ISSN 2230- 9373
Volume-VIII, Issue-1
January-March, 2018
Load Balancing Techniques in Cloud Computing - A Review
Approach
K. S. Chaudhury Dr. S. Pattanaik Dr. A.R. Routray Dr. H.S. Moharana
Abstract-
Cloud Computing is an emerging area in the field of information technology. Load balancing poses as one of the main challenges in cloud computing- a technique required to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. Load balancing techniques help in optimal utilization of resources in enhancing the performance of the system. The goal of load balancing is to minimize the resource consumption reducing energy consumption. This determines the need of new metrics, energy consumption for energy-efficient load balancing in cloud computing. This paper focused on the concept of load balancing techniques in cloud computing, the existing load balancing techniques and also discusses the different qualitative metrics or parameters like performance, scalability, overhead etc.
Keywords: Load Balancing, Green Computing, Dynamic Load Balancing, Parameters
1.
INTRODUCTION
Cloud computing provides on-demand hosted computing resources and services over the Internet on a pay-per-use basis. It is currently becoming the favored method of communication and computation over scalable networks due to numerous attractive attributes such as high availability, scalability, fault tolerance, simplicity of management and low cost of ownership. Due to the huge demand of cloud
computing, efficient load balancing becomes critical to ensure that computational tasks are evenly distributed across servers to prevent bottlenecks. The aim of this review paper is to understand the current challenges in cloud computing, primarily in cloud load balancing using static algorithms and finding gaps to bridge for more efficient static cloud load balancing in the future. We believe the ideas suggested as new solution will allow researchers to redesign better algorithms for better functionalities and improved user experiences in simple cloud systems. This could assist small businesses that cannot afford infrastructure that supports complex & dynamic load balancing algorithms.
of the system occurring due to load imbalance. When one or more components of any service fail, load balancing helps in continuation of the service by implementing fair-over, i.e. in provisioning and de-provisioning of instances of applications without fail. It also ensures that every computing resource is distributed efficiently and fairly [3]. Consumption of resources and conservation of energy are not always a prime focus of discussion in cloud computing. However, resource consumption can be kept to a minimum with proper load balancing which not only helps in reducing costs but making enterprises greener [4]. Scalability which is one of the very important features of cloud computing is also enabled by load balancing. Hence, improving resource utility and the performance of a distributed system in such a way will reduce the energy consumption and carbon footprints to achieve Green computing [5] [6].
2.
LOAD BALANCING
Fig.1 An example of Cloud Load Balancing [4]
Load balancing is the process of improving the performance of the system by shifting of workload among the processors. Workload of a
machine means the total processing time required to execute all the tasks assigned to the machine. Load balancing is done so that every virtual machine in the cloud system does the same amount of work throughout therefore increasing the throughput and minimizing the response time. Load balancing is one of the important factors to uplift the working performance of the cloud service provider. Balancing the load of virtual machines uniformly means that anyone of the available machines is not idle or partially loaded while others are heavily loaded. One of the crucial issues of cloud computing is to divide the workload dynamically. The benefits of distributing the workload includes increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction.
2.1 WHY LOAD BALANCING?
LOAD BALANCING:
The goals of load balancing are:
To improve the performance substantially.
To have a backup plan in case the system fails even partially. To maintain the system stability. To accommodate future modification in
the system.
2.2 CATEGORIES OF LOAD
BALANCING ALGORITHMS
Depending on initiation of the process, load balancing algorithms can be of three categories:
Sender Initiated: If the load balancing algorithm is initialized by the sender Receiver Initiated: If the load balancing
algorithm is initiated by the receiver Symmetric: It is the combination of both
sender initiated and receiver initiated.
Depending on the current state of the system, load balancing algorithms can be divided into two categories:
Static Algorithm: Static algorithms divide the traffic equivalently between servers. By this approach the traffic on the servers will be disdained easily and consequently it will make the situation more imperfectly. This algorithm, which divides the traffic equally, is announced as round robin algorithm. However, there were lots of problems appeared in this algorithm. Therefore, weighted round robin was defined to improve the critical challenges associated with round robin. In this algorithm each servers have been assigned a weight and according to the highest weight they received more connections. In the situation that all the weights are equal, servers will receive balanced traffic [8].
Dynamic Algorithm: Dynamic algorithms designated proper weights on servers and by
searching in whole network a lightest server preferred to balance the traffic. However, selecting an appropriate server needed, real time Communication with the networks, will lead to extra traffic added on system. In comparison between these two algorithms, although round robin algorithms based on simple rule, more loads conceived on servers and thus imbalanced traffic discovered as a result [8]. However; dynamic algorithm predicated on query that can be made frequently on servers, but sometimes prevailed traffic will prevent these queries to be answered, and correspondingly more added overhead can be distinguished on network.
2.2.1 POLICIES OR STRATEGIES IN DYNAMIC LOAD BALANCING
The different policies in dynamic load balancing are:
Transfer Policy: The part of the dynamic load balancing algorithm which selects a job for transferring from a local node to a remote node is referred to as Transfer policy or Transfer strategy.
Selection Policy: It specifies the processors involved in the load exchange (processor matching).
Location Policy: The part of the load balancing algorithm which selects a destination node for a transferred task is referred to as location policy or Location strategy.
Information Policy: The part of the dynamic load balancing algorithm responsible for collecting information about the nodes in the system is referred to as Information policy or Information strategy. Load estimation Policy: The policy which
Process Transfer Policy: The policy which is used for deciding the execution of a task that is it is to be done locally or remotely is termed as Process Transfer policy.
Priority Assignment Policy: The policy that is used to assign priority for execution of both local and remote processes and tasks is termed as Priority Assignment Policy. Migration Limiting Policy: The policy that is
used to set a limit on the maximum number of times a task can migrate from one machine to another machine [16] 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 Linux kernel. This solution helps in improving the overall performance of load balancer in a multi-core environment by running multiple load-balancing processes in one load balancer. Scheduling strategy on load balancing of virtual machine resources, Hu et al. [9] proposed a scheduling strategy on load balancing of VM resources that uses historical data and current state of the system. This strategy achieves the best load balancing and reduced dynamic migration by using a genetic algorithm. It helps in resolving the issue of load-imbalance and high cost of migration thus achieving better resource utilization. Central load balancing policy for virtual machines, A. Bhadani et al. [10] proposed a Central Load Balancing Policy for Virtual Machines (CLBVM) that balances the load evenly in a distributed virtual machine/cloud computing environment. This policy improves the overall performance of the system but does not consider the systems that are fault-tolerant.
Load Balancing strategy for Virtual Storage (LBVS): H. Liu et al. [11] proposed a load balancing virtual storage strategy (LBVS) that provides a large scale net data storage model and Storage as a Service model based on Cloud
Storage. Storage virtualization is achieved using an architecture that is three-layered and load balancing is achieved using two load balancing modules. It helps in improving the efficiency of concurrent access by using replica balancing further reducing the response time and enhancing the capacity of disaster recovery. This strategy also helps in improving the use rate of storage resource, flexibility and robustness of the system. A Task Scheduling Algorithm Based on Load Balancing, Fang et al. [12] discussed a two-level task scheduling mechanism based on load balancing to meet dynamic requirements of users and obtain high resource utilization. It achieves load balancing by first mapping tasks to virtual machines and then virtual machines to host resources thereby improving the task response time, resource utilization and overall performance of the cloud computing environment.
Honeybee Foraging Behavior: This
the bees show in their waggle dance. One measure of this reward can be the amount of time that the CPU spends on the processing of a request. The dance floor in case of honey bees is analogous to an advert board here. This board is also used to advertise the profit of the entire colony. Each of the servers takes the role of either a forager or a scout.
Biased Random Sampling: M. Randles et al. [14] investigated a distributed and scalable load balancing approach that uses random sampling of the system domain to achieve self-organization thus balancing the load across all nodes of the system. Here a virtual graph is constructed, with the connectivity of each node (a server is treated as a node) representing the load on the server. Each server is symbolized as a node in the graph, with each in degree directed to the free resources of the server. Regarding job execution and completion,
Whenever a node does or executes a job, it deletes an incoming edge, which indicates reduction in the availability of free resource. After completion of a job, the node creates an incoming edge, which indicates an increase in the availability of free resource. The addition and deletion of processes is done by the process of random sampling. The walk starts at any one node and at every step a neighbor is chosen randomly. The last node is selected for allocation for load.
Alternatively, another method can be used for selection of a node for load allocation, that being selecting a node based on certain criteria like computing efficiency, etc. Yet another method can be selecting that node for load allocation which is under loaded i.e. having highest in degree. If b is the walk length, then, as b increases, the efficiency of load allocation increases. We define a threshold value of b, which is generally equal to log n experimentally. A node upon receiving a job, will execute it only
if its current walk length is equal to or greater than the threshold value. Else, the walk length of the job under consideration is incremented and another neighbor node is selected randomly. When, a job is executed by a node then in the graph, an incoming edge of that node is deleted. After completion of the job, an edge is created from the node initiating the load allocation process to the node which was executing the job. Finally what we get is a directed graph. The load balancing scheme used here is fully decentralized, thus making it apt for large network systems like that in a cloud.
real-time Massively Multiplayer Online Games (MMOG). This algorithm after receiving capacity events as input, analyzes its components in context of the resources and the global state of the game session, thereby generating the game session load balancing actions. It is capable of scaling up and down a game session on multiple resources according to the variable user load but has occasional QoS breaches.
The different qualitative metrics or parameters that are considered important for load balancing in cloud computing are discussed as follows:
Throughput: The total number of tasks that have completed execution is called throughput. A high throughput is required for better performance of the system.
Associated Overhead: The amount of overhead that are produced by the execution of the load balancing algorithm. Minimum overhead is expected for successful implementation of the algorithm. Fault tolerant: It is the ability of the algorithm to perform correctly and uniformly even in conditions of failure at any arbitrary node in the system. Migration Time: The time taken in migration or
transfer of a task from one machine to any other machine in the system. This time should be minimum for improving the performance of the system.
Response Time: It is the minimum time that a distributed system executing a specific load balancing algorithm takes to respond.
Resource Utilization: It is the degree to which the resources of the system are utilized. A good load balancing algorithm provides maximum resource utilization.
Scalability: It determines the ability of the system to accomplish load balancing algorithm with a restricted number of processors or machines. Performance: It represents the effectiveness of the system after performing load balancing. If all the above parameters are
satisfied optimally then it will highly improve the performance of the system.
3.
LOAD
BALANCING
CHALLENGES
IN
THE
CLOUD
COMPUTING
Although cloud computing has been widely adopted, Research in cloud computing is still in its early stages and some scientific challenges remain unsolved by the scientific community, particularly on load balancing
Automated Service Provisioning: A key feature of cloud computing is elasticity. Resources can be allocated or released automatically. How then can we use or release the resources of the cloud, by keeping the same performance as traditional systems and using optimal resources?
Virtual Machines Migration: With
virtualization, an entire machine can be seen as a file or set of files, to unload a physical machine heavily loaded, it is possible to move a virtual machine between physical machines. The main objective is to distribute the load in a datacenter or set of datacenters. How then can we dynamically distribute the load when moving the virtual machine to avoid bottlenecks in Cloud computing systems? Energy Management: The benefits that
advocate the adoption of the cloud is the economy of scale. Energy saving is a key point that allows a global economy where a set of global resources will be supported by reduced providers rather that each one has its own resources. How then can we use a part of datacenter while keeping acceptable performance?
storage becomes a major challenge for cloud computing. The prime concern is how we can distribute the data to the cloud for optimum storage of data while maintaining fast access. Emergence of small data centers for cloud
computing: Small datacenters can be more beneficial, cheaper and less energy consumer than large datacenter. Small providers can deliver cloud computing services leading to geo-diversity computing. Load balancing will become a problem on a global scale to ensure an adequate response time with an optimal distribution of resources.
Throughput: Factors in Proposed system is the total number of required for better tasks that have Throughput performance of the completed execution system. With this throughput, the performance is increasing. Response time distributed system executing a specific load balancing algorithm takes to respond. In existing system, it is the degree to the resource which the resources of the system are utilized. A good load balancing algorithm provides maximum resource utilization to render better performance.
4.
CONCLUSION
Load balancing is one of the major challenges in cloud computing. It is a mechanism which distributes the dynamic local workload evenly across all the nodes in the whole cloud. This will avoid the situation where some nodes are heavily loaded while others are idle. It helps to achieve a high user satisfaction and resource utilization ratio. Hence, this will improve the overall performance and resource utility of the system. It also ensures that every computing resource is distributed efficiently and fairly. With proper load balancing, resource consumption can be kept to a minimum which will further reduce energy consumption. Existing load balancing techniques that have been discussed mainly focus on reducing
associated overhead, service response time and improving performance etc. but none of the techniques has considered the energy consumption. But still there are many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation, Energy Management, etc. which have not been fully addressed. Central to these issues is the issue of load balancing required to distribute the excess dynamic local workload evenly to all the nodes in the whole Cloud to achieve a high user satisfaction and resource utilization ratio.
5.
REFERENCES
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ABOUT AUTHORS
Prof.(Dr.) Sabyasachi Pattnaik has done the B.Tech in Computer Science, M Tech.from IIT Delhi and PhD degree in Computer Science, Now he is working as Professor in the Department of Information and Communication Technology, Fakir Mohan University, Vyasavihar, Balasore, Odisha, India. He has got 28years of teaching and research experience in the field of neural networks, soft computing techniques. He has got 85 publications in national & international journals and conferences. He has published four books in office automation, object oriented programming using C++ and cloud computing. At present he is involved in guiding several PhD, M Phil and M.Tech scholars in the field of neural networks, cluster analysis, bio-informatics & image processing. Thirteen scholars have already been awarded PhD under his guidance. He has received the best paper award & gold medal from Orissa Engineering congress and institution of Engineers. Also Prof. Pattnaik has received many honors and awards from several organizations. He is the life member, fellow member of the professional bodies like CSI, ISTE and Institution of Engineers, India & International society for research &development (ISRD).
Dr. Ashanta Ranjan Routray has done M.E in Computer Science and Engineering from Govt. College of Engineering, Manonmaniam Sundernar University, Tirunelveli and Ph.D in I&CT (Computer Science) from FM University,
Balasore. Now he is working as Associate Professor in the Department of Information and Communication Technology, Fakir Mohan University, Vyasavihar, Balasore, Odisha, India. He has got 17 years of teaching and research experience in the field of Computational Techniques, Cloud Computing and Soft Computing Techniques. He has got 32 publications in national & international journals and conferences.
Prof (Dr.) Himanshu Sekhar Moharana is a Professor in Dept. of Mechanical Engineering and Dean Research in Hi-Tech Institute of Technology, Khordha, Odisha. He is an M.E (Hons) in Manufacturing Technology from National Institute of Technical Teachers’ Training & Research, Chandigarh under Thapar University, Patiala. He is an MBA in Operations Management and Ph. D. in Mechanical Engineering from Utkal University. He is a recipient of nine State awards and a national award. He is an FIE (I), a Life Member of IIIE and ISTE.
