Temperature-Aware Scheduling Algorithm for Multi-Core System

2014 ◽  
Vol 536-537 ◽  
pp. 703-707
Author(s):  
Qi Zhang ◽  
You Lin Ruan ◽  
Feng Gao
Keyword(s):  
Scheduling Algorithm ◽  
Uniform Temperature ◽  
Energy Balancing ◽  
Core System ◽  
Task Scheduling Algorithm ◽  
Multicore System ◽  
Utilization Ratio ◽  
Simulation Results ◽  
And Performance ◽  
Core Simulation

High temperature will affect reliability and performance of multicore system. In this paper, we propose a temperature-aware task scheduling algorithm for real-time multi-core systems, which combines the DVFS and energy balancing by analyzing workload information and multicore utilization. At first, calculate average utilization ratio of tasks. Secondly, balancing strategy according to the workload is proposed for uniform temperature distribution on the cores. Finally, adapt the HR-2 and DVFS to scheduling tasks in each core. Simulation results show that the proposed scheduling algorithm obtains a better effect in temperature and energy-saving than other algorithms.

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.

Research on Resource Scheduling Algorithm Integrating Task Weight and Multi-QoS Constraint

2011 ◽  
Vol 52-54 ◽  
pp. 1125-1130
Author(s):  
Jing Chen ◽  
Ming Xin Liu
Keyword(s):  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Grid Resource ◽  
Resource Information ◽  
Grid Resource Scheduling ◽  
Utilization Ratio ◽  
Simulation Results ◽  
And Task

To improve the utilization ratio of resources and the complete number of tasks, a kind of a new grid resource scheduling algorithm TWMQC (based on Task Weight and Multi-QoS Constraint) integrating multi-QoS constraint with task weight was proposed. The accomplished process of grid resource scheduling algorithm was transformed multi attribute constraints of resource and task, according to the parametric resource information and task information, classified different task weight sets based on the priority of tasks. Multi-QoS constraints of deadline of gridlets, bandwidth and CPU were defined, and the correlative algorithms were simulated by the GridSim toolkits. The simulation results show that algorithm TWMQC, which integrating multi-QoS constraint and tasks weight is superior in solving such kind of issues by comparing and analyzing the result data.

scholarly journals A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

2019 ◽  
Vol 2019 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Karunakaran V
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Cloud Infrastructure ◽  
Solution Quality ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
Selection For ◽  
Stochastic Development ◽  
Data Centres

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

2021 ◽  
pp. 165-174
Author(s):  
Ahmed A. A. Gad-Elrab ◽  
Tamer A.A. Alzohairy ◽  
Kamal R. Raslan ◽  
Farouk A. Emara
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Heuristic Methods ◽  
Flexible Scheduling ◽  
Np Completeness ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
User Tasks ◽  
Available Resources

Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.

The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing

2011 ◽  
Vol 216 ◽  
pp. 111-115 ◽  
Author(s):  
Yun Xia Pei ◽  
Yue Zhang
Keyword(s):  
Human Resources ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimization Procedure ◽  
Map Reduce ◽  
Research Issues ◽  
Job Management ◽  
Task Scheduling Algorithm ◽  
Simulation Results

As a rapid developing infrastructure, the grid can share widely distributed computing, storage, data and human resources. In order to improve the usability and QoS of the grid, the job management in the grid is very important, and becomes one of the key research issues in grid computing. Map-Reduce provide an efficient and easy-to-use framework for parallelizing the global optimization procedure. The simulation results show the usefulness and effectiveness of our task scheduling algorithm.

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

2016 ◽  
Vol 6 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Sohan Kumar Pande ◽  
Sanjaya Kumar Panda ◽  
Satyabrata Das
Keyword(s):  
Customer Satisfaction ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Computing Systems ◽  
Customer Expectation ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Simulation Results ◽  
Multi Cloud

Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

scholarly journals Review Paper on Fault Tolerant Scheduling in Multicore System

2018 ◽  
pp. 75-83
Keyword(s):  
Real Time ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Real Time Systems ◽  
Recovery Method ◽  
Agricultural Vulnerability ◽  
Task Scheduling Algorithm ◽  
Multicore System ◽  
User Space ◽  
Time Systems

In this paper, it was discussed about various fault tolerant task scheduling Algorithm for the multicore system based on hardware and software. Blend of triple module redundancy and double module redundancy considering Agricultural vulnerability factor other than EDF and LLF scheduling algorithms were used to create hardware-based algorithm. Most of the real-time systems used shared memory as dominant part. Low overhead software-based fault tolerance approach could be implemented at user space level so that it did not require any changes at an application level. Redundant multithread processes were used which could detect soft recover from the errors and could recover from them giving low overhead, fast error mechanism recovery, and detection. The overhead incurred by this method ranged from 0 to 8% for selected benchmarks. Another system used for scheduling approach in real-time systems was hybrid scheduling. Dynamic fault tolerating scheduling gave high feasibility where task critically was used to select the fault recovery method type in order to tolerate maximum no. of faults.

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.

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