Energy-aware fast scheduling heuristics in heterogeneous computing systems

Díaz, Carlos; Guzek, Mateusz; Pecero, Johnatan E.; Danoy, Grégoire; Bouvry, Pascal; Khan, Samee U.

doi:10.1109/hpcsim.2011.5999863

Cited by 27 publications

(22 citation statements)

References 15 publications

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“…The aforementioned can be achieved by reducing the applied voltage to the node. However, the node in the sleep mode is not completely turned off and remains in its functionality state at a minimum voltage cost [6]. Performance worsens with the application of aggressively scales the supply voltage to improve the energy efficiency [27].…”

Section: Design Objectivementioning

confidence: 99%

A comparative study of voltage/frequency scaling in NoC

Usman

Khan

2013

IEEE International Conference on Electro-Information Technology , EIT 2013

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Abstract-Voltage and frequency is dynamically scaled to produce energy efficient multi-core networkon-chip (NoC). A detailed analysis of the techniques employed for this purpose are studied and based on the optimized performance the most effective ones are reported. We also highlight the most promising high performance and energy minimizing techniques.Index Terms-Network-on-chip, dynamic voltage and frequency scaling, leakage power

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Section: Design Objectivementioning

confidence: 99%

A comparative study of voltage/frequency scaling in NoC

Usman

Khan

2013

IEEE International Conference on Electro-Information Technology , EIT 2013

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“…In this section, we describe three low computational complexity heuristics with good quality schedules proposed in [15]. These heuristics are based on the Min-Min algorithm.…”

Section: Heuristics Descriptionsmentioning

confidence: 99%

“…This paper uses the weighted function called score function SF (t i , m j ) (see Eq. 1) [15], that tries to balance both aforementioned objectives. The goal is to minimize makespan and balance the load of the system.…”

Section: Heuristics Descriptionsmentioning

confidence: 99%

“…The heuristics take advantage of the resource capabilities and task requirements to optimize their objective. Their main characteristics are low-cost, and good performance behavior [15]. We evaluate the performance of the algorithms by analyzing the results of numerous simulations that feature high heterogeneity of resources, and high heterogeneity of applications.…”

Section: Introductionmentioning

confidence: 99%

“…We evaluate scalability of the heuristics by increasing the number of tasks and machines. The six heuristics that we evaluate in this work are: the well known Min-Min [5], two Task Priority Diagram (TPD) based heuristics [6], and our three developed low-complexity heuristics [15], [2]. The low computational complexity heuristics are based on list scheduling approaches and considered as batch mode scheduling heuristics 1 .…”

Section: Introductionmentioning

confidence: 99%

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Scalable, low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems

2013

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Abstract-For heterogeneous distributed computing systems, important design issues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. The heuristics are thoroughly compared with three other heuristics from the literature. The benchmarking outlines the performance of the schedulers, representing scalability, schedule length, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as efficiently as the related approaches while featuring lower complexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail.

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Energy‐efficient scheduling algorithms for batch‐of‐tasks (BoT) applications on heterogeneous computing systems

Sajid

Raza

Shahid

2015

Concurrency and Computation

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One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy-aware scheduling algorithms G1 and G2 to schedule a batch-oftasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. the reduced reliability of the system. The increased temperature leads to system failure resulting in high monetary cost as well as environmental concerns. Therefore, reducing energy consumption of computing systems has become a must with the appeal to develop energy-efficient computing units, storage, networking, and scheduling models that consume energy and other resources as minimum as possible [4][5][6][7].Scheduling is the indispensable process of resource management in computing paradigms and one of the potential components that can be very much helpful in order to optimize the energy consumption of resources. In general, optimal scheduling has been proven to be NP-complete, and it is intractable to realize an optimal scheduling model in the presence of heterogeneity of jobs as well as resources, limited memory bandwidth, and many other constraints [8]. Job scheduling on heterogeneous parallel processors in order to minimize the makespan as well as energy consumption is one of the classical combinatorial optimization problems [9,10]. The batch-of-tasks (BoT) consists of many independent tasks belonging to different applications and can be run simultaneously to utilize the resources as much as possible. The scheduling of BoT applications on HCS is very common as it has been used by many streams, namely, optimization, cryptographic analysis, high energy physics, engineering, biochemistry, drug discovery, weather forecasting, simulation applications to name a few. The wide applicability of BoT applications on HCS makes it an important area for research, and therefore, a large number of algorithms have been developed for single as well as multi-objective problems. In order to deal with a bi-objective problem, weighted aggregation is one of the widely ac...

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Energy-aware fast scheduling heuristics in heterogeneous computing systems

Cited by 27 publications

References 15 publications

A comparative study of voltage/frequency scaling in NoC

A comparative study of voltage/frequency scaling in NoC

Scalable, low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems

Energy‐efficient scheduling algorithms for batch‐of‐tasks (BoT) applications on heterogeneous computing systems

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