scholarly journals Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5906
Author(s):  
Roxana-Gabriela Stan ◽  
Lidia Băjenaru ◽  
Cătălin Negru ◽  
Florin Pop
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithms ◽  
Turnaround Time ◽  
Task Demands ◽  
Evaluation Framework ◽  
Raspberry Pi ◽  
Large Sets ◽  
Mean Waiting Time ◽  
Computational Resources

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

A Replica Based Co-Scheduler (RBS) for Fault Tolerant Computational Grid

2011 ◽  
pp. 101-116
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Fault Tolerance ◽  
Performance Optimization ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Fault Tolerant ◽  
Turnaround Time ◽  
Computational Grid ◽  
Successful Execution ◽  
Computational Resources ◽  
The Cost

Grid is a parallel and distributed computing network system comprising of heterogeneous computing resources spread over multiple administrative domains that offers high throughput computing. Since the Grid operates at a large scale, there is always a possibility of failure ranging from hardware to software. The penalty paid of these failures may be on a very large scale. System needs to be tolerant to various possible failures which, in spite of many precautions, are bound to happen. Replication is a strategy often used to introduce fault tolerance in the system to ensure successful execution of the job, even when some of the computational resources fail. Though replication incurs a heavy cost, a selective degree of replication can offer a good compromise between the performance and the cost. This chapter proposes a co-scheduler that can be integrated with main scheduler for the execution of the jobs submitted to computational Grid. The main scheduler may have any performance optimization criteria; the integration of co-scheduler will be an added advantage towards fault tolerance. The chapter evaluates the performance of the co-scheduler with the main scheduler designed to minimize the turnaround time of a modular job by introducing module replication to counter the effects of node failures in a Grid. Simulation study reveals that the model works well under various conditions resulting in a graceful degradation of the scheduler’s performance with improving the overall reliability offered to the job.

Priority-Based Task Scheduling in the Cloud Systems Using a Memetic Algorithm

2016 ◽  
Vol 25 (10) ◽  
pp. 1650119 ◽  
Author(s):  
Bahman Keshanchi ◽  
Nima Jafari Navimipour
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Memetic Algorithm ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Cloud Environment ◽  
Distributed Computing Systems ◽  
Complete Problems ◽  
Np Complete ◽  
A Priority

Task scheduling is one of the major issues to achieve high performance in distributed systems such as Grid, Peer-to-Peer and cloud environment. Generally, there are two phases in heuristics-based task scheduling algorithms in heterogeneous distributed computing systems (HeDCSs). These phases are task prioritization and processor assigning respectively. Heuristic-based task scheduling algorithms may use different policies to assign priority to subtasks which produce different makespans in a heterogeneous computing system. Thus, a suitable scheduling algorithm is one that can efficiently assign a priority to tasks in order to minimize makespan. Recently, memetic algorithms (MAs) have been used as evolutionary or population-based global search approaches with local search heuristic to optimize NP-complete problems. Recent studies on MAs have discovered their success on a wide variety of real-world problems. Since the task scheduling problem is an NP-complete, in this paper, a new task scheduling algorithm on cloud environment using multiple priority queues and a memetic algorithm (MPQMA) is proposed. The proposed method uses a genetic algorithm (GA) along with hill climbing to assign a priority to each subtask while using a heuristic-based earliest finish time (EFT) approach to search for a solution for the task-to-processor mapping. The basic idea of our approach is using the advantage of MA to increase the convergence speed of the solutions. We implemented the algorithm on Azure Cloud Service by C# language where the experimental results for the set of randomly generated graphs revealed that the proposed MPQMA algorithm outperformed the existing three task scheduling algorithms in terms of makespan with fast convergence to the optimized solution.

scholarly journals HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Guan Wang ◽  
Yuxin Wang ◽  
Hui Liu ◽  
He Guo
Keyword(s):  
Task Scheduling ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Selection Policy ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Task Priority ◽  
Low Efficiency

High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable; (2) an entry task duplication selection policy to make the makespan shorter; and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.

QoS aware Multi-Path Routing using Link Scheduling Algorithm

2019 ◽  
Vol 09 ◽  
Author(s):  
Satyasrikanth Palle ◽  
Shivashankar
Keyword(s):  
Cellular Networks ◽  
Scheduling Algorithm ◽  
Research Work ◽  
Scheduling Algorithms ◽  
Turnaround Time ◽  
Link Scheduling ◽  
Spatial Reuse ◽  
Mobile Cellular ◽  
Simulation Results ◽  
End To End

Objective: The demand for Cellular based multimedia services is growing day by day, in order to fulfill such demand the present day cellular networks needs to be upgraded to support excessive capacity calls along with high data accessibility. Analysis of traffic and huge network size could become very challenging issue for the network operators for scheduling the available bandwidth between different users. In the proposed work a novel QoS Aware Multi Path scheduling algorithm for smooth CAC in wireless mobile networks. The performance of the proposed algorithm is assessed and compared with existing scheduling algorithms. The simulation results show that the proposed algorithm outperforms existing CAC algorithms in terms of throughput and delay. The CAC algorithm with scheduling increases end-to-end throughput and decreases end-to-end delay. Methods: The key idea to implement the proposed research work is to adopt spatial reuse concept of wireless sensor networks to mobile cellular networks. Spatial reusability enhances channel reuse when the node pairs are far away and distant. When Src and node b are communicating with each other, the other nodes in the discovered path should be idle without utilizing the channel. Instead the other nodes are able to communicate parallelly the end-to-end throughput can be improved with acceptable delay. Incorporating link scheduling algorithms to this key concept further enhances the end-to-end throughput with in the turnaround time. So, in this research work we have applied spatial reuse concept along with link scheduling algorithm to enhance end-to-end throughput with in turnaround time. The proposed algorithm not only ensures that a connection gets the required bandwidth at each mobile node on its way by scheduling required slots to meet the QoS requirements. By considering the bandwidth requirement of the mobile connections, the CAC module at the BS not only considers the bandwidth requirement but also conforming the constrains of system dealy and jitter are met. Result: To verify the feasibility and effectiveness of our proposed work, with respect to scheduling the simulation results clearly shows the throughput improvement with Call Admission Control. The number of dropped calls is significantly less and successful calls are more with CAC. The percentage of dropped calls is reduced by 9 % and successful calls are improved by 91%. The simulation is also conducted on time constraint and ratio of dropped calls are shown. The total time taken to forward the packets and the ration of dropped calls is less when compared to non CAC. On a whole the CAC with scheduling algorithms out performs existing scheduling algorithms. Conclusion: In this research work we have proposed a novel QoS aware scheduling algorithm that provides QoS in Wireless Cellular Networks using Call Admission Control (CAC). The simulation results show that the end-to-end throughput has been increased by 91% when CAC is used. The proposed algorithm is also compared with existing link scheduling algorithms. The results reveal that CAC with scheduling algorithm can be used in Mobile Cellular Networks in order to reduce packet drop ratio. The algorithm is also used to send the packets within acceptable delay.

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