Performance analysis of greedy Load balancing algorithms in Heterogeneous Distributed Computing System

2014 ◽  
Author(s):  
Bibhudatta Sahoo ◽  
Dilip Kumar ◽  
Sanjay Kumar Jena
Keyword(s):  
Performance Analysis ◽  
Distributed Computing ◽  
Load Balancing ◽  
Computing System ◽  
Heterogeneous Distributed Computing

Heuristic Resource Allocation Algorithms for Dynamic Load Balancing in Heterogeneous Distributed Computing System

2014 ◽  
pp. 181-209
Author(s):  
Bibhudatta Sahoo ◽  
Sanjay Kumar Jena ◽  
Sudipta Mahapatra
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Simulated Annealing ◽  
Distributed Computing ◽  
Load Balancing ◽  
Dynamic Load ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Dynamic Load Balancing ◽  
Heterogeneous Distributed Computing

Distributed heterogeneous computing is being widely applied to a variety of large-size computational problems. These computational environments consist of multiple heterogeneous computing modules; these modules interact with each other to solve the problem. The load balancing problem in the Heterogeneous Distributed Computing System (HDCS) deals with allocation of tasks to computing nodes, so that computing nodes are evenly loaded. The complexity of dynamic load balancing increases with the size of HDCS and becomes difficult to solve effectively. Due to the complexity of the dynamic load balancing problem, the majority of researchers use a heuristic algorithm to obtain near optimal solutions. The authors use three different type of resource allocation heuristic techniques, namely greedy heuristic, simulated annealing, and genetic algorithm, for dynamic load balancing on HDCS. A new codification suitable to simulated annealing and the genetic algorithm has been introduced for dynamic load balancing on HDCS. This chapter demonstrates the use of the common coding scheme and iterative structure by simulated annealing and genetic algorithms for allocating the tasks among the computing nodes to minimize the makespan. The resource allocation algorithm uses sliding window techniques to select the tasks to be allocated to computing nodes in each iteration. A suitable codification for simulated annealing and genetic algorithm for dynamic load balancing strategy are explained along with implementation details. Consistent Expected Time to Compute (ETC) matrix is used to simulate the effect of the genetic algorithm-based dynamic load balancing scheme compared with first-fit, randomized heuristic, and simulated annealing.

scholarly journals Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

2021 ◽  
Vol 12 (1S) ◽  
pp. 257-265
Author(s):  
Vidya S. Handur, Et. al.
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Computing ◽  
Load Balancing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Computing System ◽  
Optimization Approach ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Swarm Optimization

Development of technology like Cloud Computing and its widespread usage has given rise to exponential increase in the volume of traffic. With this increase in huge traffic the resources in the network would either be insufficient to handle the traffic or the situation may cause some of the resources to be over utilized or underutilized. This condition leads to reduced performance of the system. To improve the performance of the system the traffic requires to be regulated such that all the resources are utilized conferring to their capacity which is known as load balancing. Load balancing has been one of the concerns in the distributed computing systems where the computing nodes do not have a global view of the network. There have been constant efforts to provide an efficient solution for load balancing through the approaches like game theory, fuzzy logic, heuristics and metaheuristics. Even though various solutions exist for balancing the load, the issue is challenging as there does not exist one best fit solution. The paper aims at the study of how Particle Swarm Optimization approach is used to achieve an optimal solution for load balancing in distributed computing system.

scholarly journals A hybrid Method to augment the efficiency of Distributed Computing System by DAG -Using Finest Task Allocation with Dual Mode Processors

2019 ◽  
Vol 8 (11) ◽  
pp. 4275-4284
Keyword(s):  
Distributed Computing ◽  
Task Allocation ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Service Rate ◽  
Additional Parameter ◽  
Computing Environment ◽  
Dual Mode ◽  
New Approach ◽  
Heterogeneous Distributed Computing

Distributed computing system creates or provides a platform having multiple computing nodes linked in a specified manner. On the basis of literature review of last few decades it has been noticed that most of distributed computing researchers have shown their effort to maintain load balancing between processors ,effective task scheduling and optimizing different parameters affecting execution cost and throughput .With these above scenario an additional parameter “Self reconfiguration of CPU” is also a countable parameter to augment the efficiency of distributed computing system .Through this research paper we want to present new approach of adaptive scheduling algorithm which is the mix output of effective task allocation to processor involved in computing and self-reconfiguration of those processors as per need of computing. By this proposed method we will optimize the execution cost, service rate and maximize the throughput as an outcome of organized processors consist in heterogeneous distributed computing system, resulting provide the considerable enhancement in the performance of Distributed computing environment.

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