Energy-Aware Task Migration Through Ant-Colony Optimization for Multiprocessors

Rupanetti, Dulana; Salamy, Hassan

doi:10.1109/uemcon53757.2021.9666584

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Besides DVFS and DPM, task migration is also used in multi-core platforms to optimize energy consumption. More precisely, task migration is introduced into the allocation/scheduling process to deploy independent tasks on homogeneous platforms for optimizing energy consumption [15]- [17], where [15] mainly focuses on task allocation, while [16] and [17] consider both task allocation and task scheduling. As the processors are homogeneous and the tasks are independent, task migration in the above studies is usually used to minimize the overall energy consumption.…”

Section: A Related Workmentioning

confidence: 99%

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Kritikakou

et al. 2023

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

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Heterogeneous multi-core platforms, such as ARM big.LITTLE are widely used to execute embedded applications under multiple and contradictory constraints, such as energy consumption and real-time execution. To fulfill these constraints and optimize system performance, application tasks should be efficiently mapped on multi-core platforms. Embedded applications are usually tolerant to approximated results but acceptable Quality-of-Service (QoS). Modeling embedded applications by using the elastic task model, namely, Imprecise Computation (IC) task model, can balance system QoS, energy consumption, and real-time performance during task deployment. However, stateof-the-art approaches seldom consider the problem of IC task deployment on heterogeneous multi-core platforms. They typically neglect task migration, which can improve the solutions due to its flexibility during the task deployment process. This paper proposes a novel QoS-aware task deployment method to maximize system QoS under energy and real-time constraints, where frequency assignment, task allocation, scheduling, and migration are optimized simultaneously. The task deployment problem is formulated as mixed-integer non-linear programming. Then, it is linearized to mixed-integer linear programming to find the optimal solution. Furthermore, based on problem structure and problem decomposition, we propose a novel heuristic with low computational complexity. The sub-problems regarding frequency assignment, task allocation, scheduling, and adjustment are considered and solved in sequence. Finally, the simulation results show that the proposed task deployment method improves the system QoS by 31.2% on average (up to 112.8%) compared to the state-of-theart methods and the designed heuristic achieves about 53.9% (on average) performance of the optimal solution with a negligible computing time.

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Section: A Related Workmentioning

confidence: 99%

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Kritikakou

et al. 2023

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

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“…Many works tried to control CPU temperature reduction by using bio-inspired algorithms as such, Rupanetti and Salamy [4] proposed a novel strategy for task migration in multiprocessors by combining modified ant colony algorithm (ACO) and first-fit task allocation heuristic. The ACO algorithm tries to split tasks and migrate them to processors with low task utilization to minimize the overall power consumption of the multiprocessor system on a chip (MPSoC).…”

Section: Introductionmentioning

confidence: 99%

Thermal aware task assignment for multicore processors using genetic algorithm

Parwez,

Sulaiman

2023

IJECE

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Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors.

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Energy-Aware Task Migration Through Ant-Colony Optimization for Multiprocessors

Cited by 2 publications

References 17 publications

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Thermal aware task assignment for multicore processors using genetic algorithm

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