No abstract

Component-based programming models are wellsuited to the design of large-scale, distributed applications because of the ease with which distributed functionality can be developed, deployed, and validated using the models' compositional properties. Existing component models supported by standardized technologies, such as the OMG's CORBA Component Model (CCM), however, incur a number of limitations in the context of cyber physical systems (CPS) that operate in highly dynamic, resource-constrained, and uncertain environments, such as space environments, yet require multiple quality of service (QoS) assurances, such as timeliness, reliability, and security. To overcome these limitations, this paper presents the design of a novel component model called F6COM that is developed for applications operating in the context of a cluster of fractionated spacecraft. Although F6COM leverages the compositional capabilities and port abstractions of existing component models, it provides several new features. Specifically, F6COM abstracts the component operations as tasks, which are scheduled sequentially based on a specified scheduling policy. The infrastructure ensures that at any time at most one task of a component can be active -eliminating race conditions and deadlocks without requiring complicated and error-prone synchronization logic to be written by the component developer. These tasks can be initiated due to (a) interactions with other components, (b) expiration of timers, both sporadic and periodic, and (c) interactions with input/output devices. Interactions with other components are facilitated by ports. To ensure secure information flows, every port of an F6COM component is associated with a security label such that all interactions are executed within a security context. Thus, all component interactions can be subjected to Mandatory Access Control checks by a Trusted Computing Base that facilitates the interactions. Finally, F6COM provides capabilities to monitor task execution deadlines and to configure component-specific fault mitigation actions.

Providing scalable and QoS-enabled (i.e., real-time and reliable) monitoring of resources (both virtual and physical) in the cloud is essential to supporting application QoS properties in the cloud as well as identifying security threats. Existing approaches to resource monitoring in the cloud are based on web interfaces, such as RESTful APIs and SOAP, which cannot provide real-time information efficiently and scalably because of a lack of support for fine-grained and differentiated monitoring capabilities. Moreover, their implementation overhead results in a distinct loss in performance, incurs latency jitter, and degrades reliable delivery of time-sensitive information. To address these challenges this paper presents a novel lighter weight and scalable resource monitoring and dissemination solution based on the publish/subscribe (pub/sub) paradigm. Our solution called SQRT-C leverages the OMG Data Distribution Service (DDS) real-time pub/sub middleware, and uses effective software engineering principles to make it usable with multiple cloud platforms. Preliminary empirical results comparing SQRT-C with contemporary web-based resource usage monitoring services reveals that SQRT-C is significantly better than the conventional approaches in terms of latency, jitter and scalability.

Wider adoption, availability and ubiquity of wireless networking technologies, integrated sensors, actuators, and edge computing devices is facilitating a paradigm shift by allowing us to transition from traditional statically configured vertical silos of Cyber-Physical Systems (CPS) to next generation CPS that are more open, dynamic and extensible. Fractionated spacecraft, smart cities computing architectures, Unmanned Aerial Vehicle (UAV) clusters, platoon of vehicles on highways are all examples of extensible CPS wherein extensibility is implied by the dynamic aggregation of physical resources, affect of physical dynamics on availability of computing resources, and various multi-domain applications hosted on these systems. However, realization of extensible CPS requires resolving design-time and run-time challenges emanating from properties specific to these systems. In this paper, we first describe different properties of extensible CPS -dynamism, extensibility, remote deployment, security, heterogeneity and resilience. Then we identify different design-time challenges stemming from heterogeneity and resilience requirements. We particularly focus on software heterogeneity arising from availability of various communication middleware. We then present appropriate solutions in the context of a novel domain specific language, which can be used to design resilient systems while remaining agnostic to middleware heterogeneities. We also describe how this language and its features have evolved from our past work. We use a platform of fractionated spacecraft to describe our solution.

Improvements in mobile networking combined with the ubiquitous availability and adoption of low-cost development boards have enabled the vision of mobile platforms of Cyber-Physical Systems (CPS), such as fractionated spacecraft and UAV swarms. Computation and communication resources, sensors, and actuators that are shared among different applications characterize these systems. The cyber-physical nature of these systems means that physical environments can affect both the resource availability and software applications that depend on resource availability. While many application development and management challenges associated with such systems have been described in existing literature, resilient operation and execution have received less attention. This paper describes our work on improving runtime support for resilience in mobile CPS, with a special focus on our runtime infrastructure that provides autonomous resilience via selfreconfiguration. We also describe the interplay between this runtime infrastructure and our design-time tools, as the later is used to statically determine the resilience properties of the former. Finally, we present a use case study to demonstrate and evaluate our design-time resilience analysis and runtime self-reconfiguration infrastructure.