Effort of Load Balancer to Achieve Green Cloud Computing : A Review

다국어 초록 (Multilingual Abstract)

In a distributed system, from the starting days onwards distribution of load among servers becomes a serious problem in the commercial Internet. The problem in this scenario is? The entire single application oriented server has to engage the entire amount of traffic and if they went down, all commercial activities come down offline result out of business. Running individually the application server couldn’t support start-up budgets. The folks involved in the Web Commercial plan to solve this problem by distributing the load evenly to all the servers running on the web host owned by different agents or organizations; thus new scenario was born named as Load Balancing. In this treatise, we investigate several shapes attenuated by load Balancer and reviewed the algorithms proposed on load balancing. Here we took both static as well as dynamic based algorithms and their performances are formulated by comparison with all other existing scheme. This paper also brings connectivity on green computing with cloud load balancers. By cloud computing we can attain multi tenancy and dynamic resource handling which automatically reduces co2 emission from servers. Without the facility of sharing single resources among thousands of peoples, green computing is not possible. So the nature of cloud load balancer and green computing was illustrated here.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 1.1. Distributing Models
• 1.2. Grid Computing
• 1.3. Cloud Computing
• 1.4. Green Cloud Computing
• 2. Load Balancer
• 2.1. Birth of Load Balancer
• 2.2. Domain Name System [DNS]
• 2.3. Formation of Load Balancers
• 2.4. Layer Based Load Balancer
• 3.Load Balancing Algorithms
• 3.1. Algorithm Classification
• 3.2. Works carried on Load Balancing Algorithms
• 4.1. Round Robin Algorithm [4]:
• 4.2. Opportunistic Load Balancing Algorithm [5]:
• 4.3. Min-Min Load balancing Algorithm [6]
• 4.4. Max-Min Load balancing Algorithm [7]
• 4.5. Ant Colony Optimization Based Load Balancing Algorithm [8]
• 4.6. Honeybee Foraging Load balancing Algorithm [9]
• 4.7. Biased Random Sampling load balancing algorithm [10]
• 4.8. Active Clustering Load Balancing Algorithm [11]
• 4.9. Genetic based Load Balancing Algorithm [12]
• 4.10. Agent based load balancing algorithm [13]
• 5. Result Analysis
• 6. Achieving Green Computing
• 7. Conclusion
• References