of different computational complexities. This algorithm is not maintaining deterministic approach. 12. Lock-free multiprocessing solution for LB: It proposed a lock-free multiprocessing load balancing solution that avoids the use of shared memory in contrast to other multiprocessing load balancing solutions which use shared memory and lock to maintain a user session. It is achieved by modifying kernel. This solution helps in improving the overall performance of load balancer in a multicore environment by running multiple load-balancing processes in one load balancer. 13. Heat Diffusion Based Dynamic Load Balancing: In this algorithm, proposed an efficient cell selection scheme and two diffusion based algorithm called global and local diffusion. According to heat diffusion algorithm, the virtual environment is divided into a large no of square cells and each square cells having object and every node in the cell send load to its neighboring nodes in every iteration and the transfer was the difference between the current node to that of neighboring node. it is related to heat diffusion process. the advantages of this algorithm are communication overhead is less, high speed and require little amount of calculation.[15].

5 Compare and Balance: This algorithm is uses to reach an equilibrium condition and manage unbalanced systems load. In this algorithm on the basis of probability, current host randomly select a host and compare their load. If load of current host is more than the selected host, it transfers extra load to that particular node. Then each host of the system performs the same procedure. This load balancing algorithm is also designed and implemented to reduce virtual machines migration time. Shared storage memory is used to reduce virtual machines migration time. 15. Resource Awareness Scheduling Algorithm: It is a combination of Min-Min and Max-Min algorithm and have no time consuming instruction. The time complexity of this algorithm is O(mn2) where m is the no. of resources and n is the no. of task. 16. Connection Mechanism: It is a algorithm based on least connection mechanism. It needs to count the no. of connection for each server dynamically to estimate the load. The load balancers record the connection no of each server. The no. of connection increase when a new connection is dispatch to it and decrease the no when connection finished or timeout happen.[16] 17. Round Robin: In this algorithm all the processes are divided between all processors. In this each process is assigned to the processor in a round robin order. The work load distributions between processors are equal. Different processes have not same job processing time. At many point of time some nodes may be heavily loaded and others remain idle In web servers where http requests are of similar nature and distributed equally then RR algorithm is used.when time quantum is very large then RR Scheduling Algorithm is same as the FCFS Scheduling. and when time quantum is too small then Round Robin Scheduling is known as Processor Sharing Algorithm. 18. Throttled Load Balancing Algorithm: It is completely based on virtual machine. Client first requesting the load balancer to check the right virtual machine which access that load easily and perform the operation that is given by the client.[17] 19. Load Balancing Using Firefly Algorithm: This algorithm is inspired from firefly algorithm. The proposed approach deals with a simulated cloud network with set of requests and servers. The servers are associated with nodes and each node is supplied with some attributes. The attributes are assigned to control the load in each node. It has three levels. 1. Initially a population is generated from the cloud network. 2. Scheduling index calculation 3. The scheduling list is optimized by firefly algorithm. It is efficient in optimizing scheduling by balancing the load.[18] 3. CONCLUSION: In this paper we discuss the load balancing in cloud computing and metrics for load balancing in cloud computing. We also discuss the cloud virtualization. Load balancing helps in proper utilization of resources and improve the performance of system. Load balancing is a major issues in cloud computing. In this paper we examined some existing load balancing algorithm that maintain load balancing and provide better scheduling and resource allocation techniques. Researchers have been done in this area. But still there is need of improvement in the strategy of resource allocation and scheduling algorithm. REFERENCES: [1] Gaochao Xu, Junjie Pang, and Xiaodong Fu A Load Balancing Model Based on Cloud Partitioning for the Public Cloud. IEEE TRANSACTIONS ON CLOUD COMPUTING YEAR 2013 [2] R. Hunter, The why of cloud, DisplayDocument?doc cd=226469&ref= g noreg, 2012 [3] Namrata Swarnkar, Asst. Prof. Atesh Kumar Singh and Dr. R. Shankar A Survey of Load Balancing Techniques in Cloud Computing. Vol. 2 Issue 8, August 2013 [4] Rajkumar Buyyaa and et.al. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,elsevier. [5] NIST: Nist definition of cloud computing [6] Fox et al: Above the Clouds: A Berkeley View of Cloud computing feb 2009 [7] cloud computing bible. [8] Rajwinder Kaur and Pawan Luthra Load Balancing in Cloud Computing. Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC [9] Y. Fang, F. Wang, and J. Ge, A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing, Web Information Systems and Mining, Lecture Notes in Computer Science, Vol. 6318, 2010, pages [10] Ms. Parin. V. Patel, Mr. Hitesh. D. Patel and Asst. Prof. Pinal. J. Patel A Survey On Load Balancing In

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