scholarly journals Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 244 ◽  
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Ruihua Qi
Keyword(s):  
Completion Time ◽  
Fuzzy Model ◽  
Task Assignment ◽  
Experimental Results ◽  
Volunteer Computing ◽  
Time Interval ◽  
Reliability Model ◽  
Assignment Algorithm ◽  
Computing Platforms ◽  
Task Segmentation

In volunteer computing (VC), the expected availability time and the actual availability time provided by volunteer nodes (VNs) are usually inconsistent. Scheduling tasks with precedence constraints in VC under this situation is a new challenge. In this paper, we propose two novel task assignment algorithms to minimize completion time (makespan) by a flexible task assignment. Firstly, this paper proposes a reliability model, which uses a simple fuzzy model to predict the time interval provided by a VN. This reliability model can reduce inconsistencies between the expected availability time and actual availability time. Secondly, based on the reliability model, this paper proposes an algorithm called EFTT (Earliest Finish Task based on Trust, EFTT), which can minimize makespan. However, EFTT may induce resource waste in task assignment. To make full use of computing resources and reduce task segmentation rate, an algorithm IEFTT (improved earliest finish task based on trust, IEFTT) is further proposed. Finally, experimental results verify the efficiency of the proposed algorithms.

scholarly journals Research on the Task Assignment Problem with Maximum Benefits in Volunteer Computing Platforms

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 862
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Xiaowei Wang
Keyword(s):  
Assignment Problem ◽  
Low Cost ◽  
Task Assignment ◽  
Task Completion ◽  
Volunteer Computing ◽  
Computing Services ◽  
Pruning Strategy ◽  
Computing Platforms ◽  
Deadline Constraints ◽  
Task Assignment Problem

As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

Adaptive Control of Redundant Task Execution for Dependable Volunteer Computing

2011 ◽  
pp. 135-154
Author(s):  
Hong Wang ◽  
Yoshitomo Murata ◽  
Hiroyuki Takizawa ◽  
Hiroaki Kobayashi
Keyword(s):  
Adaptive Control ◽  
Failure Probability ◽  
Task Assignment ◽  
Probability Estimation ◽  
Volunteer Computing ◽  
Data Sets ◽  
Task Dependency ◽  
Computing Platforms ◽  
Multiple Task ◽  
Dispatch Policy

On the volunteer computing platforms, inter-task dependency leads to serious performance degradation for failed task re-execution because of volatile peers. This paper discusses a performance-oriented task dispatch policy based on the failure probability estimation. The tasks with the highest failure probabilities are selected for dispatch when multiple task enquiries come to the dispatcher. The estimated failure probability is used to find the optimized task assignment that minimizes the overall failure probability of these tasks. This performance-oriented task dispatch policy is evaluated with two real world trace data sets on a simulator. Evaluation results demonstrate the effectiveness of this policy.

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

scholarly journals Max-Min Processors Scheduling

2021 ◽  
Vol 50 (1) ◽  
pp. 5-12
Author(s):  
Hani Alquhayz ◽  
Mahdi Jemmali
Keyword(s):  
Literature Review ◽  
Knapsack Problem ◽  
Completion Time ◽  
Research Work ◽  
Parallel Processors ◽  
Experimental Results ◽  
Running Time ◽  
Similar Performance ◽  
Minimum Completion Time ◽  
Randomization Method

This paper focuses on the maximization of the minimum completion time on identical parallel processors. The objective of this maximization is to ensure fair distribution. Let a set of jobs to be assigned to several identical parallel processors. This problem is shown as NP-hard. The research work of this paper is based essentially on the comparison of the proposed heuristics with others cited in the literature review. Our heuristics are developed using essentially the randomization method and the iterative utilization of the knapsack problem to solve the studied problem. Heuristics are assessed by several instances represented in the experimental results. The results show that the knapsack based heuristic gives almost a similar performance than heuristic in a literature review but in better running time.  

Research on the Non-Probabilistic Reliability Based on Interval Model

2012 ◽  
Vol 166-169 ◽  
pp. 1908-1912 ◽  
Author(s):  
Ai Rong Zhang ◽  
Xiao Liu
Keyword(s):  
Minimum Distance ◽  
Fuzzy Model ◽  
Uncertain Parameters ◽  
Reliability Model ◽  
Probabilistic Theory ◽  
Interval Model ◽  
Uncertain Variables ◽  
Sample Data ◽  
Failure Region ◽  
Robust Reliability

Due to the dependence of the sample data for a probabilistic reliability model and the fuzzy model, the interval model was used to describe the uncertain parameters through which a new measure of non-probabilistic reliability was established. Studying the model with the non-probabilistic theory, a new measure of non-probabilistic reliability was established which was the minimum distance between the failure region and the total region constructed by all uncertain variables. This kind of measure not only is consistent with the criterion of the non-probabilistic robust reliability, but also has a clearer meaning. The validity and the feasibility were proved through a computational example.

A Dynamic-Programming-Styled Algorithm for a Class of Multi-Agent Optimal Task Assignment

2001 ◽  
Author(s):  
Guang Yang ◽  
Vikram Kapila ◽  
Ravi Vaidyanathan
Keyword(s):  
Dynamic Programming ◽  
Task Assignment ◽  
Research Activity ◽  
Spacecraft Formation ◽  
Assignment Algorithm ◽  
Decentralized Decision Making ◽  
Current Research Activity ◽  
Multi Agent ◽  
Global Optimal ◽  
Multiple Spacecraft

Abstract In this paper, we use a dynamic programming formulation to address a class of multi-agent task assignment problems that arise in the study of fuel optimal control of multiple agents. The fuel optimal multi-agent control is highly relevant to multiple spacecraft formation reconfiguration, an area of intense current research activity. Based on the recurrence relation derived from the celebrated principle of optimality, we develop an algorithm with a distributed computational architecture for the global optimal task assignment. In addition, we propose a communication protocol to facilitate decentralized decision making among agents. Illustrative studies are included to demonstrate the efficacy of the proposed multi-agent optimal task assignment algorithm.

Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction

2012 ◽  
pp. 225-252
Author(s):  
Nurcin Celik ◽  
Esfandyar Mazhari ◽  
John Canby ◽  
Omid Kazemi ◽  
Parag Sarfare ◽  
...  
Keyword(s):  
Execution Time ◽  
Large Scale ◽  
Time Synchronization ◽  
Computational Grid ◽  
Experimental Results ◽  
Time Interval ◽  
Computational Power ◽  
Large Scale Systems ◽  
Large Scale Simulations ◽  
Reduce Execution Time

Simulating large-scale systems usually entails exhaustive computational powers and lengthy execution times. The goal of this research is to reduce execution time of large-scale simulations without sacrificing their accuracy by partitioning a monolithic model into multiple pieces automatically and executing them in a distributed computing environment. While this partitioning allows us to distribute required computational power to multiple computers, it creates a new challenge of synchronizing the partitioned models. In this article, a partitioning methodology based on a modified Prim’s algorithm is proposed to minimize the overall simulation execution time considering 1) internal computation in each of the partitioned models and 2) time synchronization between them. In addition, the authors seek to find the most advantageous number of partitioned models from the monolithic model by evaluating the tradeoff between reduced computations vs. increased time synchronization requirements. In this article, epoch- based synchronization is employed to synchronize logical times of the partitioned simulations, where an appropriate time interval is determined based on the off-line simulation analyses. A computational grid framework is employed for execution of the simulations partitioned by the proposed methodology. The experimental results reveal that the proposed approach reduces simulation execution time significantly while maintaining the accuracy as compared with the monolithic simulation execution approach.

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