Modeling CPU energy consumption for energy efficient scheduling

2010 ◽  
Author(s):  
Abhishek Jaiantilal ◽  
Yifei Jiang ◽  
Shivakant Mishra
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Efficient Scheduling

scholarly journals Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7446
Author(s):  
Adrian Kampa ◽  
Iwona Paprocka
Keyword(s):  
Energy Consumption ◽  
Production System ◽  
Energy Efficient ◽  
Parallel Flow ◽  
Finite Buffer ◽  
Series Production ◽  
Delivery Cost ◽  
Multi Objective ◽  
Parallel Flows ◽  
Energy Efficient Scheduling

The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and shutdown. Literature analysis shows that there is a research gap regarding multi-objective algorithms, as minimizing energy consumption is not the only factor affecting the total manufacturing cost—there are other factors, such as late delivery cost or early delivery cost with additional storage cost, which make the optimization of the total cost of the production process more complicated. Another goal is to develop previous scheduling algorithms and research framework for energy efficient scheduling. The impact of the input data on the production system performance and energy consumption for series production is investigated in serial, parallel or serial–parallel flows. Parallel flow of upcoming tasks achieves minimum values of makespan criterion. Serial and serial–parallel flows of arriving tasks ensure minimum cost of energy consumption. Parallel flow of arriving tasks ensures minimum values of the costs of tardiness or premature execution. Parallel flow or serial–parallel flow of incoming tasks allows one to implement schedules with tasks that are not delayed.

Energy Efficient Scheduling for Multiple Workflows in Cloud Environment

2018 ◽  
Vol 13 (3) ◽  
pp. 14-34
Author(s):  
Ritu Garg ◽  
Neha Shukla
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Cloud Environment ◽  
Cloud Data ◽  
Available Energy ◽  
Cloud Data Centers ◽  
Reduce Energy Consumption ◽  
Scheduling Heuristic ◽  
Communication Stage ◽  
Energy Efficient Scheduling

Cloud computing makes utility computing possible with pay as you go model. It virtualizes the systems by polling and sharing the resources, thus we need to handle more than one workflow at the same time. Workflow is the standard to represent compute intensive applications in scientific and engineering domain. Hence, in this article, the authors presented the scheduling heuristic for multiple workflows running parallel in the cloud environment with the aim to reduce the energy consumption as it is one of the major concerns of cloud data centers along with the execution performance. In the proposed approach, first clustering is performed to minimize the energy consumption and execution time during communication corresponding to precedence constraint tasks. Then cluster are scheduled is on the best available energy efficient resources. Finally, DVFS is applied in order to reduce energy consumption further when the nodes are in the idle and communication stage. The simulation has been performed on CloudSim and the results show the reduction in energy consumption by up to 42%.

scholarly journals An evolutionary scheduling approach for trading-off accuracy vs. verifiable energy in multicore processors

2017 ◽  
Vol 25 (6) ◽  
pp. 1006-1019
Author(s):  
U Liqat ◽  
Z Banković ◽  
P Lopez-Garcia ◽  
M V Hermenegildo
Keyword(s):  
Energy Consumption ◽  
Energy Budget ◽  
Energy Efficient ◽  
Energy Savings ◽  
Multicore Processors ◽  
Work Load ◽  
Control Flow ◽  
Upper And Lower Bounds ◽  
Energy Bound ◽  
Energy Efficient Scheduling

Abstract This work addresses the problem of energy-efficient scheduling and allocation of tasks in multicore environments, where the tasks can allow a certain loss in accuracy in the output, while still providing proper functionality and meeting an energy budget. This margin for accuracy loss is exploited by using computing techniques that reduce the work load, and thus can also result in significant energy savings. To this end, we use the technique of loop perforation, that transforms loops to execute only a subset of their original iterations, and integrate this technique into our existing optimization tool for energy-efficient scheduling. To verify that a schedule meets an energy budget, both safe upper and lower bounds on the energy consumption of the tasks involved are needed. For this reason, we use a parametric approach to estimate safe (and tight) energy bounds that are practical for energy verification (and optimization applications). This approach consists in dividing a program into basic (‘branchless’) blocks, establishing the maximal (resp. minimal) energy consumption for each block using an evolutionary algorithm, and combining the obtained values according to the program control flow, by using static analysis to produce energy bound functions on input data sizes. The scheduling tool uses evolutionary algorithms coupled with the energy bound functions for estimating the energy consumption of different schedules. The experiments with our prototype implementation were performed on multicore XMOS chips, but our approach can be adapted to any multicore environment with minor changes. The experimental results show that our new scheduler enhanced with loop perforation improves on the previous one, achieving significant energy savings (31% on average for the test programs) for acceptable levels of accuracy loss.

scholarly journals THE ADVANTAGE OF GENETIC ALGORITHM IN ENERGY-EFFICIENT SCHEDULING FOR HETEROGENEOUS CLOUD COMPUTING

2016 ◽  
Vol 4 (7) ◽  
pp. 1-9
Author(s):  
Hang Zhou ◽  
Samina Kausar ◽  
Ningning Dong
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Scheduling Problem ◽  
New Energy ◽  
Heavy Burden ◽  
Heterogeneous Cloud ◽  
Energy Efficient Scheduling ◽  
Computing Infrastructure

Nowadays Energy Consumption has been a heavy burden on the enterprise cloud computing infrastructure. This paper focuses on the hardware factors in energy consumption. Inspired by DVFS, it proposes a new energy-efficient (EE) model. This paper formulates the scheduling problem and genetic algorithm is applied to obtain higher efficiency value. Simulations are implemented to verify the advantage of genetic algorithm. In addition, the robustness of our strategy is validated by modifying the relevant parameters of the experiment.

scholarly journals Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 220 ◽  
Author(s):  
Tianhua Jiang ◽  
Chao Zhang ◽  
Huiqi Zhu ◽  
Jiuchun Gu ◽  
Guanlong Deng
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Whale Optimization ◽  
Energy Efficient Scheduling

Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

scholarly journals Wireless-Uplinks-Based Energy-Efficient Scheduling in Mobile Cloud Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Xing Liu ◽  
Chaowei Yuan ◽  
Zhen Yang ◽  
Enda Peng
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Mobile Cloud Computing ◽  
Scheduling Algorithm ◽  
Average Energy ◽  
Mobile Internet ◽  
Mobile Cloud ◽  
Average Energy Consumption ◽  
Energy Efficient Scheduling

Mobile cloud computing (MCC) combines cloud computing and mobile internet to improve the computational capabilities of resource-constrained mobile devices (MDs). In MCC, mobile users could not only improve the computational capability of MDs but also save operation consumption by offloading the mobile applications to the cloud. However, MCC faces the problem of energy efficiency because of time-varying channels when the offloading is being executed. In this paper, we address the issue of energy-efficient scheduling for wireless uplink in MCC. By introducing Lyapunov optimization, we first propose a scheduling algorithm that can dynamically choose channel to transmit data based on queue backlog and channel statistics. Then, we show that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in a channel-aware MCC system. Simulation results show that the proposed scheduling algorithm can reduce the time average energy consumption for offloading compared to the existing algorithm.

scholarly journals An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment

2018 ◽  
Vol 11 (1) ◽  
pp. 179 ◽  
Author(s):  
Zhongwei Zhang ◽  
Lihui Wu ◽  
Tao Peng ◽  
Shun Jia
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Efficient ◽  
Job Shop ◽  
System Level ◽  
Flexible Job Shop ◽  
Total Production ◽  
Efficient Mechanism ◽  
Energy Efficient Scheduling

Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.

Energy-efficient scheduling algorithm for real-time job set

2016 ◽  
Vol 33 (6) ◽  
pp. 1753-1766 ◽  
Author(s):  
Chin-Fu Kuo ◽  
Yung-Feng Lu ◽  
Bao-Rong Chang
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Efficient ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Total Density ◽  
Scheduling Problem ◽  
Two Phase ◽  
Content Type ◽  
Energy Efficient Scheduling

Purpose – The purpose of this paper is to investigate the scheduling problem of real-time jobs executing on a DVS processor. The jobs must complete their executions by their deadlines and the energy consumption also must be minimized. Design/methodology/approach – The two-phase energy-efficient scheduling algorithm is proposed to solve the scheduling problem for real-time jobs. In the off-line phase, the maximum instantaneous total density and instantaneous total density (ITD) are proposed to derive the speed of the processor for each time instance. The derived speeds are saved for run time. In the on-line phase, the authors set the processor speed according to the derived speeds and set a timer to expire at the corresponding end time instance of the used speed. Findings – When the DVS processor executes a job at a proper speed, the energy consumption of the system can be minimized. Research limitations/implications – This paper does not consider jobs with precedence constraints. It can be explored in the further work. Practical implications – The experimental results of the proposed schemes are presented to show the effectiveness. Originality/value – The experimental results show that the proposed scheduling algorithm, ITD, can achieve energy saving and make the processor fully utilized.

Energy-Efficient Scheduling Optimization for Parallel Applications on Heterogeneous Distributed Systems

2020 ◽  
Vol 29 (13) ◽  
pp. 2050203
Author(s):  
Nan Gao ◽  
Cheng Xu ◽  
Xin Peng ◽  
Haibo Luo ◽  
Wufei Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Energy Efficient ◽  
Gaussian Elimination ◽  
Task Assignment ◽  
Computation Time ◽  
Parallel Applications ◽  
Heterogeneous Distributed Systems ◽  
Static Energy ◽  
Energy Efficient Scheduling

Designing energy-efficient scheduling algorithms on heterogeneous distributed systems is increasingly becoming the focus of research. State-of-the-art works have studied scheduling by combining dynamic voltage and frequency scaling (DVFS) technology and turning off the appropriate processors to reduce dynamic and static energy consumptions. However, the methods for turning off processors are ineffective. In this study, we propose a novel method to assign priorities to processors for facilitating effective selection of turned-on processors to decrease static energy consumption. An energy-efficient scheduling algorithm based on bisection (ESAB) is proposed on this basis, and this algorithm directly turns on the most energy-efficient processors depending on the idea of bisection to reduce static energy consumption while dynamic energy consumption is decreased by using DVFS technology. Experiments are performed on fast Fourier transform, Gaussian elimination, and randomly generated parallel applications. Results show that our ESAB algorithm makes a better trade-off between reducing energy consumption and low computation time of task assignment (CTTA) than existing algorithms under different scale conditions, deadline constraints, and degrees of parallelism and heterogeneity.

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