scholarly journals Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization

2015 ◽  
Vol 7 (4) ◽  
pp. 465-483 ◽  
Author(s):  
Ren Gao ◽  
Juebo Wu
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Dynamic Load ◽  
Ant Colony ◽  
Dynamic Load Balancing

scholarly journals Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

2016 ◽  
Vol 19 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Antonio Llanes ◽  
José M. Cecilia ◽  
Antonia Sánchez ◽  
José M. García ◽  
Martyn Amos ◽  
...  
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Dynamic Load ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Heterogeneous Clusters

scholarly journals Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management

2021 ◽  
Vol 11 (22) ◽  
pp. 10807
Author(s):  
Fatma Mbarek ◽  
Volodymyr Mosorov
Keyword(s):  
Distributed Computing ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Dynamic Load ◽  
Nearest Neighbor ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Load Balancing Algorithm

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.

scholarly journals Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 939-946
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Cloud Environment ◽  
Task Scheduling Algorithm

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

Hermit: A Novel Approach for Dynamic Load Balancing in Cloud Computing

2020 ◽  
pp. 275-287
Author(s):  
Subasish Mohapatra ◽  
Subhadarshini Mohanty ◽  
Arunima Hota ◽  
Prashanta Kumar Patra ◽  
Jijnasee Dash
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Dynamic Load ◽  
Dynamic Load Balancing ◽  
Novel Approach
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