Accelerated Simply Periodic Task Sets for RM Scheduling

International audienceThe article examines rate-monotonic scheduling (RMS). The focus is on efficient schedulability tests of high sensitivity.Accelerated simply periodic task sets (ASPTSs) are constructed by shortening task periods in order to obtain a transformed – simply periodic – task set where each period is an integer divisor of all longer periods. The article presents a new heuristic for partitioned multiprocessor (MP) scheduling based on Specialization with respect to r (Sr) and Distance-Constrained Tasks (DCT) which use ASPTSs first described by Han and Tyan [9, 10]. They have al- ready shown the advantage of Sr and DCT over the Liu/Layland (LL) and the Burchard (Bu) bound in terms of sensitivity. First, the article compares Sr and DCT as well with other uniprocessor scheduling criteria, both theoretically and empirically. Next, these tests are applied to MP scheduling. Theory is followed by a case study and an empirical investigation with randomised task sets. Related approaches are thoroughly examined and summarised in a scheme where the central role of ASPTSs becomes obvious.The article shows that Sr and DCT provide a very good trade-off between maximizing the scheduling test sensitivity (no unnecessary hardware) and minimizing the test’s computational complexity (towards real-time decisions on schedulability)

Schedulability analysis of timed CSP models using the PAT model checker

Timed CSP can be used to model and analyse real-time and concurrent behaviour of embedded control systems. Practical CSP implementations combine the CSP model of a real-time control system with prioritized scheduling to achieve efficient and orderly use of limited resources. Schedulability analysis of a timed CSP model of a system with respect to a scheduling scheme and a particular execution platform is important to ensure that the system design satisfies its timing requirements. In this paper, we propose a framework to analyse schedulability of CSP-based designs for non-preemptive fixed-priority multiprocessor scheduling. The framework is based on the PAT model checker and the analysis is done with dense-time model checking on timed CSP models. We also provide a schedulability analysis workflow to construct and analyse, using the proposed framework, a timed CSP model with scheduling from an initial untimed CSP model without scheduling. We demonstrate our schedulability analysis workflow on a case study of control software design for a mobile robot. The proposed approach provides non-pessimistic schedulability results

Hyperprofile-based Computation Offloading for Mobile Edge Networks

In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.Comment: 5 pages, NSF REU Site publicatio