International Journal of Computer Application and Engineering Technology Volume 3-Issue 4, Oct 2014. Pp. 285-289
www.ijcaet.net
Load Balancing Strategy to Improve the Efficiency
in the Public Cloud Environment
Nalabolu Sudheer Reddy 1, Sayyad Rasheeduddin 2 1. PG Scholar, 2. Associate Professor, Dept of CSE,CRV Institute of Technology & Science, Hyderabad, Telangana, India. E. Mail: [email protected]
Abstract: Load balancing in the cloud computing environment has an important impact on the performance. Good
load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Key words: load balancing model; public cloud; cloud partition; game theory
1. Introduction
Cloud computing is an attracting technology in the field of computer science. In Gartner’s report, it says that the clo ud will bring changes to the IT industry. The cloud is chang ing our life by providing users with new types of services. Users get servic e from a cloud without paying attention to the details. NIST gave a defin ition of cloud com puting as a m odel for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable com puting resources (e.g., networks, servers, storage, app lications, and services) that can be rapidly provisioned and released with m inimal management effort or service provider interaction. More and more people pay attention to cloud computing. Cloud computing is efficient and scalable but maintaining the stability of processing so many jobs in the cloud com puting environment is a very complex problem with load balancing receiving much attention for researchers.
Since the job arrival pattern is not predictable and the capacities of each node in the cloud differ, for load balancing problem , workload control is crucial to im prove system performance and maintain stability. Load balancing schemes depending on whether the system dynamics are important can be either static or dynam ic. Static schemes do not use the system information and are less co mplex while dynam ic schemes will bring additional cos ts for the sys tem but can change as the system status changes. A dynam ic scheme is used here for its flexibility. The model has a m ain controller and balancers to gather and analyze the infor mation. Thus, the dynamic control has little influence on the other working nodes.
2. Related Work
There have been m any studies of load balancing for the cloud environm ent. Load balancing in cloud computing was described in a white paper written by Adler who in troduced the tools and techniques commonly used for load balancing in the cloud. However, load balancing in the cloud is still a new problem that needs n ew architectures to adap t to many changes. Chaczko et al. described the role that load balancing plays in im proving the performance and m aintaining stability. There are m any load balancing algo rithms, such as Round Robin, Equally Spread Current Execution Algorithm , and Ant Colony al gorithm. Nishant et al.used the ant colony
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4. Cloud Partition Load Balancing Strategy 4.1 Motivation
Good load balance will im prove the perform ance of the entire cloud. However, there is no common method that can adapt to all possible diffe rent situations. Various m ethods have been developed in improving existing solutions to resolve new problems.
Each particular method has advantage in a particular area but not in all situations. Therefore, the current model integrates several methods and switches between the load balance m ethods based on the system status.
A relatively simple method can be used for the partition idle state with a more complex method for the normal state. The load balancers then switch methods as the status changes. Here, the idle status uses an im proved Round R obin algorithm while the norm al status uses a gam e theory based load balancing strategy.
4.2. Load balance strategy for the idle status
When the cloud partition is idle, many computing resources are available and relatively few jobs are arriving. In this situation, th is cloud partition has the ability to process jobs as quickly as possible so a simple load balancing method can be used.
There are m any simple load balance algorith m methods such as the Random algorithm, the Weight Round Robin, and the Dynam ic Round Robin .The Round Robin algorithm is used here for its simplicity. The Round Robin algorithm is one of the simplest load balancing algorithms, which passes each new request to the next server in the queue. The algorithm does not record the status of each connection so it has no status information. In the regular Round Robin algorithm , every node has an equal opportunity to be chose n. However, in a public cloud, the configuration and the performance of each node will be not th e same; thus, this method may overload some nodes. Thus, an improved Round Robin algorithm is used, which called “Round Robin based on the load degree evaluation”.
4.3. Load balancing strategy for the normal status
When the cloud partition is norm al, jobs are arrivi ng much faster than in the idle state and the situation is far more complex, so a different strategy is used fo r the load balancing. Each user wants his jobs com pleted in th e shortest tim e, so the public cloud needs a m ethod that can complete the jobs of all users with reasonable response time. As an implementation of distributed system, the load balancing in the cloud computing environment can be viewed as a gam e. Since the grid computing and cloud co mputing environments are also distributed system , these Algorithms can also be used in grid computing and cloud computing environments.
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