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

Xu, Luhua; Qiao, Jianzhong; Lin, Shukuan; Wang, Xiaowei

doi:10.3390/sym12050862

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…Xu et al 37 proposed a VC‐based task scheduling to maximize volunteers' benefits. Initially, all the tasks are scheduled in the list based on their priority and deadline using the maximum benefit scheduling algorithm which provides more benefits to volunteer devices as only allowable tasks are completed while the others are pruned out.…”

Section: Literature Surveymentioning

confidence: 99%

Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Jangu,

Raza

2023

Concurrency and Computation

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Fog computing has become an effective platform for computing delay‐sensitive IoT tasks. However, the increased scalability of IoT devices () makes it difficult for fog nodes to perform better. Volunteer computing (VC) has emerged as a supportive technology in which resource‐capable s, such as computers and laptops, share their idle resources to compute the IoT tasks. However, in VC‐based approaches, improper selection of volunteer nodes (VNs) may result in an increased failure rate and delay. To address these challenges, this work proposes a Smart Admission Control strategy utilizing volunteer‐enabled Fog‐Cloud computing (SAC‐VFC). The VNs are selected based on grey TOPSIS ranking. The incoming tasks are classified based on priority and delay and then scheduled using the Improved Jellyfish Algorithm (IJFA). Smart gateway (SGW) and fog manager (FM) act as mediators for allocating tasks among voluntary, fog, and cloud resources. FM performs a similarity‐based clustering of fog nodes using the enhanced Fuzzy C Means clustering (EFCM) algorithm to manage resources. Simulation study suggests the superior performance of SAC‐VFC over peers under comparison in terms of average delay, average makespan, success rate of tasks and tasks satisfying the deadline metrics.

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Section: Literature Surveymentioning

confidence: 99%

Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Jangu,

Raza

2023

Concurrency and Computation

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“…The authors of [23] suggested research on the maximum gains for volunteer computing platforms (VCPs). VCPs can be considered asymmetric multiprocessing systems (AMSs).…”

Section: Related Workmentioning

confidence: 99%

“…Then, the algorithm arranges tasks in non-increasing order based on their cost function V, grouped by shared resources (lines [23][24][25][26][27][28][29][30][31][32]. The output of BABP is the task set where the tasks are sorted depending on their cost function.…”

Section: Blocking-aware-based Partitioning (Babp) Algorithmmentioning

confidence: 99%

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

et al. 2021

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Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

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“…Main aim of JSP is to minimize the makespan [10]. In multi-objective optimization, two or more conflicting objectives are optimized with a given set of constraints simultaneously.…”

Section: A Related Workmentioning

confidence: 99%

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

Tağtekin,

Öztürk,

Sezer

2021

Preprint

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Nowadays, DevOps pipelines of huge projects are getting more and more complex. Each job in the pipeline might need different requirements including specific hardware specifications and dependencies. To achieve minimal makespan, developers always apply as much machines as possible. Consequently, others may be stalled for waiting resource released. Minimizing the makespan of each job using a few resource is a challenging problem. In this study, it is aimed to 1) automatically determine the priority of jobs to reduce the waiting time in the line, 2) automatically allocate the machine resource to each job. In this work, the problem is formulated as a multi-objective optimization problem. We use GA algorithm to automatically determine job priorities and resource demand for minimizing individual makespan and resource usage. Finally, the experimental results show that our proposed priority list generation algorithm is more effective than current priority list producing method in the aspects of makespan and allocated machine count.

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

Cited by 5 publications

References 44 publications

Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

A Case Study: Using Genetic Algorithm for Job Scheduling Problem

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