System Performance in Cloud Services: Stability and Resource Allocation

2013 ◽  
Author(s):  
Jay Kiruthika ◽  
Souheil Khaddaj
Keyword(s):  
Resource Allocation ◽  
System Performance ◽  
Cloud Services

scholarly journals An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications

2017 ◽  
Vol 6 (3) ◽  
pp. 124
Author(s):  
Elaheh Kheiri ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Service Quality ◽  
System Performance ◽  
Response Rate ◽  
Cloud Services ◽  
Service Performance ◽  
Software As A Service ◽  
Efficient Resource ◽  
Simulation Results

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

scholarly journals ДОСЯГНЕННЯ ЕФЕКТИВНОГО РОЗПОДІЛЕНОГО ПЛАНУВАННЯ ЗА ДОПОМОГОЮ ЧЕРГ ПОВІДОМЛЕНЬ У ХМАРІ ДЛЯ БАГАТОЗАДАЧНИХ ОБЧИСЛЕНЬ ТА ВИСОКОПРОДУКТИВНИХ ОБЧИСЛЕНЬ

2020 ◽  
Author(s):  
Старовойтенко Олексій Володимирович
Keyword(s):  
Distributed Systems ◽  
System Performance ◽  
State Of The Art ◽  
Computation Time ◽  
Cloud Services ◽  
Distributed Scheduling ◽  
Message Processing ◽  
Computational Problem ◽  
Control Units ◽  
And Control

Due to the growth of data and the number of computational tasks, it is necessary to ensure the required level of system performance. Performance can be achieved by scaling the system horizontally / vertically, but even increasing the amount of computing resources does not solve all the problems. For example, a complex computational problem should be decomposed into smaller subtasks, the computation time of which is much shorter. However, the number of such tasks may be constantly increasing, due to which the processing on the services is delayed or even certain messages will not be processed. In many cases, message processing should be coordinated, for example, message A should be processed only after messages B and C. Given the problems of processing a large number of subtasks, we aim in this work - to design a mechanism for effective distributed scheduling through message queues. As services we will choose cloud services Amazon Webservices such as Amazon EC2, SQS and DynamoDB. Our FlexQueue solution can compete with state-of-the-art systems such as Sparrow and MATRIX. Distributed systems are quite complex and require complex algorithms and control units, so the solution of this problem requires detailed research.

A Resource Allocation Model for Desktop Clouds

2015 ◽  
pp. 199-218 ◽  
Author(s):  
Abdulelah Alwabel ◽  
Robert John Walters ◽  
Gary B. Wills
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Cloud Services ◽  
New Paradigm ◽  
Allocation Mechanism ◽  
Desktop Grids ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Computing Services ◽  
New Type

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

2016 ◽  
Vol 6 (5) ◽  
pp. 2291
Author(s):  
Fereshteh Hoseini ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Processing Time ◽  
Resource Constraints ◽  
Cloud Services ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Real Time Applications ◽  
Simulation Results ◽  
Np Hard Problems

<p class="Abstract">By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks.</p>

A Grid Flow Management System for Resource Allocation Problem

2013 ◽  
Vol 706-708 ◽  
pp. 1985-1988
Author(s):  
Li Li Ding ◽  
Xiao Ling Wang ◽  
Zheng Wei Wang
Keyword(s):  
Resource Allocation ◽  
System Performance ◽  
Management System ◽  
Allocation Problem ◽  
Grid Service ◽  
Resource Allocation Problem ◽  
Flow Management ◽  
Rule Based ◽  
Simulated Environment ◽  
Grid Resources

This paper describes a framework for the grid flow management system in resource allocation problem based on the autonomous manager grid service (AMGS). We develop a user agent which is able to estimate the scoring rule based on grid resources attributes without human intervention, since agents are autonomous and intelligent in behavior. The reverse auction protocol involving an iterative algorithm for solving the resource allocation problem is also present. We implement the new protocol in a simulated environment and study its economic efficiency and its effect on the grid system performance.

scholarly journals CAI method in Cloud services for user workload using Cloudsim framework

2019 ◽  
Vol 8 (4) ◽  
pp. 12622-12626
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Service ◽  
Cloud Services ◽  
Time Data ◽  
Real Time Data ◽  
The Impact ◽  
System Designs ◽  
New System

In sighting the distinct patterns of processing capability in a cloud service is pedantic to enhance the resource management and operable conditions of the servers without compromising the Quality of Service is important. Simulations and models based on practicable parameters are required to understand the impact of the load on new system designs and policies. The proposed scheme and analysis provides a requirement for designing new systems which will be lessaffected by process loads. Classifying, analysis and improving (CAI) is done using real-time data center logs and simulations are done based on user requests and data center configurations. Simulations are created using cloudsim framework. Various simulations are done to provide a comprehensive result to improve the resource allocation for the system.

scholarly journals Minimizing the Cost for Resource Allocation from Multiple Cloud Providers

2021 ◽  
Vol 9 (11) ◽  
pp. 1611-1618
Author(s):  
Manasa Jonnagadla
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Service Providers ◽  
Low Cost ◽  
Hybrid Approach ◽  
Cloud Service ◽  
Cloud Services ◽  
Business Efficiency ◽  
Demand Variability ◽  
Cloud Resources

Abstract: Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

2020 ◽  
Vol 13 (5) ◽  
pp. 957-964
Author(s):  
Siva Rama Krishna ◽  
Mohammed Ali Hussain
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Cloud Services ◽  
Management Technique ◽  
Cloud Environment ◽  
Allocation Model ◽  
Proposed Model ◽  
Cloud Server ◽  
Different Types ◽  
Multi Level

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

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