Multi-Point Synchronization for Fog-Controlled Internet of Things

This paper presents a fog-resident controller architecture for synchronizing the operations of large collections of Internet of Things (IoT) such as drones, Internet of Vehicles, etc. Synchronization in IoT is grouped into different classes, use cases identified and multi-point synchronous scheduling algorithms are developed to schedule tasks with varying timing requirements; strict (synchronous) and relaxed (asynchronous and local) onto a bunch of worker nodes that are coordinated by a fog resident controller in the presence of disconnections and worker failures. The algorithms use time-based or component-based redundancy to cope with failures and embed a publish-subscribe message update scheme to reduce the message overhead at the controller as the number of workers increase. The performance of the algorithms are evaluated using trace-driven experiments and practicability is shown by implementing the time-based redundancy synchronous scheduling algorithm in JAMScript -- a polyglot programming platform for Cloud of Things and report initial findings.Comment: 12 pages, 14 figures, journa

Towards Predictable Real-Time Performance on Multi-Core Platforms

Cyber-physical systems (CPS) integrate sensing, computing, communication and actuation capabilities to monitor and control operations in the physical environment. A key requirement of such systems is the need to provide predictable real-time performance: the timing correctness of the system should be analyzable at design time with a quantitative metric and guaranteed at runtime with high assurance. This requirement of predictability is particularly important for safety-critical domains such as automobiles, aerospace, defense, manufacturing and medical devices. The work in this dissertation focuses on the challenges arising from the use of modern multi-core platforms in CPS. Even as of today, multi-core platforms are rarely used in safety-critical applications primarily due to the temporal interference caused by contention on various resources shared among processor cores, such as caches, memory buses, and I/O devices. Such interference is hard to predict and can significantly increase task execution time, e.g., up to 12x on commodity quad-core platforms. To address the problem of ensuring timing predictability on multi-core platforms, we develop novel analytical and systems techniques in this dissertation. Our proposed techniques theoretically bound temporal interference that tasks may suffer from when accessing shared resources. Our techniques also involve software primitives and algorithms for real-time operating systems and hypervisors, which significantly reduce the degree of the temporal interference. Specifically, we tackle the issues of cache and memory contention, locking and synchronization, interrupt handling, and access control for computational accelerators such as GPGPUs, all of which are crucial to achieving predictable real-time performance on a modern multi-core platform.Comment: This is the Ph.D. dissertation of the autho