Enhancing the Capability of Load Management Techniques in Cloud Using H_FAC Algorithm Optimization

2020 ◽  
Vol 16 (2) ◽  
pp. 65-81 ◽  
Author(s):  
Shadab Siddiqui ◽  
Manuj Darbari ◽  
Diwakar Yagyasen
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Cloud Service ◽  
Cuckoo Search ◽  
Hybridization Technique ◽  
Hybrid Technique ◽  
Algorithm Optimization ◽  
Bee Colony

Load balancing is a major research discipline in Cloud computing. The services are provided to users on pay as you go manner. Although a lot of algorithms have been proposed for load balancing, but performance is still an issue. The authors have proposed a new hybrid algorithm H_FAC to optimize the performance in cloud computing. The hybrid technique combines cuckoo search along with the Firefly algorithm of swarm intelligence. The benefit of using hybridization technique is that strength of one algorithm will overcome the shortcomings of other algorithms. Blockchain ID based Signature technique is used to ensure the authenticity of cloud service provider. The experimental results of H_FAC minimize the standard deviation, execution time significantly and improved throughput thereby optimizing the performance. The hybrid algorithm is also compared with other algorithms like ant colony optimization, artificial bee colony, round robin, FCFS and modified throttled. This approach helps the users to get the resources from authentic resource providers with a reduced execution time.

Load Balancing pada Cloud Computing dengan Sumber Daya Terbatas Menggunakan Penggabungan Algoritma ESCE dan Throttled

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Endang Wahyu Pamungkas ◽  
Divi Galih Prasetyo Putri
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
High Reliability ◽  
Cloud Service ◽  
The Other ◽  
Limited Resources ◽  
Resource Quality ◽  
Cloud Computing Environment ◽  
Load Balancing Algorithm

Recently cloud computing technology has been implemented by many companies. This technology requires a really high reliability that closely related to hardware specification and management resource quality used. Adequate hardware would make resource allocation easier. On the other hand, resource allocation will be harder if the resources are limited. This is a common condition in a developing cloud service provider. In this paper, a load balancing algorithm to allocate resources in cloud computing environment that has limited resources has been proposed. This algorithm is developed by taking the advantages of the existing algorithms, Equally Spread Current Execution and Throttled. We merge those algorithm without losing the advantages and we try to eliminate the shortcoming of each algorithm. The result shows that this algorithm is able to give a significant improvement in the limited resources environment. In addition, the algorithm also able to compete with the other algorithm in the more adequate resource environment. Based on the consistent results, this algorithm is expected to be more adaptive in different resources environment.

Dynamic Task Assignment with Load Balancing in Cloud Platform

2016 ◽  
pp. 363-385
Author(s):  
Subhadarshini Mohanty ◽  
Prashant Kumar Patra ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
User Satisfaction ◽  
Task Assignment ◽  
Heterogeneous Data ◽  
Hybrid Technique ◽  
Cloud Platform ◽  
Dynamic Task ◽  
Data Centres ◽  
Task Assignment Problem

Load balancing is one of the major issue in cloud computing. Load balancing helps in achieving maximum resource utilization and user satisfaction. This mechanism transparently transfer load from heavily loaded process to under loaded process. In this paper we have proposed a hybrid technique for solving task assignment problem in cloud platform. PSO based heuristic has been developed to schedule random task in heterogeneous data centres. Here we have also used variants of Particle Swarm Optimization(PSO) which gives better result than PSO and other heuristics for load balancing in cloud computing environment.

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

A New Conception of Load Balancing in Cloud Computing Using Tasks Classification Levels

2018 ◽  
Vol 8 (4) ◽  
pp. 118-133 ◽  
Author(s):  
Fahim Youssef ◽  
Ben Lahmar El Habib ◽  
Rahhali Hamza ◽  
Labriji El Houssine ◽  
Eddaoui Ahmed ◽  
...  
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Execution Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Mathematical Functions ◽  
Worst Case ◽  
Cloud Users

Cloud users can have access to the service based on “pay as you go.” The daily increase of cloud users may decrease the performance, the availability and the profitability of the material and software resources used in cloud service. These challenges were solved by several load balancing algorithms between the virtual machines of the data centers. In order to determine a new load balancing improvement; this article's discussions will be divided into two research axes. The first, the pre-classification of tasks depending on whether their characteristics are accomplished or not (Notion of Levels). This new technique relies on the modeling of tasks classification based on an ascending order using techniques that calculate the worst-case execution time (WCET). The second, the authors choose distributed datacenters between quasi-similar virtual machines and the modeling of relationship between virtual machines using the pre-scheduling levels is included in the data center in terms of standard mathematical functions that controls this relationship. The key point of the improvement, is considering the current load of the virtual machine of a data center and the pre-estimation of the execution time of a task before any allocation. This contribution allows cloud service providers to improve the performance, availability and maximize the use of virtual machines workload in their data centers.

scholarly journals HAMM: A Hybrid Algorithm of Min-Min and Max-Min Task Scheduling Algorithms in Cloud Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 209-218
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Scheduling Algorithms ◽  
Cloud Computing Environment ◽  
Concurrent Execution ◽  
Optimization Parameters ◽  
Better Than

Nowadays, with the huge development of information and computing technologies, the cloud computing is becoming the highly scalable and widely computing technology used in the world that bases on pay-per-use, remotely access, Internet-based and on-demand concepts in which providing customers with a shared of configurable resources. But, with the highly incoming user’s requests, the task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing of a workload among cloud resources to enhance the overall cloud system performance. For these reasons, various types of task scheduling algorithms are introduced such as traditional, heuristic, and meta-heuristic. A heuristic task scheduling algorithms like MET, MCT, Min-Min, and Max-Min are playing an important role for solving the task scheduling problem. This paper proposes a new hybrid algorithm in cloud computing environment that based on two heuristic algorithms; Min-Min and Max-Min algorithms. To evaluate this algorithm, the Cloudsim simulator has been used with different optimization parameters; makespan, average of resource utilization, load balancing, average of waiting time and concurrent execution between small length tasks and long size tasks. The results show that the proposed algorithm is better than the two algorithms Min-Min and Max-Min for those parameters

scholarly journals Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

2019 ◽  
Vol 11 (3) ◽  
pp. 606-626
Author(s):  
Shymaa G. Eladl ◽  
Nesreen I. Ziedan ◽  
Tamer S. Gaafar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Hybrid Algorithm
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