Cost- and Time-Based Data Deployment for Improving Scheduling Efficiency in Distributed Clouds

2020 ◽  
Author(s):  
Chunlin Li ◽  
Yihan Zhang ◽  
Xiaomei Qu ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Service Level Agreement ◽  
Data Access ◽  
Service Level ◽  
Speculative Execution ◽  
Improved Genetic Algorithm ◽  
Real Time Processing ◽  
Data Intensive ◽  
Access Cost

Abstract In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Delivery ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

A new popularity-based data replication strategy in cloud systems

2021 ◽  
Vol 17 (2) ◽  
pp. 159-177
Author(s):  
Abdenour Lazeb ◽  
Riad Mokadem ◽  
Ghalem Belalem
Keyword(s):  
Service Level Agreement ◽  
Data Replication ◽  
Evaluation Study ◽  
Service Level ◽  
Cloud Provider ◽  
Computing Systems ◽  
Data Intensive ◽  
Replication Strategy ◽  
Context Data ◽  
Replication Technique

Data-intensive cloud computing systems are growing year by year due to the increasing volume of data. In this context, data replication technique is frequently used to ensure a Quality of service, e.g., performance. However, most of the existing data replication strategies just reproduce the same number of replicas on some nodes, which is certainly not enough for more accurate results. To solve these problems, we propose a new data Replication and Placement strategy based on popularity of User Requests Group (RPURG). It aims to reduce the tenant response time and maximize benefit for the cloud provider while satisfying the Service Level Agreement (SLA). We demonstrate the validity of our strategy in a performance evaluation study. The result of experimentation shown robustness of RPURG.

QoS-Constrained Resource Scheduling Base on Market Economy Models in Grid Computing

2013 ◽  
Vol 443 ◽  
pp. 599-602
Author(s):  
Lei Chen
Keyword(s):  
Market Economy ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Resource Scheduling ◽  
Data Access ◽  
Grid Environment ◽  
Task Scheduling Algorithm ◽  
User Tasks

The Grid task scheduling algorithm is proposed that takes the service quality of resource scheduling, time and cost together into consideration, so that it can better meet user tasks Quality of Service (QoS) requirements and make the complex grid environment open. On the basis of the price model drove by supply and demand in economy, we design the Grid task scheduling algorithm in the market economy model. The experiment results indicate the effectiveness of proposed algorithm in terms of usersQoS guarantee. It reduce data access latency and decrease bandwidth consumption.

scholarly journals QRAS: efficient resource allocation for task scheduling in cloud computing

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Harvinder Singh ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri
Keyword(s):  
Resource Allocation ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Resource ◽  
Time Problem ◽  
Efficient Resource ◽  
Np Hard Problem ◽  
Quality Of Service Parameters

AbstractCloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.

QoS Enabled Task Scheduling Approach in Cloud Computing as Per Service Level Agreement

2020 ◽  
Vol 17 (11) ◽  
pp. 5003-5009
Author(s):  
Puneet Banga ◽  
Sanjeev Rana
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Turnaround Time ◽  
Service Level ◽  
Profit Margins ◽  
Essential Services ◽  
Time Average ◽  
Average Turnaround Time

Due to constraints along with profit margins in background, service provider’s sometime neglect to feed essential services to their respective clients. Such compulsion raises the demand for efficient task scheduling that can meet multiple objectives. But without any prior agreement, again makes a casual approach. So this dispute can be addressed when competent scheduling executes right over the Service Level Agreement. It acts as hotspots to define set of rules to assure quality of service. At this time, there is a huge demand of SLA opted scheduling that can produce profitable results from provider’s and client’s as well. This article presents a fundamental approach that can be applied to existing scheduling techniques on the fly. Result shows drastic improvement in terms of average waiting time, average turnaround time without comprising provider’s cost margin at all along with fairness.

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