Efficient partitioning of sporadic real-time tasks with shared resources and spin locks

Abstract

Abstract-Partitioned fixed-priority scheduling is widely used in embedded multiprocessor real-time systems due to its simplicity and low runtime overheads. However, it fundamentally requires a static mapping of tasks to processors to be determined. Optimal task set partitioning is known to be NP-hard, and the situation is further aggravated when limited resources (such as I/O ports, co-processors, buffers, etc.) must be shared among the tasks. Partitioning heuristics are much faster to compute, but may fail to find a valid mapping even if one exists. In practice, such inefficiencies can be addressed by over-provisioning processors (i.e., by using more and faster processors than strictly required), albeit at the expense of increased space, weight, and power (SWaP) requirements.This work makes two contributions towards the efficient mapping of real-time tasks that share resources protected by spin locks. First, an Integer Linear Programming (ILP) formulation of the problem is presented, which, while computationally expensive, is efficient in the sense that it will find a valid assignment if one exists, thereby minimizing processor requirements. This ILP formulation is the first optimal solution to the mapping problem in the presence of spin locks. Second, a new resource-aware partitioning heuristic is introduced, which, while not optimal, is efficient in the sense that it easily scales to large problem instances. Notably, the proposed heuristic is much simpler than prior approaches, parameter-free, and shown to perform well for a wide range of workloads.

Dependency Graph Approach for Multiprocessor Real-Time Synchronization

“…In subsequent work, Hsiu et al [23] proposed a dedicated-core framework to separate the execution of critical sections and normal sections, and employed a priority-based mechanism for resource sharing, such that each request can be blocked by at most one lower-priority request. Wieder and Brandenburg [49] proposed a greedy slacker partitioning heuristic in the presence of spin locks. The resource-oriented partitioned (ROP) scheduling was proposed by Huang et.…”

Triple Speed: Energy-Aware Real-Time Task Synchronization in Homogeneous Multi-Core Systems

“…To reduce synchronization overheads, previous studies have assigned tasks to the same core according to accessed resources [10], [28]. Wieder and Brandenburg [27] presented a greedy partition heuristic to maximize the least slack among all tasks. Although the mentioned studies adequately addressed the effect of remote blocking on schedulability, the requirement of global resource sharing cannot be avoided.…”

“…Previous studies have extended the stack resource policy (SRP) [21] and priority ceiling protocol (PCP) [22] for multiprocessor systems to reduce the remote blocking resulting from resource sharing among different cores [18], [19], namely multiprocessor SRP (MSRP) and multiprocessor PCP (MPCP), respectively. Numerous studies have been conducted for investigating synchronization-aware task allocation to enhance the schedulability of tasks with resource sharing in multicore systems [10], [23], [24], [25], [26], [27], [28]. Although the effect of remote blocking on schedulability has been adequately addressed in the mentioned studies, the requirement of global resource sharing cannot be avoided.…”

A model-based synthesis flow for automotive CPS

“…The analysis of real-time communication mechanisms based on spin locks for AUTOSAR runnables in multicores is presented in [14], and mechanisms for a flowpreserving AUTOSAR implementation of synchronous signals are presented and compared in [13]. The optimal placement of tasks with deadline constraints communicating with locking primitives in multicores is discussed in [24,5], with an MILP solution and e↵ective heuristics presented.…”

“…The first task synthesis algorithm is a search heuristic extended from the solution presented for single-core platforms in [7] and the allocation heuristics in [24], [5]. The algorithm conducts a greedy allocation with limited backtrack, and has a post-processing step that tries to further improve the solution.…”