Energy-Efficient Resource Utilization for Heterogeneous Embedded Computing Systems

Huang, Jing; Li, Renfa; An, Jiyao; Ntalasha, Derrick; Yang, Fan; Li, Keqin

doi:10.1109/tc.2017.2693186

Cited by 37 publications

(15 citation statements)

References 22 publications

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“…Energy consumption is often reduced with DVFS (Dynamic Voltage and Frequency Scaling), which scales processors' voltage and frequency simultaneously as a middleware implemented on the operating system level [19]. Since high frequency is the main cause of high energy consumption for a processor, proper use of DVFS effectively saves energy [20].…”

Section: Related Workmentioning

confidence: 99%

Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Huang

Jiao

et al. 2020

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

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Multiprocessor systems are increasingly deployed in real-time applications, where reliability, energy consumption, and makespan are often the main scheduling objectives. In this work, we investigate dynamic scheduling of tasks modelled by directed acyclic graphs (DAGs), which is an NP-hard problem with all existing methods being heuristics. Our contributions have two steps: (i) Assuming that the allocation of DAG nodes to processors is given, we propose OEA (Optimal Energy Allocation) and SOEA (Search-based OEA)-the first optimal methods that minimise the energy consumption whilst satisfying the reliability requirement-for homogeneous and heterogeneous systems, respectively; (ii) We present a novel scheduling algorithm ODS (Out-Degree Scheduling) that allocates the DAG nodes according to their out-degrees, and considering energy consumption, reliability, as well as dynamic finish time. ODS dominates the widely applied HEFT (Heterogeneous Earliest Finish Time) in makespan. Combining SOEA with ODS makes a complete solution to the problem of dynamic DAG scheduling on multiprocessor systems, and achieves generally better results compared to the existing approaches. Specifically, in most cases, we are better on all the three objectives, i.e., reliability, energy as well as makespan, and in other cases we are better on some of the objectives.

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Section: Related Workmentioning

confidence: 99%

Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Huang

Jiao

et al. 2020

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

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“…Cao et al [8] addressed optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Huang et al [12] minimized power consumption under performance constraints through load distribution for heterogeneous embedded nodes with dedicated/general tasks and different queueing disciplines. Lefévre and Orgerie [16] explored the energy issue by analyzing how much energy virtualized environments cost, and provided an energy-efficient framework dedicated to cloud architectures.…”

Section: B Related Workmentioning

confidence: 99%

Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

Li¹

2019

IEEE Access

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We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level. INDEX TERMS Arbitrary cloud server, average task response time, data center, heterogeneous server, power consumption.

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“…Ghafari et al proposed a new power‐aware load balancing algorithm based on artificial bee colony to detect both overutilized and underutilized hosts for effective power management . Huang et al studied the problem of power consumption minimization with performance constraint in heterogeneous distributed embedded systems by optimal load distribution . Kansal and Chana discussed existing load balancing techniques in cloud computing and further compared them based on various parameters and discussed these techniques from energy consumption and carbon emission perspective .…”

Section: Related Researchmentioning

confidence: 99%

Energy-Efficient Resource Utilization for Heterogeneous Embedded Computing Systems

Cited by 37 publications

References 22 publications

Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Dynamic DAG Scheduling on Multiprocessor Systems: Reliability, Energy, and Makespan

Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

Optimal load distribution for multiple classes of applications on heterogeneous servers with variable speeds

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