Contention Aware Energy Efficient Scheduling on Heterogeneous Multiprocessors

Singh, Jagpreet; Betha, Sandeep; Mangipudi, Bhargav; Auluck, Nitin

doi:10.1109/tpds.2014.2322354

Cited by 39 publications

(16 citation statements)

References 26 publications

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“…Static scheduling of DAGs on multiprocessors with only makespan objective is an NP-hard problem [1], [2]. To deal with hardness, researchers have proposed optimal ILPs [14]- [16], meta-heuristics [17]- [20] and low-complexity heuristics [21]- [23]. The Heterogeneous Earliest Finish Time First (HEFT) [21] is one of the first very popular polynomial time list scheduling algorithm, which sorts tasks in a DAG in to a list and later greedily schedule tasks from the list one at a time to reduce the finish time.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic and Static Energy Efficient Scheduling of Task Graphs on Multiprocessors: A Heuristic

2020

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For energy efficient scheduling of task graphs on multiprocessors, dynamic voltage and frequency scaling (DVFS) and duplication are two widely used techniques. DVFS is generally used to utilize the execution slack by lowering the voltage and frequency of a task to decrease the dynamic energy consumption. Whereas duplication decreases the schedule length and communication energy consumption by replicating certain dependent tasks to avoid communication delays. However, while making decisions on DVFS and duplication for a task, the static energy consumption is mostly overlooked. With chip technologies reducing to a few nano meters, static energy consumption due to leakage current has become important. This article proposes a novel polynomial time heuristic that uses both DVFS and duplication to optimize static energy consumption along with dynamic and communication energy when scheduling task graphs on heterogeneous multiprocessors. The proposed list scheduling algorithm also balances schedule length with energy consumption using proposed normalized difference parameters while making scheduling decisions for a particular task. The results demonstrate the ability of the proposed algorithm to decrease the overall energy consumption with an improved or comparable schedule length as compared with other algorithms in various scenarios.

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Section: Related Workmentioning

confidence: 99%

“…without increasing much on the time complexity. For large task graphs, a restricted number of voltage-frequency pairs can be used [16] to reduce the time complexity.…”

Section: Algorithm:dseasmentioning

confidence: 99%

Dynamic and Static Energy Efficient Scheduling of Task Graphs on Multiprocessors: A Heuristic

2020

Self Cite

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“…In their approach task mapping and scheduling are integrated with dynamic voltage scaling to maximize energy efficiency. Singh et al [38] propose a contention-aware, energy efficient, duplication-based mixed integer programming (CEEDMIP) formulation for scheduling task graphs on NoC-based heterogeneous multiprocessors. The key idea of their approach is to duplicate some tasks to reduce the communication energy as well as traffic congestion.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Tariq¹,

Ishak²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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We investigate the problem of scheduling a set of tasks with individual deadlines and conditional precedence constraints on a heterogeneous Network on Chip (NoC)-based Multi-Processor System-on-Chip (MPSoC) such that the total expected energy consumption of all the tasks is minimized, and propose a novel approach. Our approach consists of a scheduling heuristic for constructing a single unified schedule for all the tasks and assigning a frequency to each task and each communication assuming continuous frequencies, an Integer Linear Programming (ILP)-based algorithm and a polynomial time heuristic for assigning discrete frequencies and voltages to tasks and communications. We have performed experiments on 16 synthetic and 4 real-world benchmarks. The experimental results show that compared to the state-of-theart approach, our approach using the ILP-based algorithm and our approach using the polynomial-time heuristic achieve average improvements of 31% and 20%, respectively, in terms of energy reduction. compared to v j and traverse the same links that v j traverses. 4) If v j is a task node, compute its finish time ζ j = r j + t k,j , and insert unconditional directed edges from v j to unscheduled nodes concurrent to v j and mapped on the same processor where v j is mapped. 5) Delete v j from ReadySet and insert all ready nodes in G to ReadySet. Consider the CTG in Figure 1(b) and the MPSoC in Figure 2(a) where all the processors are identical. The execution times of tasks at the maximum processor frequency are t 1,1

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“…In cloud computing applications, most essential thing is the allocation of resources efficiently to increase the performance of cloud information centers. Therefore, to enhance the performance and counter these issues in recent time different techniques are introduced by various researchers such as Hierarchical reliability-driven scheduling (HRDS) technique [7], Constrained earliest finish time (CEFT) algorithm [8], Contention-aware energy-efficient duplication (FastCEED) technique [9], Dynamic voltage and frequency scaling (DVFS) [10] and voltage and frequency island (VFI) technique [11]. In above all techniques, Dynamic voltage and frequency scaling (DVFS) is one of the most widely emerged technique for efficient scheduling of task-loads.…”

Section: Introductionmentioning

confidence: 99%

An energy optimization with improved QOS approach for adaptive cloud resources

Sudha

Chitnis²

2020

IJECE

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In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques.

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Contention Aware Energy Efficient Scheduling on Heterogeneous Multiprocessors

Cited by 39 publications

References 26 publications

Dynamic and Static Energy Efficient Scheduling of Task Graphs on Multiprocessors: A Heuristic

Dynamic and Static Energy Efficient Scheduling of Task Graphs on Multiprocessors: A Heuristic

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

An energy optimization with improved QOS approach for adaptive cloud resources

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