DVFS-Based Quality Maximization for Adaptive Applications With Diminishing Return

Yu, Heng; Ha, Yajun; Veeravalli, Bharadwaj; Chen, Fupeng; El-Sayed, Hesham

doi:10.1109/tc.2020.2997242

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…Remark 3.1: Transforming dual problem from (2) to (4), we avoid solving s ik directly since s ik can be expressed by other variables α, β, γ and µ. (7) and (8) show that by properly solving the problem of minimizing Lagrangian, we can cut the feasible region of s ik and force s ik to be a binary variable.…”

Section: Optimal Qos-aware Task Mapping Methodsmentioning

confidence: 99%

“…On the contrary, the QoS-aware task mapping approaches [2], [3], [6]- [10] use the IC task model to maximize system QoS under real-time and/or energy constraints. For modeling the system QoS, the most realistic approaches are based on the linear function [2], [3], [6], [7] and the concave function [8]- [10]. The concave function is more general since it can characterize more extensive applications than the linear function [11].…”

Section: Introductionmentioning

confidence: 99%

“…The concave function is more general since it can characterize more extensive applications than the linear function [11]. However, existing QoS-aware approaches that model QoS through concave functions focus on either uni-processor platforms [10] or with fixed task-to-processor allocation [8] and omit energy-related issues [9]. To address this limitation, we propose a QoSaware mapping problem for IC tasks with the general concave function on heterogeneous multicore platforms under energy consumption and real-time constraints.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

Mo,

Li,

Kritikakou

et al. 2024

IEEE Trans. Circuits Syst. II

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Heterogeneous multicore architectures have become one of the most widely used hardware platforms for embedded systems, where time, energy, and system QoS are the major concerns. The Imprecise Computation (IC) model splits a task into mandatory and optional parts, allowing the trade-off of the above issues. However, existing approaches, to maximize system QoS (Quality-of-Service) under time or energy constraints, use a linear function to model system QoS. Therefore, they become unsuitable for general applications, whose QoS is modeled by a concave function. To deal with this limitation, this work addresses the Mixed-Integer Non-Linear Programming (MINLP) problem of mapping IC tasks to a set of heterogeneous cores by concurrently deciding which processor executes each task and the number of cycles of optional tasks (i.e., task allocation and scheduling), under real-time and energy supply constraints. Furthermore, as existing solution algorithms either demand high time complexity or only achieve feasible solutions, we propose a novel approach based on problem transformation and dual decomposition that finds an optimal solution while avoiding high computational complexity. Simulation results show that the proposed approach achieves 98% performance of the optimization solver Gurobi, but only with 19.8% of its computation time.

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Section: Optimal Qos-aware Task Mapping Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

Mo,

Li,

Kritikakou

et al. 2024

IEEE Trans. Circuits Syst. II

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“…These divisions are distributed to multiple servers on the networked system through a reasonable task-scheduling strategy to complete parallel computing, thus shortening the makespan of the entire workload. The DLT has been successfully applied in various big data-related fields, such as image processing [ 5 ], dynamic voltage and frequency regulation [ 6 ], signature searching [ 7 ], data flow optimization [ 8 ], real-time video encoding [ 9 ], and other typical big data application problems.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Heterogeneous Server Utilization with Limited Availability Times for Divisible Loads Scheduling on Networked Systems

Wang

Veeravalli

Song

et al. 2023

Sensors

Self Cite

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Most of the available divisible-load scheduling models assume that all servers in networked systems are idle before workloads arrive and that they can remain available online during workload computation. In fact, this assumption is not always valid. Different servers on networked systems may have heterogenous available times. If we ignore the availability constraints when dividing and distributing workloads among servers, some servers may not be able to start processing their assigned load fractions or deliver them on time. In view of this, we propose a new multi-installment scheduling model based on server availability time constraints. To solve this problem, we design an efficient heuristic algorithm consisting of a repair strategy and a local search strategy, by which an optimal load partitioning scheme is derived. The repair strategy guarantees time constraints, while the local search strategy achieves optimality. We evaluate the performance via rigorous simulation experiments and our results show that the proposed algorithm is suitable for solving large-scale scheduling problems employing heterogeneous servers with arbitrary available times. The proposed algorithm is shown to be superior to the existing algorithm in terms of achieving a shorter makespan of workloads.

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Quality Optimization of Adaptive Applications via Deep Reinforcement Learning in Energy Harvesting Edge Devices

Chen

Jiang

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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DVFS-Based Quality Maximization for Adaptive Applications With Diminishing Return

Cited by 4 publications

References 38 publications

Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

Maximizing Heterogeneous Server Utilization with Limited Availability Times for Divisible Loads Scheduling on Networked Systems

Quality Optimization of Adaptive Applications via Deep Reinforcement Learning in Energy Harvesting Edge Devices

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