Partitioning Real-Time Systems on Multiprocessors with Shared Resources

• There are two main approaches to task scheduling on multiprocessor/multi-core platforms; 1) global scheduling, under which migration of tasks among processors is allowed, and 2) partitioned scheduling under which tasks are allocated onto processors and task migration is not allowed. Under global scheduling a higher utilization bound can be achieved, but in practice the overheads of migrating tasks is high. On the other hand under partitioned scheduling, besides simplicity and efficiency, existing scheduling and synchronization methods developed for uniprocessor platforms can more easily be extended to partitioned scheduling. However the partitioned scheduling protocols suffer from the problem of partitioning tasks among processors/cores which is a bin-packing problem. Therefore, several heuristic algorithms have been developed for partitioning a task set on multiprocessor platforms. However, these algorithms typically assume independent tasks while in practice real-time systems often contain tasks that share resources and hence may block each other.In this paper we propose a blocking-aware partitioning algorithm which allocates a task~set onto processors in a way that the overall amount of blocking times of tasks are decreased. The algorithm reduces the total utilization which, in turn, has the potential to decrease the total number of required processors (cores). In this paper we evaluate our algorithm and compare it with an existing similar algorithm. The comparison criteria includes both number of schedulable systems as well as processor reduction performance.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

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