Current hard real-time scheduling and analysis techniques are unable to efficiently utilize the computational bandwidth provided by multicore platforms. This is due to the large gap between worst-case execution time predictions used in schedulability analysis and actual execution times seen in practice. In this paper, we view this gap as "slack" that can be accounted for during schedulability analysis and reclaimed for less critical work. We use this technique to develop an architecture for scheduling mixed-criticality real-time workloads on multiprocessor platforms. Our architecture provides temporal isolation among tasks of different criticalities while allowing slack to be redistributed across criticality levels.

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Existing research in soft real-time scheduling has focused on determining tardiness bounds given a scheduling algorithm. In this paper, we study lateness bounds, which are related to tardiness bounds, and propose a scheduling algorithm to minimize lateness bounds, namely the global fair lateness

Abstract. The Earliest Deadline First (EDF) scheduling algorithm is known to be suboptimal for meeting all deadlines under global scheduling on multiprocessor platforms. However, EDF is an attractive choice for scheduling soft-real-time systems on multiprocessors. Previous work has demonstrated that the maximum tardiness is bounded, and has derived formulas for computing tardiness bounds, in EDF-scheduled real-time systems that can be modeled as collections of recurrent tasks modeled using the well-known implicit-deadline (Liu and Layland) task model. This research extends the applicability of previous techniques to systems that are modeled using the more general arbitrary sporadic task model. It also improves on prior work even for implicit-deadline systems. An algorithm is derived here that computes tardiness bounds in polynomial time. Previously, these bounds could only have been approximated in sub-exponential time.

Semi-partitioned real-time scheduling algorithms extend partitioned ones by allowing a (usually small) subset of tasks to migrate. The first such algorithm to be proposed was directed at soft real-time (SRT) sporadic task systems where bounded deadline tardiness is acceptable. That algorithm, called EDF-fm, is able to fully utilize the underlying hardware platform's available capacity. Moreover, it has the desirable practical property that migrations are boundary-limited, i.e., they can only occur at job boundaries. Unfortunately, EDF-fm requires restrictions on pertask utilizations, and thus is not optimal. In this paper, a new boundary-limited, semi-partitioned algorithm is presented for SRT systems that is the first such algorithm to be optimal. This algorithm, called EDF-os, is similar to EDF-fm but utilizes several new mechanisms that obviate the need for per-task utilization restrictions. Experiments presented herein show that, not only is EDF-os provably better than EDF-fm with respect to schedulability, tardiness bounds under EDF-os are often much lower as well.

Abstract. The SMS4 block cipher is part of the Chinese WAPI wireless standard. This paper describes the specification and offers a specification for a toy version called simplified SMS4 (S-SMS4). We explore algebraic attacks on SMS4 and S-SMS4 using Gröbner basis attacks on equation systems over GF(2) and GF(2 8 ), as well as attacks using a SAT solver derived from the GF(2) model. A comparison of SAT and Gröbner basis attacks is provided.

Under current analysis, tardiness bounds applicable to global earliest-deadline-first scheduling and related policies depend on per-task worst-case execution times. By splitting job budgets to create subjobs with shorter periods and worst-case execution times, such bounds can be reduced to near zero for implicit-deadline sporadic task systems. However, doing so will result in more preemptions and could create problems for synchronization protocols. This paper analyzes this tradeoff between theory and practice by presenting an overhead-aware schedulability study pertaining to job splitting. In this study, real overhead data from a scheduler implementation in LITMUS RT was factored into schedulability analysis. This study shows that despite practical issues affecting job splitting, it can still yield substantial reductions in tardiness bounds.

Dataflow software architectures are prevalent in prototypes of advanced automotive systems, for both driver-assisted and autonomous driving. Safety constraints of these systems necessitate real-time performance guarantees. Automotive prototypes often ensure such constraints through overprovisioning and dedicated hardware; however, a commercially viable system must utilize as few low-cost multicore processors as possible to meet size, weight, and power constraints. In short, these platforms must do more with less. To this end, we develop cache-aware and overhead-cognizant scheduling techniques that lessen guaranteed response times without unnecessarily constraining platform utilization. We implement these techniques in PGM RT , a portable middleware framework for managing real-time dataflow applications on multicore platforms. The efficacy of our techniques is demonstrated through overhead-aware schedulability experiments and runtime observations. Results for our test platform show that cache-aware clustered scheduling outperforms naïve partitioned and global approaches in terms of schedulability and end-to-end response times of dataflows.