scholarly journals Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

2021 ◽  
Vol 5 (3) ◽  
pp. 44-53
Author(s):  
Sohaib Ahmad
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Cost Efficiency ◽  
Performance Enhancement ◽  
Workload Management ◽  
Virtual Storage ◽  
Management Algorithm ◽  
Scheduling Method ◽  
And Storage ◽  
Individual Node

The need to process and dealing with a vast amount of data is increasing with the developing technology. One of the leading promising technology is Cloud Computing, enabling one to accomplish desired goals, leading to performance enhancement. Cloud Computing comes into play with the debate on the growing requirements of data capabilities and storage capacities. Not every organization has the financial resources, infrastructure & human capital, but Cloud Computing offers an affordable infrastructure based on availability, scalability, and cost-efficiency. The Cloud can provide services to clients on-demand, making it the most adapted system for virtual storage, but still, it has some issues not adequately addressed and resolved. One of those issues is that load balancing is a primary challenge, and it is required to balance the traffic on every peer adequately rather than overloading an individual node. This paper provides an intelligent workload management algorithm, which systematically balances traffic and homogeneously allocates the load on every node & prevents overloading, and increases the response time for maximum performance enhancement.

Efficient on Demand Dynamic Availability-Distribution-Span Scheduling and Load Balancing Scheme for Cloud Computing

2016 ◽  
Vol 13 (10) ◽  
pp. 7655-7660 ◽  
Author(s):  
V Jeyakrishnan ◽  
P Sengottuvelan
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Scheduling Algorithms ◽  
Time Factor ◽  
Cloud Environment ◽  
On Demand ◽  
Availability Factor ◽  
Scheduling Method

The problem of load balancing in cloud environment has been approached by different scheduling algorithms. Still the performance of cloud environment has not been met to the point and to overcome these issues, we propose a naval ADS (Availability-Distribution-Span) Scheduling method to perform load balancing as well as scheduling the resources of cloud environment. The method performs scheduling and load balancing in on demand nature and takes dynamic actions to fulfill the request of users. At the time of request, the method identifies set of resources required by the process and computes Availability Factor, Distributional Factor and Span Time factor for each of the resource available. Based on all these factors computed, the method schedules the requests to be processed in least span time. The proposed method produces efficient result on scheduling as well as load balancing to improve the performance of resource utilization in the cloud environment.

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

Load Balancing in Cloud Computing

2020 ◽  
pp. 294-316
Author(s):  
Pradeep Kumar Tiwari ◽  
Geeta Rani ◽  
Tarun Jain ◽  
Ankit Mundra ◽  
Rohit Kumar Gupta
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
High Reliability ◽  
Large Data ◽  
Resource Provisioning ◽  
Data Handling ◽  
On Demand ◽  
Load Imbalance ◽  
Physical Resources ◽  
And Storage

Cloud computing is an effective alternative information technology paradigm with its on-demand resource provisioning and high reliability. This technology has the potential to offer virtualized, distributed, and elastic resources as utilities to users. Cloud computing offers numerous types of computing and storage means by connecting to a vast pool of systems. However, because of its large data handling property, the major issue the technology facing is the load balancing problem. Load balancing is the maximum resource utilization with effective management of load imbalance. This chapter shares information about logical and physical resources, load balancing metrics, challenges and techniques, and also gives some suggestions that could be helpful for future studies.

scholarly journals Qos and Load Balancing in Cloud Computing- an Access for Performance Enhancement using Agent Based Software

2019 ◽  
Vol 8 (11S) ◽  
pp. 641-644
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Enhancement ◽  
Service Providers ◽  
Cloud Service ◽  
Service Level ◽  
Vital Role ◽  
Software Agent ◽  
Gateway System ◽  
Cloud Service Providers

Cloud computing, one of the advanced and emerged technologies in the field of computer science has been embraced by different organizations of various sizes. The purpose of organizations moving into cloud is manifold, out of which , performance enhancement and cost optimizer are the primary ones. Generally, when an organization moves their operations into cloud, Cloud Service Providers (CSPs) provision various machine images to different users based on their requirements within the organization. Also, CSPs, potentially offer Anything as a Service (XaaS) to organizations with the help of distributed and connected server farms available at geographically separate locations. QoS parameters in terms of service time, as specified in the Service Level Agreements(SLAs) between CSPs and organizations must be adhered strictly. As an effort towards maintaining QoS, within the cloud, the operational approach of load balancing across multiple distributed servers play a vital role. This paper presents a novel load balancing algorithmic framework with the help of software agent that runs in the gateway system between cloud consumers and cloud service providers. This software agent in the gateway system is vested with the responsibility of diverting the incoming work process to the appropriate servers, based on their current workload and resource utilization. The efficiency of this approach is tested using CLOUDSIM by creating different number of cloudlets and hosts

scholarly journals Cloudlet Networks Performance Analysis and Improvement

2017 ◽  
Vol 3 (2) ◽  
pp. 22
Author(s):  
Jameela Abdulla Hassan ◽  
Fahad Al-Dosari
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Mobile Cloud Computing ◽  
Round Robin ◽  
Share Data ◽  
Index Terms ◽  
Load Balancing Algorithm ◽  
And Storage

Abstract— Cloud computing is a Participation in the process and storage operations across distant servers that are shared by many organizations and users and thus be transferred from an application to a service. The organization can share data over the Internet and user can pay only for the resources that will be used only. While cloud computing has disadvantages, there are some advantages for cloudlets have over cloud computing which include: lower network latency and users having full ownership of the data shared. When the need of data to be stored in the servers grows quickly, the workload in every resource will grow too. So, we need a load balancing algorithm and the load balancing is important issue in the cloud environment. Load balancing defined as a technique that divides the extra load equally across all the resources to ensure that no one resource overloaded. . So the performance of the cloud can be improved by having an excellent load balancing strategy. For that we will discuss the existing load balancing algorithms in cloud computing and propose algorithm to improve round robin algorithm by CloudAnalyst simulator  based on a factor of  response time and processing time  and the proposed algorithm was found to be best in response time and processing time when we compare it with round robin algorithms.   Index Terms— Cloud Computing, CloudAnalyst, Load Balance, Mobile Cloud Computing, Cloudlet Networks.

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