PROPOSED OF A LOAD BALANCING METHOD FOR DATA INTENSIVE APPLICATIONS ON A HYBRID CLOUD ACCOUNTING FOR COST INCLUDING POWER CONSUMPTION

2012 ◽  
Keyword(s):  
Load Balancing ◽  
Power Consumption ◽  
Hybrid Cloud ◽  
Data Intensive ◽  
Data Intensive Applications

Priority Based Load Balancing in Cloud for Data Intensive Applications

2012 ◽  
Vol 5 (4) ◽  
pp. 200-206
Author(s):  
E. Iniya Nehru ◽  
S. Sujatha ◽  
P. Seethalaks ◽  
N. Sridharan
Keyword(s):  
Load Balancing ◽  
Data Intensive ◽  
Data Intensive Applications

scholarly journals Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

2004 ◽  
Vol 12 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Johan Parent ◽  
Katja Verbeeck ◽  
Jan Lemeire ◽  
Ann Nowe ◽  
Kris Steenhaut ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Heterogeneous Systems ◽  
Machine Learning Techniques ◽  
Parallel Applications ◽  
Communication Overhead ◽  
Data Intensive ◽  
Multi Agent ◽  
State Coordination ◽  
Data Intensive Applications

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

scholarly journals User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
P. Suresh ◽  
P. Balasubramanie
Keyword(s):  
Load Balancing ◽  
User Satisfaction ◽  
Scheduling Algorithm ◽  
Efficient Utilization ◽  
Grid Environment ◽  
Data Intensive ◽  
Scheduling Model ◽  
User Demand ◽  
Overall Performance ◽  
Data Intensive Applications

Grid computing is a collection of computational and data resources, providing the means to support both computational intensive applications and data intensive applications. In order to improve the overall performance and efficient utilization of the resources, an efficient load balanced scheduling algorithm has to be implemented. The scheduling approach also needs to consider user demand to improve user satisfaction. This paper proposes a dynamic hierarchical load balancing approach which considers load of each resource and performs load balancing. It minimizes the response time of the jobs and improves the utilization of the resources in grid environment. By considering the user demand of the jobs, the scheduling algorithm also improves the user satisfaction. The experimental results show the improvement of the proposed load balancing method.

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