A Survey Of Load Balancing In Cloud Computing Challenges And Algorithms Pdf


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Metrics details. Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets- overloading and under-loading.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Mohan Published Load Balancing is the one of the most significant parts in distributed environments.

Assorted Load Balancing Algorithms in Cloud Computing: A Survey

Research Scholar, Dept. It provides shared resources, information, software packages and other resources as per client requirements at specific time.

As cloud computing is growing rapidly and more users are attracted towards utility computing, better and fast service needs to be provided. For better management of available good load balancing techniques are required. So those load balancing in cloud becoming more interested area of research. And through better load balancing in cloud, performance is increased and user gets better services. Here in this paper we have discussed many different load balancing techniques used to solve the issue in cloud computing environment.

A Cloud refers to a distinct IT environment that is designed for the purpose of remotely provisioning scalable and measured IT resources [1]. It is a type of computing in which resources are shared rather than owning personal devises or local personal servers which can be used to handle applications on system. The word cloud in cloud computing is used as a metaphor for internet so we can define a cloud computing as the internet based computing in which the different services like storage, servers and application are provided to organizations computers and device using internet[2].

It allows the users to use resources according to the arrival of their needs in real time. Thus, we can say that cloud computing enables the user to have convenient and on-demand access of shared pool of computing resource such as storage, network, application and services, etc.. On pay per use basis. Cloud computing is growing in the real time environment and the information about the cloud and the services it provide and its deployment models are discussed.

Figure 1 illustrating the three basic service layers that constitute the cloud computing. It provides three basic services that are Software as a Service, Platform as a Service and Infrastructure as a Service [2]. The rest of the paper is organized as follows. In section 2 we discussed virtualization of cloud. In section 4 various load balancing techniques are discussed. And in section 5 Challenges of load balancing in cloud computing are explained.

In full virtualization the entire installation of one system is done on other system. Due to this all the software that are present in actual server will also available in virtual system and also sharing of computer system among multiple users and emulating hardware located on different systems are possible. In this type of virtualization, multiple operating systems are allowed to run on a single system by using system resources like memory and the processor VMware software.

Here complete services are not fully available, but partial services are provided. Disaster recovery, migration and capacity management are some salient features of Para virtualization. In static algorithm the traffic is divided evenly among the servers. This algorithm requires a prior knowledge of system resources, so that the decision of shifting of the load does not depend on the current state of system. Static algorithm is proper in the system which has low variation in load.

In dynamic algorithm the lightest server in the whole network or system is searched and preferred for balancing a load. For this real time communication with network is needed which can increase the traffic in the system. Here current state of the system is used to make decisions to manage the load.

On receiving the request service manager divide it into subtasks. After that service manager will assign subtask to service node to execute task. In , B. This is based on session switching at application layer. In round robin, request is sent to the node having least number of connections. RR is enhanced and in CLBDM, the calculation of the connection time between the client and the node is done and if the connection time goes above the threshold then problem is raised.

If a problem is arises, then the connection between the client and the node is terminated and the Task is forwarded to the further node using Round Robin law. In , L. Colb et al [7] introduced the Map Reduced based Entity Resolution load balancing technique which is based on large datasets. In this technique, two main tasks are done: Map task and Reduce task which the author has described. For mapping task, the PART method is executed where the request entity is partitioned into parts.

Map task reads the entities in parallel and process them, so that overloading of the task is reduced. In , J Hu et al. This technique considers the historical data and also the current state of system.

Here, central scheduler and resource monitor is used. The scheduling controller checks the availability of resources to perform a task and assigns the same. Resource availability details are collected by resource monitor. In , J Al-Jaroodi et al. This can also be implemented for load balancing in cloud computing. For example if one server starts from 0 to incremental order than other will start from m to detrimental order independently from each other.

As on downloading two consecutive blocks the task is considered as finished and assigned next task to server. Because of reduction in network communication between client and node network overhead is reduced.

In , K. Nishant et al [10] introduced a static load balancing technique called Ant Colony Optimization. In this technique, an ant starts the movement as the request is initiated. This technique uses the Ants behavior to collect information of cloud node to assign task to the particular node. The ant moves in forward direction from an overloaded node looking for next node to check whether it is an overloaded node or not.

Now if ant find under loaded node still it move in forward direction in the path. And if it finds the overloaded node then it starts the backward movement to the last under loaded node it found previously. In the algorithm [8] if ant found the target node, ant will commit suicide so that it will prevent unnecessary backward movement. In , T. Yu Wu et al. This technique works on integration of de duplication and access point optimization.

To calculate optimum selection point some parameter are defined: hash code of data block to be downloaded, position of server having target block of data, transition quality and maximum bandwidth.

Another calculation parameter to find weather connection can handle additional node or is at busy level B a , B b or B c. B a denote connection is very busy to handle new connection , B b denotes connection is not busy and B c denotes connection is limited and additional study needed to know more about connection.

Mondal et al [12] have proposed a load balancing technique called Stochastic Hill Climbing based on soft computing for solving the optimization problem. This technique solves the problem with high probability. It is a simple loop moving in direction of increasing value which is uphill. And this make minor change in to original assignment according to some criteria designed. It contains two main criteria one is candidate generator to set possible successor and the other is evaluation criteria which ranks each valid solution.

This leads to improved solution. There are some qualitative metrics that can be improved for better load balancing in cloud computing [14][15]. Throughput: It is the total number of tasks that have completed execution for a given scale of time. It is required to. Associated Overhead: It describes the amount of overhead during the implementation of the load balancing. Fault tolerant: We can define it as the ability to perform load balancing by the appropriate algorithm without.

Every load balancing algorithm should have good fault tolerance approach. Migration time: It is the amount of time for a process to be transferred from one system node to another node for.

Response time: In Distributed system, it is the time taken by a particular load balancing technique to respond. Resource Utilization: It is the parameter which gives the information within which extant the resource is utilized.

Scalability: It is the ability of load balancing algorithm for a system with any finite number of processor and. Performance: It is the overall efficiency of the system. If all the parameters are improved then the overall system.

In this paper, we have surveyed various load balancing techniques for cloud computing. The main purpose of load balancing is to satisfy the customer requirement by distributing load dynamically among the nodes and to make maximum resource utilization by reassigning the total load to individual node.

This ensures that every resource is distributed efficiently and evenly. So the performance of the system is increased. We have also discussed virtualization of cloud and required qualitative matrix for load balancing. Radhika Pathak would like to thank thesis guide Prof.

Sandeep Sahu for his great effort and instructive comments in this paper work. Wang, J. Tao, M. Sotomayor, B. Montero, IM. Llorente, and I. Wang, S-C. Yan, W-P.

A Survey on Load Balancing Techniques in Cloud Computing

Emerging cloud computing technology is a big step in virtual computing. Cloud computing provides services to clients through the internet. Cloud computing enables easy access to resources distributed all over the world. Increase in the number of the population has further increased the challenge. The main challenge of cloud computing technology is to achieve efficient load balancing.

A Systematic Review on Load Balancing Issues in Cloud Computing

December 5, Journal article Open Access. Amandeep Kaur ; Pooja Nagpal. Since its inception, the cloud computing paradigm has gained the widespread popularity in the industry and academia.

Research Scholar, Dept. It provides shared resources, information, software packages and other resources as per client requirements at specific time. As cloud computing is growing rapidly and more users are attracted towards utility computing, better and fast service needs to be provided. For better management of available good load balancing techniques are required.

A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms

5 Comments

Tiago L.
17.05.2021 at 23:21 - Reply

Inthis paper, we investigate the different algorithms proposed toresolve the issue of load balancing and task scheduling in CloudComputing. We.

Yolette C.
19.05.2021 at 06:28 - Reply

at run-time [5]. Some static load balancing algorithms are Min–Min, Min–Max, and Round. Robin algorithm.

Fatima S.
19.05.2021 at 12:07 - Reply

Load balancing algorithms were investigated heavily in various environments; however, with Cloud environments, some additional challenges are present and​.

Riley M.
20.05.2021 at 07:00 - Reply

Tremendous advantages of virtualization and cloud computing innovations have invigorated the Information and Communication Technology sector towards embracing cloud computing.

Lorna V.
27.05.2021 at 03:58 - Reply

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