Multiprocessor real-time scheduling with arbitrary processor affinities: from practice to theory

被引:11
|
作者
Gujarati, Arpan [1 ]
Cerqueira, Felipe [1 ]
Brandenburg, Bjoern B. [1 ]
机构
[1] Max Planck Inst Software Syst MPI SWS, D-67663 Kaiserslautern, Germany
关键词
Multiprocessors; Real-time systems; Processor affinities; Schedulability analysis; TASK SYSTEMS;
D O I
10.1007/s11241-014-9205-9
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Contemporary multiprocessor real-time operating systems, such as VxWorks, LynxOS, QNX, and real-time variants of Linux, allow a process to have an arbitrary processor affinity, that is, a process may be pinned to an arbitrary subset of the processors in the system. Placing such a hard constraint on process migrations can help to improve cache performance of specific multi-threaded applications, achieve isolation among applications, and aid in load-balancing. However, to date, the lack of schedulability analysis for such systems prevents the use of arbitrary processor affinities in predictable hard real-time systems. This paper presents the first analysis of multiprocessor scheduling with arbitrary processor affinities from a real-time systems perspective. It is shown that job-level fixed-priority scheduling with arbitrary processor affinities is strictly more general than global, clustered, and partitioned job-level fixed-priority scheduling combined. Concerning the more general case of job-level dynamic priorities, it is shown that global and clustered scheduling are equivalent to multiprocessor real-time scheduling with arbitrary processor affinities. The Linux push and pull scheduler is studied as a reference implementation and two approaches for the schedulability analysis of hard real-time tasks with arbitrary processor affinities are presented. In the first approach, the scheduling problem is reduced to "global-like" sub-problems to which existing global schedulability tests can be applied. The second approach is specifically based on response-time analysis and models the response-time computation as a linear optimization problem. The latter linear-programming-based approach has better runtime complexity than the former reduction-based approach. Schedulability experiments show the proposed techniques to be effective.
引用
收藏
页码:440 / 483
页数:44
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