scholarly journals Flow Network-Based Real-Time Scheduling for Reducing Static Energy Consumption on Multiprocessors

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1330-1344
Author(s):  
Joohyung Sun ◽  
Hyeonjoong Cho ◽  
Arvind Easwaran ◽  
Ju-Derk Park ◽  
Byeong-Cheol Choi
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Flow Network ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Static Energy

scholarly journals Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1833 ◽  
Author(s):  
Tamás Bányai
Keyword(s):  
Energy Efficiency ◽  
Supply Chain ◽  
Energy Consumption ◽  
Real Time ◽  
Real Time Scheduling ◽  
Improve Energy Efficiency ◽  
Increase Energy Efficiency ◽  
Last Mile ◽  
Time Scheduling ◽  
Last Mile Delivery

Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

scholarly journals Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Manal A. El Sayed ◽  
El Sayed M. Saad ◽  
Rasha F. Aly ◽  
Shahira M. Habashy
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Bin Packing ◽  
Shared Resources ◽  
Energy Aware ◽  
Task Partitioning ◽  
Worst Case ◽  
Real Time Scheduling ◽  
Static Partitioning ◽  
Time Scheduling

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

scholarly journals Energy-Efficient Real-Time Scheduling Algorithm for Fault-Tolerant Autonomous Systems

2018 ◽  
Vol 19 (4) ◽  
pp. 387-400
Author(s):  
Hussein El Ghor ◽  
Julia Hage ◽  
Nizar Hamadeh ◽  
Rafic Hage Chehade
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Integrated Circuit ◽  
Energy Efficient ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Scheduling Algorithms ◽  
Real Time Scheduling ◽  
Time Scheduling

For the past decades, we have experienced an aggressive technology scaling due to the tremendous advancements of Integrated Circuit technology. As massive integration continues, the power consumption of the IC chips exponentially increases which further degraded the system reliability. This in turn poses significant challenges to the design of real-time autonomous systems. In this paper, we target the problem of designing advanced real-time scheduling algorithms that are subject to timing, energy consumption and fault-tolerant design constraints. To this end, we first investigated the problem of developing scheduling techniques for uniprocessor real-time systems that minimizes energy consumption while still tolerating up to k transient faults to preserve the system's reliability. Two scheduling algorithms are proposed: the first scheduler is an extension of an optimal fault-free energy-efficient scheduling algorithm, named ES-DVFS. The second algorithm aims to enhance the energy saving by reserving adequate slack time for recovery when faults strike. We derive a necessary and sufficient condition that must be efficiently checked for the time and energy feasibility of aperiodic jobs in the presence of failures. Later, we formally prove that the proposed algorithm is optimal for a k-fault-tolerant model. Our simulation results demonstrate that the proposed schedulers can efficiently improve energy savings when compared with previous works.

Workload assignment for global real-time scheduling on unrelated clustered platforms

2021 ◽  
Author(s):  
Antoine Bertout ◽  
Joël Goossens ◽  
Emmanuel Grolleau ◽  
Roy Jamil ◽  
Xavier Poczekajlo
Keyword(s):  
Real Time ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

2021 ◽  
Vol 13 (6) ◽  
pp. 3400
Author(s):  
Jia Ning ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Bin Chen ◽  
Yi Tang
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Demand Response ◽  
System Operation ◽  
Real Time Scheduling ◽  
High Penetration ◽  
Time Scheduling ◽  
Grid Load ◽  
Grid Side ◽  
Scheduling Models

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

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