Static Priority Assignment Research Articles

Optimizing Parameters for Uncertain Execution and Rescheduling Robustness

We describe use of Monte Carlo simulation to optimize schedule parameters for execution and rescheduling robustness in the face of execution uncertainties. We search in the activity input parameter space where a) the onboard scheduler is a one shot non-backtracking scheduler and b) the activity input priority determines the order in which activities are considered for placement in the schedule. We show that simulation driven search for activity parameters outperforms static priority assignment. Our approach can be viewed as using simulation feedback to determine problem specific heuristics e.g. Squeaky Wheel Optimization. These techniques are currently baselined for use in the ground operations of NASA’s next planetary rover, the Mars 2020 rover.

Deployment of real-time systems in the cloud environment

Interest in real-time systems has grown considerably over recent years, primarily due to significant increase in the use of smart technologies and latency-sensitive applications such as cloud gaming, audio/video streaming, and smart homes. Significant work has been done on resource mapping in the cloud environment, and a number of promising results have been established accordingly where the focus is mainly on resource provisioning. However, the applicability of cloud computing services for real-time systems generated from smart systems is still in its infancy and remains unexplored, relatively. To address this gap, we propose a model for the smart systems that periodically offload computational workload to the cloud environment where virtual machines are allocated according to rate-monotonic scheduling policy to ensure requests are processed within the associated deadlines. Deadlines of tasks have been relaxed to improve server utilization as well as maintain a level of confidence in the timing constrains. Experimental results are discussed to highlight the applicability of static priority assignment for the workload in the context of virtual machines allocation.

Utilisation bounds of P-FRP tasks

Priority-based functional reactive programming (P-FRP) is a new functional programming formalism for developing safety-critical embedded systems. P-FRP allows static priority assignment and guarantees real-time response by preempting lower priority tasks. Due to the state-less nature of functional programmes, preempted tasks in P-FRP are aborted and have to restart after the higher priority tasks have completed execution. Since the execution semantics of P-FRP are different from the classical preemptive model of execution, existing utilisation-based sufficient conditions cannot be applied. In this paper, we derive a new utilisation-based sufficient schedulability condition for P-FRP, and validate it using experimental task sets. We then compare the sufficient schedulability condition of P-FRP with that of the preemptive model to demonstrate the severe restrictions on task set sizes in P-FRP. Effect on the utilisation bound under conditional preemption has also been analysed.

A Static Priority Assignment Algorithm with the Least Number of Priority Levels

With the increased penetration of real time systems into rapidly evolving systems, especially, in on-chip systems such as PDA (personal digital assistant) and PSP (play station portable) etc., the performance/price ratio is becoming a major concern for the system designers. At present, to maximize the performance/price ratio, these systems provide a limited number of priority levels to reduce price and the same priority level is assigned to multiple tasks. Such a trade off makes the most widely used static priority assignment algorithms such as RM (rate monotonic) and DM (deadline monotonic), impractical. As an alternative approach, static limited priority assignment assigns priority to tasks in such a way that the system feasibility is maintained by employing only a few priority levels. To date, static limited priority assignments can be classified into two categories, fixed-number priority and least-number priority assignment. This paper proposes a necessary and sufficient condition for analyzing task set feasibility under static limited priority assignment to make it suitable for a wide rage of applications. In addition, the superiority of low-to-high priority assignment strategy and the concept of saturated task group/assignment are highlighted along with several important properties for transformation among the limited priority assignments. A formal proof is drawn in favor of the least-number priority assignment when tasks are staticpriority schedulable. Finally, a least-number priority assignment algorithm with low time complexity is presented and its efficiency is verified by the experimental results.

Feasibility test for real-time communication using wormhole routing

Real-time applications are becoming increasingly demanding in terms of the computational power and the I/O bandwidth required. Massively parallel computers using wormhole routing are the most promising architectures to deliver scalable computational power efficiently. It follows that real-time applications will want to take advantage of these architectures. However, before real-time applications can exploit massively parallel architectures, mechanisms need to be developed to ensure that critical hard-deadline messages meet their deadlines. An off-line feasibility test for real-time wormhole-routed messages is presented. The test will work for any static priority assignment scheme. The effectiveness of several proposed priority assignment methods is evaluated using random (uniform) traffic patterns. Simulations using a flit level simulator (FLS) are performed to validate the test. Simulations show that a high degree of schedulability can be achieved with a finite number of virtual channels, and that packetising long messages increases the overall schedulability of the network.