Some Machine-to-Machine (M2M) communication links particularly those in a industrial automation plant have stringent latency requirements. In this paper, we study the delay-performance for the M2M uplink from the sensors to a Programmable Logic Controller (PLC) in a industrial automation scenario. The uplink traffic can be broadly classified as either Periodic Update (PU) and Event Driven (ED). The PU arrivals from different sensors are periodic, synchronized by the PLC and need to be processed by a prespecified firm latency deadline. On the other hand, the ED arrivals are random, have low-arrival rate, but may need to be processed quickly depending upon the criticality of the application. To accommodate these contrasting Quality-of-Service (QoS) requirements, we model the utility of PU and ED packets using step function and sigmoidal functions of latency respectively. Our goal is to maximize the overall system utility while being proportionally fair to both PU and ED data. To this end, we propose a novel online QoS-aware packet scheduler that gives priority to ED data as long as that results the latency deadline is met for PU data. However as the size of networks increases, we drop the PU packets that fail to meet latency deadline which reduces congestion and improves overall system utility. Using extensive simulations, we compare the performance of our scheme with various scheduling policies such as First-Come-First-Serve (FCFS), Earliest-Due-Date (EDD) and (preemptive) priority. We show that our scheme outperforms the existing schemes for various simulation scenarios.
Lastly, a number of scheduling algorithms have been proposed specifically for real-time embedded systems (see @cite_1 and references therein) that are agnostic to the application scenario, wireless technology used or hardware-software architecture. These schemes assume hybrid task sets comprising of periodic requests and aperiodic requests. They are broadly classified into Fixed-priority and Dynamic priority assignments. The goal of all these schemes is to guarantee completion of service (before deadline) for all periodic request and simultaneously aim to reduce the average response times of aperiodic requests. Fixed priority schemes schedule periodic tasks using Rate-Monotonic algorithm but differ in service for aperiodic tasks. Dynamic scheduling algorithms schedule periodic tasks using EDD scheme and allow better processor utilization and enhance aperiodic responsiveness as compared to the fixed priority schemes. The drawbacks of these schemes is that they assume that the random service times for each tasks are known . Hence these schemes are not truly in their present form and would require considerable modifications.
@cite_1
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Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications is a basic treatise on real-time computing, with particular emphasis on predictable scheduling algorithms. It introduces the fundamental concepts of real-time computing, illustrates the most significant results in the field, and provides the essential methodologies for designing predictable computing systems which can be used to support critical control applications. This volume serves as a textbook for advanced level courses on the topic. Each chapter provides basic concepts, which are followed by algorithms that are illustrated with concrete examples, figures and tables. Exercises are included with each chapter and solutions are given at the end of the book. The book also provides an excellent reference for those interested in real-time computing for designing and or developing predictable control applications.