Timing analysis in existing and emerging cyber physical systems

A main mission of safety-critical cyber-physical systems is to guarantee timing correctness. The examples of safety- critical systems are avionic, automotive or medical systems in which timing violations could have disastrous effects, from loss of human life to damage to machines and/or the environment. Over the past decade, multicore processors have become increasingly common for their potential of efficiency, which has made new single-core processors become relatively scarce. As a result, it has created a pressing need to transition to multicore processors. However, existing safety-critical software that has been certified on single-core processors is not allowed to be fielded on a multicore system as is. The issue stems from, namely, serious inter- core interference problems on shared resources in current multicore processors, which create non-deterministic timing behavior. Since meeting the timing constraints is the crucial requirement of safety-critical real-time systems, the use of more than one core in a multicore chip is currently not certified yet by the authorities. Academia has paid relatively little attention to non-determinism due to uncoordinated I/O communications, as compared with other resources such as cache or memory, although industry considers it as one of the most troublesome challenges. Hence we focused on I/O synchronization, requiring no information of Worst Case Execution Time (WCET) that can get impacted by other interference sources. Traditionally, a two-level scheduling, such as Integrated Modular Avionics system (IMA), has been used for providing temporal isolation capability. However, such hierarchical approaches introduce significant priority inversions across applications, especially in multicore systems, ultimately leading to lower system utilization. To address these issues, we have proposed a novel scheduling mechanism called budgeted generalized rate monotonic analysis (Budgeted GRMS) in which different applications’ tasks are globally scheduled for avoiding unnecessary priority inversions, yet the CPU resource is still partitioned for temporal isolation among applications. Incorporating the issues of no information of WCETs and I/O synchronization, this new scheduling paradigm enables the “safe” use of multicore processors in safety-critical real-time systems. Recently, newly emerging Internet of Things (IoT) and Smart City applications are becoming a part of cyber- physical systems, as the needs are required and the feasibility are getting visible. What we need to pay attention to is that the promises and challenges arising from IoT and Smart City applications are providing new research landscapes and opportunities and fundamentally transforming real-time scheduling. As mentioned earlier, in traditional real-time systems, an instance of a program execution (a process) is described as a scheduling entity, while, in the emerging applications, the fundamental schedulable units are chunks of data transported over communication media. Another transformation is that, in IoT and Smart City applications, there are multiple options and combinations to choose to utilize and schedule since there are massively deployed heterogeneous kinds of sensing devices. This is contrary to the existing real-time work which is given a fixed task set to be analyzed. For that reason, they also suggest variants of performance or quality optimization problems. Suppose a disaster response infrastructure in a troubled area to ensure safety of humanitarian missions. Cameras and other sensors are deployed along key routes to monitor local conditions, but turned off by default and turned on on-demand to save limited battery life. To determine a safe route to deliver humanitarian shipments, a decision-maker must collect reconnaissance information and schedule the data items to support timely decision-making. Such data items acquired from the time-evolving physical world are in general time-sensitive - a retrieved item may become stale and no longer be accurate/relevant as conditions in the physical environment change. Therefore, “when to acquire” affects the performance and correctness of such applications and thus the overall system safety and data timeliness should be carefully considered. For the addressed problem, we explored various algorithmic options for maximizing quality of information, and developed the optimal algorithm for the order of retrievals of data items to make multiple decisions. I believe this is a significant initial step toward expanding timing-safety research landscapes and opportunities in the emerging CPS area