Energy-Aware Task Scheduling Approach Using DVFS and Particle Swarm Optimization for Heterogeneous Multicore Processors

K, Siddesha; Jayaramaiah, G.V.

doi:10.1007/978-981-16-1342-5_75

Cited by 1 publication

(3 citation statements)

References 19 publications

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“…Compared with [12], the approach in [11] mainly focuses on system-level DVFS, and the frequency of each processor cannot be adjusted individually. In [13] and [14], DVFS is combined into the task allocation process to minimize energy consumption under real-time constraints. Besides DVFS and DPM, task migration is also used in multi-core platforms to optimize energy consumption.…”

Section: A Related Workmentioning

confidence: 99%

“…Considering the overhead of task migration, extra energy E M i and time t M i should be added to problem (14). First, task end time is updated by te 2i−1 = te 2i−1 + α i t M i , where binary variable α i denotes task τ i is migrated or not.…”

Section: Continuous Variablesmentioning

confidence: 99%

“…Finally, energy constraint ( 13) is rewritten as E total + i∈N α i E M i ≤ E buget . Moreover, the proposed approach can be extended to completely heterogeneous platforms by modifying the constraints in problem (14), where we assume that the platforms contain multiple clusters and each cluster has a single core.…”

Section: Continuous Variablesmentioning

confidence: 99%

See 2 more Smart Citations

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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