Efficient task pruning mechanism to improve robustness of heterogeneous computing systems

Denninnart, Chavit; Gentry, James A.; Mokhtari, Ali; Salehi, Mohsen Amini

doi:10.1016/j.jpdc.2020.03.018

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…In a series of publications [30], [31], [32], [33], [34], job pruning techniques have been investigated. The authors consider an oversubscribed system to which jobs are submitted at random times.…”

Section: Queuing Networkmentioning

confidence: 99%

Dynamic Scheduling Strategies for Firm Semi-Periodic Real-Time Tasks

Gao

Pallez

Robert

et al. 2023

IEEE Trans. Comput.

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This paper introduces and assesses novel strategies to schedule firm semi-periodic real-time tasks. Jobs are released periodically and have the same relative deadline. Job execution times obey an arbitrary probability distribution and can take either bounded or unbounded values. We investigate several optimization criteria, the most prominent being the Deadline Miss Ratio (DMR). All previous work uses some admission policies but never interrupt the execution of an admitted job before its deadline. On the contrary, we introduce three new control parameters to dynamically decide whether to interrupt a job at any given time. We derive a Markov model and use its stationary distribution to determine the best value of each control parameter. Finally we conduct an extensive simulation campaign with 16 different probability distributions. The results nicely demonstrate how the new strategies help improve system performance compared with traditional approaches. In particular, we show that (i) compared to pre-execution admission rules, the control parameters make significantly better decisions; (ii) specifically, the key control parameter is to upper bound the waiting time of each job; (iii) the best scheduling strategy decreases the DMR by up to 0.35 over traditional competitors.

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Section: Queuing Networkmentioning

confidence: 99%

Dynamic Scheduling Strategies for Firm Semi-Periodic Real-Time Tasks

Gao

Pallez

Robert

et al. 2023

IEEE Trans. Comput.

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“…In [27], the authors employed linear weighted sum techniques to minimize both energy and makespan. In [3], [28], the probabilistic approach is used to determine the probability of on-time completion of tasks on available computing resources. Then, a task dropping mechanism is deployed to maximize the system's robustness.…”

Section: Related Workmentioning

confidence: 99%

FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Mokhtari¹,

Hossen²,

Jamshidi³

et al. 2022

Preprint

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Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multiobjective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

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“…Job pruning in heterogeneous computing systems: In a series of publications [5], [6], [10], [17], [20], job pruning techniques have been investigated. The authors consider an oversubscribed system to which jobs are submitted at random times.…”

Section: Related Workmentioning

confidence: 99%