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Many suspensions, many problems: a review of self-suspending tasks in real-time systems
In general computing systems, a job (process/task) may suspend itself whilst it is waiting for some activity to complete, e.g., an accelerator to return data. In real-time systems, such self-suspension can cause substantial performance/schedulability degradation. This observation, first made in 1988, has led to the investigation of the impact of self-suspension on timing predictability, and many relevant results have been published since. Unfortunately, as it has recently come to light, a number of the existing results are flawed. To provide a correct platform on which future research can be built, this paper reviews the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems. We provide (1) a systematic description of how self-suspending tasks can be handled in both soft and hard real-time systems; (2) an explanation of the existing misconceptions and their potential remedies; (3) an assessment of the influence of such flawed analyses on partitioned multiprocessor fixed-priority scheduling when tasks synchronize access to shared resources; and (4) a discussion of the computational complexity of analyses for different self-suspension task models
Scheduling algorithms and timing analysis for hard real-time systems
Real-time systems are designed for applications in which response time is critical. As timing is a major property of such systems, proving timing correctness is of utter importance. To achieve this, a two-fold approach of timing analysis is traditionally involved: (i) worst-case execution time (WCET) analysis, which computes an upper bound on the execution time of a single job of a task running in isolation; and (ii) schedulability analysis using the WCET as the input, which determines whether multiple tasks are guaranteed to meet their deadlines. Formal models used for representing recurrent real-time tasks have traditionally been characterized by a collection of independent jobs that are released periodically. However, such a modeling may result in resource under-utilization in systems whose behaviors are not entirely periodic or independent. Examples are (i) multicore platforms where tasks share a communication fabric, like bus, for accesses to a shared memory beside processors; (ii) tasks with synchronization, where no two concurrent access to one shared resource are allowed to be in their critical section at the same time; and (iii) automotive systems, where tasks are linked to rotation (e.g., of the crankshaft, gears, or wheels). There, their activation rate is proportional to the angular velocity of a specific device. This dissertation presents multiple approaches towards designing scheduling algorithms and schedulability analysis for a variety of real-time systems with different characteristics. Specifically, we look at those design problems from the perspective of speedup factor — a metric that quantifies both the pessimism of the analysis and the non-optimality of the scheduling algorithm. The proposed solutions are shown promising by means of not only speedup factor but also extensive evaluations