scholarly journals Study of Load Optimization and Performance Issues in Cloud

2018 ◽  
Vol 11 (3) ◽  
pp. 1035
Author(s):  
Madhina D Banu ◽  
Aranganathan Aranganathan
Keyword(s):  
Load Balancing ◽  
Performance Optimization ◽  
Research Study ◽  
Optimization Approach ◽  
Minimal Cost ◽  
Cloud Environment ◽  
Effective Solution ◽  
Heavy Load ◽  
Load Optimization ◽  
Cloud Application

“Nowadays, cloud computing is the latest computing platform which is feasible to the user for computation and unlimited storage and data transmission with minimal cost and time in a cloud environment during the internet. The load balancing is important criteria of cloud environment that avoid same nodes overloaded and others are idle. Ultimately load balancing can enhance the QoS parameters including make span, cost and resource utilization. To optimize the load, the existing load optimization approach is properly utilized federation mechanisms, which offers physical resources based on demand to maintain the cloud application efficiency. However, the technique failed to optimize load where part of the servers suffering from heavy load after an execution of the application. In the current scenario, several systems are facing same kinds of problem which is the biggest cause to increase the virtual machine (VM) cost. Current systems still have a time delay, request-response process error from data center side in cloud environments. To overcome these issues, the research study studies all related technique for cloud performance optimization and load balancing issues. The main framework objective is to offer an effective solution to store/search/transmit/ data with minimal cost and time without compromising the QoS constraints. The study represents different policies and cloud-specific strategies to enhance the performance of cloud application with minimal cost and time. The research study is also planning to find out an effective solution for traffic, data congestion and media streaming issues in a cloud environment.

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.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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.

Task Scheduling in cloud using ACO

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuvaraj Natarajan ◽  
Srihari Kannan ◽  
Gaurav Dhiman
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Limited Resource ◽  
Cloud Environment ◽  
Clear Cut ◽  
Strategic Approach ◽  
User Demand ◽  
Scheduling Strategies ◽  
Cloud Users ◽  
And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment

2015 ◽  
Vol 25 (06) ◽  
pp. 935-960
Author(s):  
Yuan-Hsin Tung ◽  
Shian-Shyong Tseng ◽  
Wei-Tek Tsai
Keyword(s):  
Approximate Solutions ◽  
Relationship Matrix ◽  
Minimal Cost ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Domain Experts ◽  
Knowledge Based ◽  
Expert Opinions ◽  
Service Oriented ◽  
System Maintenance

Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.

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