Computing First-to-First Propagation Delays through Sequences of Fixed-Priority Periodic Tasks

2012 ◽  
Author(s):  
Rodney R. Howell
Keyword(s):  
Periodic Tasks ◽  
Fixed Priority ◽  
Propagation Delays

Allocating fixed-priority periodic tasks on multiprocessor systems

1995 ◽  
Vol 9 (3) ◽  
pp. 207-239 ◽  
Author(s):  
Yingfeng Oh ◽  
Sang H. Son
Keyword(s):  
Multiprocessor Systems ◽  
Periodic Tasks ◽  
Fixed Priority

The Algorithms of Dynamic Servers for Scheduling Aperiodic Real-Time Tasks – A Review

2014 ◽  
Vol 945-949 ◽  
pp. 3380-3383
Author(s):  
Feng Xiang Zhang
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Schedulability Analysis ◽  
Periodic Tasks ◽  
Dynamic Algorithms ◽  
Fixed Priority ◽  
Aperiodic Tasks ◽  
Hard Real Time

This paper focus on the dynamic server algorithms, and the servers are used for scheduling soft aperiodic tasks. Many types of servers and their schedulability analysis are reviewed, these properties can be used for constructing hierarchical embedded systems, where the soft aperiodic tasks and the hard real-time tasks can be scheduled in the same system. The aperiodic tasks in the server are not preemptable, and they are executed in a first-come first-served (FCFS) manner. If it is not specified, there is only one server in the system, and rest of the processes in the system are ordinary periodic tasks. The servers could be scheduled by fixed priority or dynamic algorithms.

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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