Performance Parameters for Load Balancing Algorithm in Grid Computing

2012 ◽  
pp. 333-336
Author(s):  
Pardeep Kumar ◽  
Ravi Rastogi ◽  
Shiv Kumar Gupta ◽  
Krishna Kumar Verma
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Performance Parameters ◽  
Load Balancing Algorithm

scholarly journals Implementation of Load Balancing Algorithm in a Grid Computing

2006 ◽  
Vol 3 (4) ◽  
pp. 1810-1813 ◽  
Author(s):  
Abdallah Boukerram ◽  
Samira Ait Kaci A
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Load Balancing Algorithm

A threshold-based load balancing algorithm for grid computing systems

2015 ◽  
Vol 21 (4) ◽  
pp. 237-257 ◽  
Author(s):  
S.F. El-Zoghdy ◽  
Alaa I. Elnashar
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Computing Systems ◽  
Load Balancing Algorithm

scholarly journals MINIMIZATION OF LOAD BASED RESOURCES IN CLOUD COMPUTING SYSTEMS

2015 ◽  
pp. 72-75
Author(s):  
ZULFIKHAR AHMAD ◽  
ASHIS KU. MISHRA ◽  
BIKASH CHANDRA ROUT
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Distributed System ◽  
Work Load ◽  
Memory Capacity ◽  
Performance Parameters ◽  
Computing Systems ◽  
Divisible Load ◽  
Network Load ◽  
Load Balancing Algorithm

“Cloud computing” is a term, which involves virtualization, distributed computing, networking, software and Web services. Our Objective is to develop an effective load balancing algorithm using Divisible Load Scheduling Theorem to maximize or minimize different performance parameters (throughput, latency for example) for the clouds of different sizes. Central to these issues lays the establishment of an efficient load balancing algorithm. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all processor in the system or every node in the network does approximately the equal amount of work at any instant of time.

Evaluation of Fault Tolerant and Min-Max Load Balancing Algorithm in Grid Computing

2018 ◽  
Vol 6 (7) ◽  
pp. 1331-1337
Author(s):  
Mr. Mahesh Reddy G ◽  
Mr. Lakshminarayana G ◽  
.Mr. Srinuvasa Reddy K
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Fault Tolerant ◽  
Load Balancing Algorithm

scholarly journals Cloud computing based load balancing algorithm for erlang concurrent traffic

2020 ◽  
Vol 17 (2) ◽  
pp. 1109
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Memory Load ◽  
Performance Parameters ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Concurrent Tasks ◽  
User Access ◽  
Load Balancing Algorithm

<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced.  There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>

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