Fixed-Priority Schedulability of Sporadic Tasks on Uniprocessors is NP-Hard

2017 ◽  
Author(s):  
Pontus Ekberg ◽  
Wang Yi
Keyword(s):  
Np Hard ◽  
Sporadic Tasks ◽  
Fixed Priority

Schedulability Analysis of Fixed Priority Servers for Scheduling Aperiodic Real-Time Tasks – A Review

2014 ◽  
Vol 651-653 ◽  
pp. 1933-1936
Author(s):  
Feng Xiang Zhang
Keyword(s):  
Real Time ◽  
Schedulability Analysis ◽  
Timing Constraints ◽  
Sporadic Tasks ◽  
Fixed Priority ◽  
Rate Monotonic ◽  
Aperiodic Tasks ◽  
Deadline Monotonic ◽  
Priority Algorithm

This paper focus on the schedulability analysis of fixed priority servers. A number of fixed priority servers and their schedulability analysis are reviewed, these results and properties can be used for constructing systems with different timing constraints, where real-time tasks with hard deadlines and the soft aperiodic tasks can be scheduled in the same system. The aperiodic tasks in the fixed priority servers are not preemptable, and they are scheduled in a first-come first-served manner. There is only one server with many periodic or sporadic tasks in the system. The tasks and the server are scheduled by a fixed priority algorithm such as rate monotonic or deadline monotonic.

scholarly journals Timing Analysis of Fixed Priority Self-Suspending Sporadic Tasks

2015 ◽  
Author(s):  
Geoffrey Nelissen ◽  
Jose Fonseca ◽  
Gurulingesh Raravi ◽  
Vincent Nelis
Keyword(s):  
Timing Analysis ◽  
Sporadic Tasks ◽  
Fixed Priority

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

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