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For the issues with the proposed framework, this paper proposes a new framework for the analysis of real-time job scheduling on multicore platforms with shared memory. This paper also proposes a new model for the analysis of self-suspending task systems. The contributions of this paper are listed below.
In the proposed framework, Pak and Malekian (2017) provide the lower bound of the computation workloads that can be self-suspended. However, there are several issues with the proposed framework. One is the inaccuracy of the lower bound. The lower bound is based on the evaluation of the worst-case response time of jobs, which is derived based on the assumption that jobs’ scheduling functions are monotone, and this assumption can be violated in many real-world applications (e.g., when jobs are not monotone). This assumption is reasonable for a job-scheduling algorithm, because monotone scheduling functions can provide a lower bound on the response times of jobs.
What is known about the minimum amount of computation that can be offloaded to a powerful and reliable computing server? In Carminati et al. (2014), the lower bound is calculated for the case of discrete time systems.
Previous work by Pak and Malekian (2017) proposes a framework for dynamic self-suspension behaviors. They identify the scheduling functions of jobs as the core components of a job-scheduling algorithm, and a job can be modeled as a state transition process. In this study, the state of a job is assumed to be a set of a system’s variables, and the scheduling function specifies a transition in the state. They provided a deterministic model for job scheduling, in which the scheduling function is defined by a set of scheduling constraints, and the job’s behavior is determined by the state transition process and the scheduling constraints.
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