Reinforcement learning based multi core scheduling (RLBMCS) for real time systems

Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system

Vertically integrated analysis and transformation for embedded software

Journal ArticleProgram analyses and transformations that are more aggressive and more domain-specific than those traditionally performed by compilers are one possible route to achieving the rapid creation of reliable and efficient embedded software. We are creating a new framework for Vertically Integrated Program Analysis (VIPA) that makes use of information gathered at multiple levels of abstraction such as high-level models, source code, and assembly language. This paper describes our approach and shows how and why it will help create better embedded software

Lock inference for systems software

Journal ArticleWe have developed task scheduler logic (TSL) to automate reasoning about scheduling and concurrency in systems software. TSL can detect race conditions and other errors as well as supporting lock inference: the derivation of an appropriate lock implementation for each critical section in a system. Lock inference solves a number of problems in creating flexible, reliable, and efficient systems software. TSL is based on a notion of asymmetrical preemption relations and it exploits the hierarchical inheritance of scheduling properties that is common in systems software

Dual ceiling protocol for real-time synchronization under preemption threshold scheduling

AbstractThe application of object-oriented design methods to real-time embedded systems is seriously hindered by the lack of existing real-time scheduling techniques that can be seamlessly integrated into these methods. Preemption threshold scheduling (PTS) enables a scalable real-time system design and thus has been suggested as a solution to this problem. However, direct adoption of PTS may lead to long priority inversion since object-oriented real-time systems require synchronization considerations in order to maintain consistent object states. In this paper, we propose the dual ceiling protocol (DCP) in order to solve this problem. While DCP exploits both priority ceilings and preemption threshold ceilings, this is not a straightforward integration of existing real-time synchronization protocols for PTS. We present the rationale for the locking conditions of DCP and show that it leads to the least blocking and response times by comparison with other real-time synchronization protocols. We also present its blocking properties and schedulability analyses. We implemented PTS and DCP in a real-time object-oriented CASE tool and present the associated experimental results, which show that the proposed protocol is a viable solution that is superior to other real-time synchronization protocols for PTS

Schedulability analysis of synchronization protocols based on overrun without payback for hierarchical scheduling frameworks revisited

In this paper, we revisit global as well as local schedulability analysis of synchronization protocols based on the stack resource policy (SRP) and overrun without payback for hierarchical scheduling frameworks based on fixed-priority preemptive scheduling (FPPS). We show that both the existing global and local schedulability analysis are pessimistic, present improved analysis, and illustrate the improvements by means of examples

Optimal Selection of Preemption Points to Minimize Preemption Overhead

Abstract—A central issue for verifying the schedulability of hard real-time systems is the correct evaluation of task execution times. These values are significantly influenced by the preemption overhead, which mainly includes the cache related delays and the context switch times introduced by each preemption. Since such an overhead significantly depends on the particular point in the code where preemption takes place, this paper proposes a method for placing suitable preemption points in each task in order to maximize the chances of finding a schedulable solution. In a previous work, we presented a method for the optimal selection of preemption points under the restrictive assumption of a fixed preemption cost, identical for each preemption point. In this paper, we remove such an assumption, exploring a more realistic and complex scenario where the preemption cost varies throughout the task code. Instead of modeling the problem with an integer programming formulation, with exponential worst-case complexity, we derive an optimal algorithm that has a linear time and space complexity. This somewhat surprising result allows selecting the best preemption points even in complex scenarios with a large number of potential preemption locations. Experimental results are also presented to show the effectiveness of the proposed approach in increasing the system schedulability.

Minimizing stack and communication memory usage in real-time embedded applications

In the development of real-time embedded applications, especially those on systems-on-chip, an efficient use of RAM memory is as important as the effective scheduling of the computation resources. The protection of communication and state variables accessed by concurrent tasks must provide real-time schedulability guarantees while using the least amount of memory. Several schemes, including preemption thresholds, have been developed to improve schedulability and save stack space by selectively disabling preemption. However, the design synthesis problem is still open. In this article, we target the assignment of the scheduling parameters to minimize memory usage for systems of practical interest, including designs compliant with automotive standards. We propose algorithms either proven optimal or shown to improve on randomized optimization methods like simulated annealing.</jats:p