An EDF schedulability test for periodic tasks on reconfigurable hardware devices

2006 ◽  
Author(s):  
Klaus Danne ◽  
Marco Platzner
Keyword(s):  
Reconfigurable Hardware ◽  
Periodic Tasks ◽  
Schedulability Test

scholarly journals Utilization Bound Scheduling Analysis for Nonpreemptive Uniprocessor Architecture Using UML-RT

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
S. Ewins Pon Pushpa ◽  
Manamalli Devasigamani
Keyword(s):  
Release Time ◽  
Periodic Tasks ◽  
Task Set ◽  
Processor Utilization ◽  
Window Analysis ◽  
Rate Monotonic ◽  
Utilization Bound ◽  
Priority Scheme ◽  
Schedulability Test ◽  
Necessary And Sufficient

The key for adopting the utilization-based schedulability test is to derive the utilization bound. Given the computation times, this paper proposes two utilization bound algorithms to derive interrelease times for nonpreemptive periodic tasks, using a new priority scheme, “Rate Monotonic Algorithm-Shortest Job First.” The obtained task set possesses the advantage of Rate Monotonic Algorithm and Shortest Job First priority scheme. Further, the task set is tested for schedulability, by first deriving a general schedulability condition from “problem window” analysis and, a necessary and sufficient schedulability condition for a task to be scheduled, at any release time are also derived. As a technical contribution, success ratio and effective processor utilization are analyzed for our proposed utilization bound algorithms on a uniprocessor architecture modeled using UML-RT.

scholarly journals Static Scheduling of Periodic Hardware Tasks with Precedence and Deadline Constraints on Reconfigurable Hardware Devices

2011 ◽  
Vol 2011 ◽  
pp. 1-28 ◽  
Author(s):  
Ikbel Belaid ◽  
Fabrice Muller ◽  
Maher Benjemaa
Keyword(s):  
Reconfigurable Hardware ◽  
Mixed Integer ◽  
Task Graph ◽  
Periodic Tasks ◽  
Dynamic Partial Reconfiguration ◽  
Static Scheduling ◽  
Optimal Allocations ◽  
Heterogeneous Hardware ◽  
Deadline Constraints ◽  
Task Graph Scheduling

Task graph scheduling for reconfigurable hardware devices can be defined as finding a schedule for a set of periodic tasks with precedence, dependence, and deadline constraints as well as their optimal allocations on the available heterogeneous hardware resources. This paper proposes a new methodology comprising three main stages. Using these three main stages, dynamic partial reconfiguration and mixed integer programming, pipelined scheduling and efficient placement are achieved and enable parallel computing of the task graph on the reconfigurable devices by optimizing placement/scheduling quality. Experiments on an application of heterogeneous hardware tasks demonstrate an improvement of resource utilization of 12.45% of the available reconfigurable resources corresponding to a resource gain of 17.3% compared to a static design. The configuration overhead is reduced to 2% of the total running time. Due to pipelined scheduling, the task graph spanning is minimized by 4% compared to sequential execution of the graph.

Schedulability Test for Multiprocessor Scheduling Based on Hyperbolic Bound

2012 ◽  
Vol 23 (8) ◽  
pp. 2223-2234
Author(s):  
Hong-Ya WANG ◽  
Wei YIN ◽  
Hui SONG ◽  
Lih-Chyun SHU ◽  
Mei WANG
Keyword(s):  
Multiprocessor Scheduling ◽  
Schedulability Test

The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

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