scholarly journals Imaging Tasks Scheduling for High-Altitude Airship in Emergency Condition Based on Energy-Aware Strategy

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Li Zhimeng ◽  
He Chuan ◽  
Qiu Dishan ◽  
Liu Jin ◽  
Ma Manhao
Keyword(s):  
Energy Consumption ◽  
High Altitude ◽  
Minimum Energy ◽  
Scheduling Problem ◽  
Optimization Strategy ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Emergency Condition ◽  
Minimum Energy Consumption ◽  
Tasks Scheduling

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airship’s cruising speed based on the distribution of task’s deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible.

OPTIMIZING THE PIPELINE DIAMETER OF A CIRCULAR SPRINKLER ACCORDING TO THE MINIMUM ENERGY CONSUMPTION CRITERION

2021 ◽  
pp. 66-71
Author(s):  
NIKOLAY V. TSUGLENOK ◽  
Keyword(s):  
Energy Consumption ◽  
Water Supply ◽  
Electric Drive ◽  
Electric Motor ◽  
Water Distribution ◽  
Minimum Energy ◽  
Operating Conditions ◽  
Total Energy Consumption ◽  
Minimum Energy Consumption ◽  
The Relationship

The authors have determined the conditions for the eff ective use of modern electrifi ed circular sprinklers in the central part of Russia. Their designs are chosen depending on the agrotechnical requirements for irrigation, including the change in the diameter of the water distribution pipeline. However, when the diameter of the pipeline changes, the load on the electric drive of the support trolleys of the sprinkler changes too, which leads to a corresponding change in energy consumption. In turn, this also changes the load of the water supply pump. The paper sets the task of determining the optimal change in the diameter of pipelines according to the criterion of minimum energy consumption, taking into account a number of assumptions. The authors have analyzed the relationship between the change in the load on the electric drive of the sprinkler support trolley and the change in the diameter of one sprinkler section pipeline. It has been found that a decrease in the diameter by 27% (for example, the transition of the diameter of 219 mm to the diameter of 159 mm) leads to a decrease in the load on the electric drive by 38%. However, this also leads to an increase in the head loss in the water supply pump motor and, respectively, to an increase in the load and energy consumption by 0.8…3.8%. The eff ect is initially obvious, but the power of the electric motor of the water supply pump is 10…25 times higher than that of the electric motor of the sprinkler support trolley. Based on the similarity coeffi cients of the irrigation components (water supply and water distribution), the relationship beteween the total energy consumption and the change in the diameter of the water distribution pipeline has been obtained. By diff erentiating the obtained function, the dependence of the value of the optimal diameter for specifi c operating conditions is also obtained. Graphs of the relationship between energy consumption and the change in diameter have been determined, taking into account some restrictions: pump supply, static pressure, and the number of the sprinkler sections.

scholarly journals Workload-Aware and CPU Frequency Scaling for Optimal Energy Consumption in VM Allocation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhen Liu ◽  
Yongchao Xiang ◽  
Xiaoya Qu
Keyword(s):  
Energy Consumption ◽  
Greedy Algorithms ◽  
Minimum Energy ◽  
Task Characteristics ◽  
Frequency Scaling ◽  
Vm Placement ◽  
Total Energy Consumption ◽  
Work Time ◽  
Minimum Energy Consumption ◽  
Cpu Utilization

In the problem of VMs consolidation for cloud energy saving, different workloads will ask for different resources. Thus, considering workload characteristic, the VM placement solution will be more reasonable. In the real world, different workload works in a varied CPU utilization during its work time according to its task characteristics. That means energy consumption related to both the CPU utilization and CPU frequency. Therefore, only using the model of CPU frequency to evaluate energy consumption is insufficient. This paper theoretically verified that there will be a CPU frequency best suit for a certain CPU utilization in order to obtain the minimum energy consumption. According to this deduction, we put forward a heuristic CPU frequency scaling algorithm VP-FS (virtual machine placement with frequency scaling). In order to carry the experiments, we realized three typical greedy algorithms for VMs placement and simulate three groups of VM tasks. Our efforts show that different workloads will affect VMs allocation results. Each group of workload has its most suitable algorithm when considering the minimum used physical machines. And because of the CPU frequency scaling, VP-FS has the best results on the total energy consumption compared with the other three algorithms under any of the three groups of workloads.

OPTIMAL DESIGN OF HEXAPOD WALKING ROBOT LEG STRUCTURE BASED ON ENERGY CONSUMPTION AND WORKSPACE

2014 ◽  
Vol 38 (3) ◽  
pp. 305-317 ◽  
Author(s):  
Ya-guang Zhu ◽  
Bo Jin ◽  
Wei Li ◽  
Shi-tong Li
Keyword(s):  
Energy Consumption ◽  
Optimal Design ◽  
Minimum Energy ◽  
Inequality Constraints ◽  
Optimal Parameters ◽  
Walking Robot ◽  
Total Energy Consumption ◽  
Joint Torques ◽  
Minimum Energy Consumption ◽  
Robot Leg

In order to achieve the optimal design of the hexapod walking robot leg structure, a combined index of energy consumption and workspace is raised. By deriving the energy consumption functions and analyzing the target workspace, a mathematical model of nonlinear programming with inequality constraints is established. The genetic algorithm coupled with inverse kinematics and trajectory planning in a gait period is utilized to solve the optimization problem. The analysis verifies that the requirements of turning and obstacle overcoming can be satisfied, and the total energy consumption can be reduced. The results show that the optimal parameters not only satisfy the requirement of the target workspace, but also achieve the minimum energy consumption and lower joint torques.

scholarly journals Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 44 ◽  
Author(s):  
Hongchan Li ◽  
Haodong Zhu ◽  
Tianhua Jiang
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Migrating Birds Optimization ◽  
Migrating Birds

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

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