Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

Jiang, Enda; Wang, Ling; Wang, Jing-jing

doi:10.26599/tst.2021.9010007

Cited by 88 publications

(29 citation statements)

References 28 publications

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“…tmax 0 p (t) .dt, P total = P static + P dynamic (12) P dynamic depicts the charging and discharging of the output capacitance while the chip is working and is related to the switching activity α and c a load capacitance V represents the supply voltage and f defines operational frequency as mentioned below in Eq. (13).…”

Section: Cpu Power/energy Modelmentioning

confidence: 99%

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An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Khan¹,

Din²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multicore system. High utilization factor (u i ) on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on u i 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

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Section: Cpu Power/energy Modelmentioning

confidence: 99%

“…CMOS circuits consume less power as compared to other devices which are using just one type of transistor. This property makes them unique for embedded systems which are mostly used in battery-operated [13].…”

Section: Introductionmentioning

confidence: 99%

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Khan¹,

Din²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A decrease in the chip size of a multi-core processor certainly increases the number of transistors on a chip more rapidly than before. The chip can require more energy due to which a gradual increase in power density is observed that affects the reliability of a multi-core processor [13]. Scheduling and task allocation techniques are facing issues during the migration of tasks to balance and manage power on multiprocessor systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for燞omogeneous MPSoCs

Khan¹,

Din²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%-15% because excessive on-chip temperature shortens the chip's life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor (u i ) on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%-4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

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“…Its literature is listed in References 28‐30. Among scientific workflow scheduling heuristic on multiple heterogeneous resources, HEFT 13 is the lightweight well‐know heuristics that have been widely employed by other heuristics for cost reduction, 10 for energy consumption reduction, 30‐32 for privacy 33 and encryption, 34 for clustering 35 and data partitioning 36 . The main objective of HEFT is to find a fair schedule that uses the possible minimum execution time by following the task prioritization technique and task mapping technique under low complexity.…”

Section: Related Workmentioning

confidence: 99%

Deadline‐constrained cost‐energy aware workflow scheduling in cloud

Bugingo

Zheng

Lei

et al. 2021

Concurrency and Computation

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Nowadays, scientists are dealing with large-scale scientific workflows that need a high processing capacity platform to facilitate on-time completion. Cloud computing is the ideal platform to overcome this problem as it has several resources that scientists may choose from depending on the size of their applications. However, using cloud computing requires some monetary charges. Recently cloud computing providers started a new pricing schema that offers to their users a set of resources with specific combinations of CPU frequency configurations settings and price. The selected configurations settings reflect energy consumption. Besides, the configuration selection to meet users' satisfaction (minimum cost) and providers' satisfaction (energy saving) is crucial. Therefore, a multiobjective (cost and energy) efficient mechanism is essential. In this article, we address an important novel problem concerning multiobjective deadline constrained workflow scheduling in the cloud. We first study the relationship between cost minimization and minimization of the energy consumption in a cloud environment, and then discuss, develop, and propose an algorithm with two variants to help the system satisfy both sides (users and providers) at the same time during the selection of the configuration. The proposed heuristic is evaluated using specified real-world applications. The observed results indicate that our heuristic can reduce significantly the energy consumption and the cost at the same time. K E Y W O R D Scloud computing, cost-efficient workflow scheduling, deadline constrained workflow scheduling, energy efficient workflow scheduling, multicriteria workflow scheduling, multiobjective workflow scheduling, weighted sum for workflow scheduling INTRODUCTIONLarge-scale workflow-based applications appear in most of every scientific experiment. They are presented with many interdependent tasks, computing dependencies, and data flows. Workflow scheduling is the process of mapping workflow tasks to the available computing resources for the satisfaction of some constraints. Among distributed systems, Cloud computing has been witnessed to be the applicable environment to handle the computation requirements of large-scale workflow-based applications. Cloud computing is the lasted generation of the distributed system like grid computing, cluster computing, and parallel computing, which offers to its user a resizable computing resource, storage, and bandwidth on a pay

show abstract

Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

Cited by 88 publications

References 28 publications

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for燞omogeneous MPSoCs

Deadline‐constrained cost‐energy aware workflow scheduling in cloud

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