scholarly journals Energy-Efficient Reliability-Aware Scheduling Algorithm on Heterogeneous Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Tang ◽  
Weizhen Tan
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Heterogeneous Systems ◽  
High Energy ◽  
Parallel Applications ◽  
Energy Aware ◽  
Simulation Performance ◽  
Energy Aware Scheduling

The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.

E-BaTS: Energy-Aware Scheduling for Bag-of-Task Applications in HPC Clusters

2015 ◽  
Vol 25 (03) ◽  
pp. 1541005
Author(s):  
Alexandra Vintila Filip ◽  
Ana-Maria Oprescu ◽  
Stefania Costache ◽  
Thilo Kielmann
Keyword(s):  
Energy Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Terms Of Trade ◽  
Exhaustive Search ◽  
Energy Aware ◽  
Trade Offs ◽  
Energy Aware Scheduling ◽  
And Performance ◽  
Performance Computing

High-Performance Computing (HPC) systems consume large amounts of energy. As the energy consumption predictions for HPC show increasing numbers, it is important to make users aware of the energy spent for the execution of their applications. Drawing from our experience with exposing cost and performance in public clouds, in this paper we present a generic mechanism to compute fast and accurate estimates for the tradeoffs between the performance (expressed as makespan) and the energy consumption of applications running on HPC clusters. We validate our approach by implementing it in a prototype, called E-BaTS and validating it with a wide variety of HPC bags-of-tasks. Our experiments show that E-BaTS produces conservative estimates with errors below 5%, while requiring at most 12% of the energy and time of an exhaustive search for providing configurations close to the optimal ones in terms of trade-offs between energy consumption and makespan.

scholarly journals A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Lin ◽  
Wentai Wu ◽  
James Z. Wang
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Power Efficiency ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Cloud Data ◽  
Virtual Clusters ◽  
Resource Requirements

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

A new high-performance open CNC system and its energy-aware scheduling algorithm

2017 ◽  
Vol 93 (5-8) ◽  
pp. 1513-1525 ◽  
Author(s):  
Chang-yi Deng ◽  
Rui-feng Guo ◽  
Xun Xu ◽  
Ray Y Zhong ◽  
Zhenyu Yin
Keyword(s):  
High Performance ◽  
Scheduling Algorithm ◽  
Cnc System ◽  
Energy Aware ◽  
Open Cnc ◽  
Energy Aware Scheduling ◽  
Open Cnc System

scholarly journals Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3473
Author(s):  
Alejandro Santiago ◽  
Mirna Ponce-Flores ◽  
J. David Terán-Villanueva ◽  
Fausto Balderas ◽  
Salvador Ibarra Martínez ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Heterogeneous Systems ◽  
Parallel Applications ◽  
Literature Study ◽  
Task List ◽  
Local Searches ◽  
Np Hard Problem ◽  
Comparative Literature Study ◽  
Performance Computing

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption.

scholarly journals Energy-efficient scheduling of streaming applications in VFI-NoC-HMPSoC based edge devices

2020 ◽  
Author(s):  
Umair Ullah Tariq ◽  
Haider Ali ◽  
Lu Liu ◽  
John Panneerselvam ◽  
James Hardy
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Population Based ◽  
Multiprocessor System ◽  
Task Mapping ◽  
Energy Aware ◽  
Streaming Applications ◽  
Energy Aware Scheduling ◽  
Population Based Algorithm

AbstractEnergy-aware high-performance computing is becoming a challenging facet for streaming applications at edge devices in Internet-of-Things (IoT) due to the high computational complexity involved. Therefore, the number of processors has increased significantly on the multiprocessor system subsequently, Voltage Frequency Island (VFI) recently adopted for an effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, energy-aware scheduling of real-time streaming applications on edge-devices is investigated. First, an innovative re-timing based technique is developed to transform the dependent workload into an independent task model to avail resources and the wasted slack in the processors with a possible minimal prologue. Moreover, unlike the existing population-based optimization algorithms, a novel population-based algorithm, ARSH-FATI is proposed that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. Finally, a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm is presented. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based MPSoCs.

Reliable Energy-Aware Scheduling Algorithm With Multi-Level Budget for Real-Time Embedded System

2021 ◽  
Vol 12 (4) ◽  
pp. 55-76
Author(s):  
Ajitesh Kumar ◽  
Sanjai Kumar Gupta
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
Real Time ◽  
Scheduling Algorithm ◽  
Management Technique ◽  
Energy Aware ◽  
Wide Range ◽  
Energy Aware Scheduling ◽  
Computing Performance ◽  
Embedded Applications

Energy consumption of embedded applications has rapidly increased with the advancement of technology and computing. There is a little improvement in energy consumption as compared to computing and storage capacity. Although computing performance has been continuously increasing, power/energy consumption is more critical in the design of real-time embedded systems. Real-time embedded applications need a power management technique to judicially balance the energy consumption and computing performance. It should be done in such a way that the system performance improves along with an increase in the lifespan of the system. The proposed methodology presented in this paper deals with the minimization of energy for time-critical embedded applications. Simulation studies, along with theoretical analysis, have been carried out to show the effectiveness of the proposed three-phase reliable energy-aware scheduling method. It is observed that the proposed approach provides better tolerance (approximately four times) and consumes less energy (35% to 45%) for a wide range of applications.

scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

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