Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

Liu, Yifan; Du, Chenglie; Chen, Jinchao; Du, Xixun

doi:10.1002/cpe.6520

Cited by 8 publications

(8 citation statements)

References 53 publications

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“…Where EFT (T i ) represents the earliest completion time of T i , EST (T i ) represents the earliest start time of T , and W ikr represents the calculation time of T i on s k at a given power frequency f kr . Then, W ikr can be defined as: The model adopts exponential function and expresses the reliability of the task based on the execution time and failure rate of the task [27]. When the task is executed, the failure rate may be expressed in ξ, related only to hardware parameters.…”

Section: Task Modelmentioning

confidence: 99%

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Liu,

Ding

2024

Preprint

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Heterogeneous multi-core processor has the ability to switch between differenttypes of cores to perform tasks, which provides more space and possibility forrealizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systemswith high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reductionmay lead to an increase in transient failure rates, reducing system reliability. Inorder to solve this problem, this paper studies the energy optimal schedulingproblem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO),which improves the efficiency of heterogeneous task scheduling and shortens thetask completion time. The algorithm uses the learning and chaos perturbationstrategies based on opposition and crossover strategies to balance the search andutilization capabilities, and can further improve the performance of OIWHO.Compared with previous work, our proposed algorithm has more advantagesthan existing algorithms. Experimental results show that the average computingtime of OIWHO algorithm is 13.38%, 10.90%, 6.97%, 2.39% and 3.21% fasterthan QHA, PSO, GWO, LFD and OIWOA, respectively. Especially when solvinglarge-scale problems, our algorithm takes less time than other algorithms.

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Section: Task Modelmentioning

confidence: 99%

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Liu,

Ding

2024

Preprint

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“…These methods calculate the priority of tasks based on a set of predefined rules and subsequently assign them to available resources. Meta-heuristic methods include genetic algorithm [15], ant colony optimization algorithm [20][21], particle swarm optimization [13], and etc. The meta-heuristic algorithm is a process of simulating natural behavior in the solution space to find better solutions.…”

Section: Related Workmentioning

confidence: 99%

“…How to sort ready tasks is one of the important issues in this work. Different from the other works [15][16] that only consider one DAG and use the rank u as tasks' priority, in this work, the tasks in ready task list RT L are from different DAGs and have different deadlines. In order to ensure that as many DAGs as possible can be completed in time, we define urgency(v s i ) as the original priority of tasks in RT L, can be calculated by…”

Section: Task Preprocessing Phasementioning

confidence: 99%

Online deadline-constrained scheduling of parallel applications in CPSs

Liu

Chen

et al. 2023

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As a cutting-edge distributed intelligent system that seamlessly combines and collaborates between the information world and the physical world, cyber-physical systems (CPSs) possess vast practical applications. Given that CPSs are characterized by constant interaction with their surrounding environment, fluctuations in resource performance triggered by external changes present significant challenges for achieving optimal application scheduling. To harness the potential of heterogeneous and capacity-limited computing resources in CPSs, applications must be precisely modeled and allocated to resources to enhance their execution efficiency, even under uncertain conditions. Against this backdrop, this paper focuses on the online deadline-constrained scheduling problem of parallel applications and proposes a novel uncertainty-aware scheduling algorithm to schedule the applications in the changing environment. Firstly, parallel applications are expertly modeled as directed acyclic graphs (DAGs), and a novel ranking methodology is employed to generate task priorities based on their urgency of completion. Subsequently, the optimal resources are meticulously chosen for each task to expedite execution, supplemented by a discarding mechanism to amplify the likelihood of successful application. Finally, the priority of tasks and resource status is updated based on environmental feedback to expertly tackle uncertainties. Experiments with randomly-generated applications conclusively demonstrate that the proposed approach boasts superior scheduling performance compared to similar algorithms.

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“…Energy Consumption Calculation. Each resource R j has the computing capabilities C j and a power consumption P j ; the energy consumption usually varies with time and power consumption when a task is running on a resource [30,31]. The energy consumption E j of resource R j is computed as follows:…”

Section: Abstractimilarity Calculationmentioning

confidence: 99%

Ontology-Based Resource Modeling and Matching Framework in Avionics Systems

Chen

et al. 2022

International Journal of Aerospace Engineering

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In order to realize rapid service-oriented construction of resources and efficient supply-demand matching in avionics systems, an ontology-based resource modeling and matching framework in avionics systems is proposed. First, this paper constructs a hierarchical resource organization framework based on virtual resource pool, covering dispersed and heterogeneous avionics resources and shielding the differences and diversity of resources; next, introduces ontology technology into resource modeling and encapsulates resources as services using the idea of “resource as a service,” facilitating the access and control of resources; in addition, proposes a matching framework which takes into account the energy consumption, in which it firstly tries to find a list of resources which can meet task requirements by a stepwise intelligent matching strategy based on semantic similarity, and the resources in the list will then be sorted according to the energy consumption. Finally, a radar detection case is taken to demonstrate that the proposed model and method are feasible and effective.

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Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

Cited by 8 publications

References 53 publications

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Online deadline-constrained scheduling of parallel applications in CPSs

Ontology-Based Resource Modeling and Matching Framework in Avionics Systems

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