scholarly journals DEEP LEARNING BASED LOAD BALANCING USING MULTIDIMENSIONAL QUEUING LOAD OPTIMIZATION ALGORITHM FOR CLOUD ENVIRONMENT.

2020 ◽  
Vol 9 (10) ◽  
pp. 156-167
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Load Balancing ◽  
Response Time ◽  
Success Rate ◽  
Virtual Machines ◽  
Service Providers ◽  
Resource Scheduling ◽  
Cloud Environment ◽  
Load Optimization

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

scholarly journals Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

2021 ◽  
pp. 1-8
Author(s):  
Mohammed Radi ◽  
Ali Alwan ◽  
Abedallah Abualkishik ◽  
Adam Marks ◽  
Yonis Gulzar
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Service Providers ◽  
Cost Effective ◽  
Cloud Service ◽  
User Preferences ◽  
Cloud Environment ◽  
Data Centre ◽  
Wide Range ◽  
Service Broker

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

2020 ◽  
pp. 1042-1057
Author(s):  
Xiaojing Hou ◽  
Guozeng Zhao
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Energy Consumption ◽  
Load Balancing ◽  
Energy Cost ◽  
Learning Strategy ◽  
Resource Scheduling ◽  
High Energy ◽  
Fusion Algorithm ◽  
Low Efficiency

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

2018 ◽  
Vol 13 (3) ◽  
pp. 54-72 ◽  
Author(s):  
Xiaojing Hou ◽  
Guozeng Zhao
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Energy Consumption ◽  
Load Balancing ◽  
Data Centers ◽  
Learning Strategy ◽  
Resource Scheduling ◽  
High Energy ◽  
Fusion Algorithm ◽  
Low Efficiency

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

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.

Receiver Initiated Deadline Aware Load Balancing Strategy (RDLBS) for Cloud Environment

2017 ◽  
Vol 8 (3) ◽  
pp. 53-73
Author(s):  
Raza Abbas Haidri ◽  
Chittaranjan Padmanabh Katti ◽  
Prem Chandra Saxena
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Load Balancing ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Computing Technology ◽  
Trade Off ◽  
The Cost ◽  
State Of Art

The emerging cloud computing technology is the attention of both commercial and academic spheres. Generally, the cost of the faster resource is more than the slower ones, therefore, there is a trade-off between deadline and cost. In this paper, the authors propose a receiver initiated deadline aware load balancing strategy (RDLBS) which tries to meet the deadline of the requests and optimizes the rate of revenue. RDLBS balances the load among the virtual machines (VMs) by migrating the request from the overloaded VMs to underloaded VMs. Turnaround time is also computed for the performance evaluation. The experiments are conducted by using CloudSim simulator and results are compared with existing state of art algorithms with similar objectives.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

scholarly journals Load balancing in Cloud Computing: Issues and Challenges

2021 ◽  
Vol 12 (2) ◽  
pp. 3224-3231
Author(s):  
K. Balaji, Et. al.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Load Distribution ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Services ◽  
Cloud Environment ◽  
Computing Technology ◽  
Load Balancer

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate  resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.

scholarly journals Load balancing in Cloud Computing: Issues and Challenges

2021 ◽  
Vol 12 (2) ◽  
pp. 3077-3084
Author(s):  
K. Balaji , Et. al.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Load Distribution ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Services ◽  
Cloud Environment ◽  
Computing Technology ◽  
Load Balancer

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate  resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.

scholarly journals Priority Based Mechanism for Resource Sharing in Cloud

2020 ◽  
Vol 9 (3) ◽  
pp. 311-316
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Open Source ◽  
Resource Sharing ◽  
Virtual Machines ◽  
Service Providers ◽  
Priority Level ◽  
Lower Priority ◽  
Computing Technologies ◽  
Cloud Computing Service

Cloud computing technologies are getting matured day by day. Revolutions in underlying software, virtualization and hardware technologies related to storage, processing and computing technologies has helped cloud computing service providers to win trust of concerned stake holders. However, the exponentially increasing demand of cloud based resources has made task of resource management and utilization more and more challenging. A novel load balancing technique in cloud computing environment is presented in this paper. The virtual machines are implemented on an open source cloud computing platform on open source operating system. A virtual machines’ priorities based load balancing approach presented here indicates improvement in overall waiting time for load balancing. The mechanism prioritizes load balancing on same priority level virtual machines or lower priority level virtual machines.

Sign in / Sign up

Export Citation Format

Share Document