This work studies fixed priority (FP) scheduling of real-time jobs with end-to-end deadlines in a distributed system. Specifically, given a multi-stage pipeline with multiple heterogeneous resources of the same type at each stage, the problem is to assign priorities to a set of real-time jobs with different release times to access a resource at each stage of the pipeline subject to the end-to-end deadline constraints. Note, in such a system, jobs may compete with different sets of jobs at different stages of the pipeline depending on the job-to-resource mapping. To this end, following are the two major contributions of this work. We show that an OPA-compatible schedulability test based on the delay composition algebra can be constructed, which we then use with an optimal priority assignment algorithm to compute a priority ordering. Further, we establish the versatility of pairwise priority assignment in such a multi-stage multi-resource system, compared to a total priority ordering. In particular, we show that a pairwise priority assignment may be feasible even if a priority ordering does not exist. We propose an integer linear programming formulation and a scalable heuristic to compute a pairwise priority assignment. We also show through simulation experiments that the proposed approaches can be used for the holistic scheduling of real-time jobs in edge computing systems.

Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies. By jointly considering job scheduling, server resource allocation, and IoT device mobility, we investigate the deadlineconstrained job offloading and resource management problem in MEC with both communication and computation contentions, aiming to maximize the total energy saved for IoT devices. For the offline version of the problem, where job information is known in advance, we formulate it as an Integer Linear Programming problem and propose an approximation algorithm, LHJS, with a constant performance guarantee. For the online version, where job information is only known upon release, we propose a heuristic algorithm, LBS, that is invoked whenever a job is released. Finally, we conduct experiments with parameters from real-world applications to evaluate their performance.

A significant transition in the characteristics of computational workloads coupled with the developments in computing as well as networking concepts and practices has presented various challenges and opportunities. In this work, we study dynamic offloading of soft real-time tasks in SDN based fog computing system. Preliminary results have been presented to highlight the impact of intra/inter-cluster offloading on the penalty due to deadline miss. Finally, the future research directions have been outlined.

FlexRay offers high-speed reliable transmission in the vehicle domain. Thus, it is one of the most popular in-vehicle communication protocols for the applications that are safety-critical or applications with very low transmission time requirement. Due to the real-time constraint, the worst case end-to-end delay (EED) for such applications transmitting over a shared bus must be known. This paper addresses the problem of computation of EED for a given set of tasks with precedence constraint and corresponding messages, which are scheduled with slot multiplexing for event-triggered communication over the dynamic segment of FlexRay. First, computing precise delay has been shown to be an intractable problem. Subsequently, motivated by the intractability, an efficient estimation technique has been proposed to compute approximate EED. Unlike the common practice of considering the network in isolation, we present a comprehensive timing analysis, which considers the effects of task execution as well. Moreover, EED is allowed to be greater than interactivation time of task graph. The extensive simulation has been performed on the test cases generated with uniform as well as normal distribution.Note to Practitioners—In the past few decades, due to the large-scale use and sophistication of automotive electronic systems, various in-vehicle communication networks and protocols are also evolving. FlexRay is one such in-vehicle communication protocol, which provides the mechanism for the fault-tolerant high-speed communication over FlexRay bus. FlexRay is widely being employed for safety-critical applications in the automotive domain. For such applications, often the timing constraint is imposed with a bound on the worst case EED. Thus, timing properties of the designed network must be verified before applying it on the real domain such that the worst case EED for the propagation of an event at sensor end to actuator end is known. The proposed estimation technique can be used to compute approximate EED for an application scheduled with other applications over the dynamic segment of FlexRay. In addition, various guidelines for designing the network in order to reduce overestimation have also been presented.

Higher reliability and lower energy consumption are conflicting, yet among the most important design objectives for the real-time systems. Moreover, in the domain of real-time systems, non-preemptive scheduling is relatively unexplored with objectives such as reliability and energy. Thus we propose an active replication based framework to schedule a set of periodic real-time tasks in the non-preemptive heterogeneous environment such that the given reliability and timing constraints are satisfied whereas the energy consumption is minimized. First, we formulate the problem as a constraint optimization problem that provides an optimal solution; however, it does not scale well. Thus, we also propose heuristics which apply reservation of processors and reallocation of jobs, to compute suboptimal solution efficiently in terms of energy consumption as well as schedulability. Heuristics make use of the interplay of task-level reliability target, reliability of replicas, number of replicas, reliability of tasks, and energy consumption. We perform an experimental study on the test cases generated by extending UUnisort algorithm[1]and observe the effect of various simulation parameters on energy consumption and schedulability.

Fog computing is a non-trivial extension of cloud computing to overcome many inherent limitations, such as huge network bandwidth and high latency. Fog computing involves a significantly large number of fog instances, unlike few centralized cloud servers. This work is an attempt to address two crucial challenges in fog computing systems in an integrated manner, namely the pricing of the resources and offloading of real-time tasks to appropriate fog instances. We propose an integrated framework to devise the pricing of the resources to maximize the social welfare and an offloading mechanism to minimize the cost of execution of the workloads with the timing constraint.