A Parallel Programming Course Based on an Execution Time-Energy Consumption Optimization Problem

2016 ◽  
Author(s):  
Javier Cuenca ◽  
Domingo Gimenez
Keyword(s):  
Energy Consumption ◽  
Parallel Programming ◽  
Execution Time ◽  
Optimization Problem ◽  
Energy Consumption Optimization

scholarly journals A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1876 ◽  
Author(s):  
Hongjie Liu ◽  
Tao Tang ◽  
Jidong Lv ◽  
Ming Chai
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Energy Conservation ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Energy Utilization ◽  
Total Size ◽  
Energy Consumption Optimization ◽  
Regenerative Energy ◽  
Timetable Optimization

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences.

EXTENDED CONTROL FLOW GRAPH BASED PERFORMANCE AND ENERGY CONSUMPTION OPTIMIZATION USING SCRATCH-PAD MEMORY

2009 ◽  
Vol 18 (04) ◽  
pp. 697-711
Author(s):  
XUEXIANG WANG ◽  
HANLAI PU ◽  
JUN YANG ◽  
LONGXING SHI
Keyword(s):  
Energy Consumption ◽  
Greedy Algorithm ◽  
Directed Graph ◽  
Execution Time ◽  
Control Flow ◽  
Control Flow Graph ◽  
Flow Graph ◽  
Scratch Pad Memory ◽  
Energy Consumption Optimization ◽  
Time And Energy

A Scratch-Pad memory (SPM) allocation method to improve the performance of a specified application while reducing its energy consumption is presented in this paper. Integrated in the design is an extended control flow graph (ECFG) built directly from the application's instruction flow. The application of the design is transformed into a directed graph that consists of nodes and relationships. Likewise, to provide a solution in decreasing the overhead of moving nodes to SPM, the design is enhanced with a refined greedy algorithm based on ECFG. An experiment is conducted to prove the feasibility and efficiency of the method. The results indicate that the method indeed improves performance by an average of 11% and consumes lesser energy by an average of 28%. This is in comparison to previous research which based on the control flow graph (CFG) method. The latter was discovered to have disregarded the relationships of nodes. In conclusion, the application's execution time and energy consumption were reduced by an average up to 56% and 69% respectively, compared to a non-SPM environment.

scholarly journals Optimization Issues of a Hammer Mill Working Process Using Statistical Modelling

2021 ◽  
Vol 13 (2) ◽  
pp. 973
Author(s):  
Gigel Paraschiv ◽  
Georgiana Moiceanu ◽  
Gheorghe Voicu ◽  
Mihai Chitoiu ◽  
Petru Cardei ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Optimization Problem ◽  
Specific Energy Consumption ◽  
Statistical Modelling ◽  
Hammer Mill ◽  
Objective Functions ◽  
Working Process ◽  
Rotor Spinning ◽  
Milled Material ◽  
Different Types

Our paper presents the hammer mill working process optimization problem destined for milling energetic biomass (MiscanthusGiganteus and Salix Viminalis). For the study, functional and constructive parameters of the hammer mill were taken into consideration in order to reduce the specific energy consumption. The energy consumption dependency on the mill rotor spinning frequency and on the sieve orifices in use, as well as on the material feeding flow, in correlation with the vegetal biomass milling degree was the focus of the analysis. For obtaining this the hammer mill was successively equipped with 4 different types of hammers that grind the energetic biomass, which had a certain humidity content and an initial degree of reduction ratio of the material. In order to start the optimization process of hammer mill working process, 12 parameters were defined. The objective functions which minimize hammer mill energy consumption and maximize the milled material percentage with a certain specific granulation were established. The results obtained can serve as the basis for choosing the optimal working, constructive, and functional parameters of hammer mills in this field, and for a better design of future hammer mills.

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

Stability of aerobic granular sludge under low energy consumption: Optimization of granular size distribution by a novel internal component

2021 ◽  
Author(s):  
Runjuan Cao ◽  
Yatong Ji ◽  
Taixing Han ◽  
Jingsong Deng ◽  
Liang Zhu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Granular Sludge ◽  
Pollutant Removal ◽  
Low Energy ◽  
Aerobic Granular Sludge ◽  
Mesh Screen ◽  
Energy Consumption Optimization ◽  
Low Energy Consumption ◽  
Internal Component ◽  
The Stability

To enhance the stability and pollutant removal performance of an aerobic granular sludge (AGS), four groups of AGS reactors with different pore sizes of mesh screen (R1 is control reactor,...

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Sign in / Sign up

Export Citation Format

Share Document