A load balancing model using bio inspired firefly algorithm in cloud computing

2017 ◽  
Vol 7 (1.1) ◽  
pp. 671 ◽  
Author(s):  
Vikas . ◽  
M Sai Vivek ◽  
Pushyami Manohar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Firefly Algorithm ◽  
Process Time ◽  
Shared Resources ◽  
Time Intervals ◽  
Load Model ◽  
Request Sequence ◽  
Cloud Server ◽  
Index Table

Cloud computing is a model which helps in accessing the end-users interface which is adjustable as well as flexible service on Internet. The primary aim implemented scheme is for maximizing utilization of resource. It also offers a load which is good balanced compared to the all other resources in the servers of cloud. Some fundamental characteristics like usage of memory, process time, rate of the access, a resource load model can be deliver, by considering the load index which is new, present load computed to the virtual machine cloud server shared resources. If a load index calculated to the resources then initiation of balancing load for efficient utilization of resources. After all resources load index computed, the load balancing operation started to use the resources effectively by assigned resources processing for respective node for decreasing values of the load. The assignment of the resources to the nodes which are proper will become a problem i.e. optimal distribution. For this huge number of optimised schemes like algorithm of genetic, algorithm of modified genetic will be used to balance the load. But all these are may not be effective to offer best solutions. Because, the exploration issues are not overcome in these approaches. However, we can say that procedure of efficient optimisation is good for balancing load compared to others. Therefore, we implemented a new scheme for optimisation approach which is known as firefly algorithm for balancing the load. Initially the index table updated with the help of available virtual servers as well as request sequence. After that, based on new formulae, the load index will be calculated. The bal-ance of the load can be done with the help of load index using the algorithm of firefly. The results which are expected can be gathered and the implemented algorithm is efficient to balance the load in optimised time intervals.  

scholarly journals Measuring the Performance on Load Balancing Algorithms

2019 ◽  
pp. 41-49 ◽  
Author(s):  
Saumendu Roy ◽  
Dr. Md. Alam Hossain ◽  
Sujit Kumar Sen ◽  
Nazmul Hossain ◽  
Md. Rashid Al Asif
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Load Balancing ◽  
Virtual Machines ◽  
Huge Amount ◽  
Heavy Load ◽  
Cloud Server ◽  
Load Balancing Algorithm ◽  
Day By Day ◽  
Better Than

Load balancing is an integrated aspect of the environment in cloud computing. Cloud computing has lately outgoing technology. It has getting exoteric day by day residence widespread chance in close to posterior. Cloud computing is defined as a massively distributed computing example that is moved by an economic scale in which a repertory of abstracted virtualized energetically. The number of clients in cloud computing is increasing exponentially. The huge amount of user requests attempt to entitle the collection for numerous applications. Which alongside with heavy load not far afield off from cloud server. Whenever particular (Virtual Machine) VMs are overloaded then there are no more duties should be addressed to overloaded VM if under loaded VMs are receivable. For optimizing accomplishment and better response or reaction time the load has to be balanced between overloaded VMs (virtual machines). This Paper describes briefly about the load balancing accession and identifies which is better than others (load balancing algorithm).

scholarly journals The Hybrid Optimization Algorithm for Load Balancing in Cloud

2019 ◽  
Vol 8 (5S3) ◽  
pp. 67-71 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Direction ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Shared Resources

The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

scholarly journals A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2718
Author(s):  
M. A. Elmagzoub ◽  
Darakhshan Syed ◽  
Asadullah Shaikh ◽  
Noman Islam ◽  
Abdullah Alghamdi ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Swarm Intelligence ◽  
Bat Algorithm ◽  
Quality Parameters ◽  
Smart Devices ◽  
The Internet ◽  
Time Data ◽  
Shared Resources ◽  
Bee Colony

Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

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