Modern computing systems are often formed by multiple components that interact with each other through the use of shared resources (e.g., CPU, network bandwidth, storage). In this paper, we consider a representative scenario of one such system in the context of an Internet of Things application. The system consists of a network of self-adaptive cameras that share a communication channel, transmitting streams of frames to a central node. The cameras can modify a quality parameter to adapt the amount of information encoded and to affect their bandwidth requirements and usage. A critical design choice for such a system is scheduling channel access, i.e., how to determine the amount of channel capacity that should be used by each of the cameras at any point in time. Two main issues have to be considered for the choice of a bandwidth allocation scheme: (i) camera adaptation and network access scheduling may interfere with one another, (ii) bandwidth distribution should be triggered only when necessary, to limit additional overhead. This paper proposes the first formally verified event-triggered adaptation scheme for bandwidth allocation, designed to minimize additional overhead in the network. Desired properties of the system are verified using model checking. The paper also describes experimental results obtained with an implementation of the scheme.

Devices sharing a network compete for bandwidth, being able to transmit only a limited amount of data. is is for example the case with a network of cameras, that should record and transmit video streams to a monitor node for video surveillance. Adaptive cameras can reduce the quality of their video, thereby increasing the frame compression, to limit network congestion. In this paper, we exploit our experience with computing capacity allocation to design and implement a network bandwidth allocation strategy based on game theory, that accommodates multiple adaptive streams with convergence guarantees. We conduct some experiments with our implementation and discuss the results, together with some conclusions and future challenges.

Time-critical networks require strict delay bounds on the transmission time of packets from source to destination. Routes for transmissions are usually statically determined, using knowledge about worst-case transmission times between nodes. This is generally a conservative method, that guarantees transmission times but does not provide any optimization for the typical case. In real networks, the typical delays vary from those considered during static route planning. The challenge in such a scenario is to minimize the total delay from a source to a destination node, while adhering to the timing constraints. For known typical and worst-case delays, an algorithm was presented to (statically) determine the policy to be followed during the packet transmission in terms of edge choices. In this paper we relax the assumption of knowing the typical delay, and we assume only worst-case bounds are available. We present a reinforcement learning solution to obtain optimal routing paths from a source to a destination when the typical transmission time is stochastic and unknown. Our reinforcement learning policy is based on the observation of the state-space during each packet transmission and on adaptation for future packets to congestion and unpredictable circumstances in the network. We ensure that our policy only makes safe routing decisions, thus never violating predetermined timing constraints. We conduct experiments to evaluate the routing in a congested network and in a network where the typical delays have a large variance. Finally, we analyze the application of the algorithm to large randomly generated networks.

Modern computer systems consist of large number of entities connected through a shared resource. One such system is a video surveillance network consisting of a set of cameras and a network manager. The surveillance cameras capture a stream of images, encode them with a quality factor and transmit them to the manager over a shared constrained network. The central manager allocates bandwidth to the cameras in a fair manner using a threshold based gametheoretic approach. The presence of these multiple control loops that interact with each other leads to complexity in providing performance and safety guarantees. Our previous work explored performance of the eventbased manager using model checking to verify relevant properties of linear models. In this paper we build on our previous work by verifying complex camera models that capture uncertainties during image capture. We model the uncertainties using probabilistic Markov Decision Processes (MDPs) and verify relevant properties of the system. We also evaluate system performance for different system parameters with varying triggering thresholds, showing the advantage of model checking for safe and informed parameter selection. Finally, we evaluate the effect of varying thresholds on manager interventions by capturing images on a commercial off-the-shelf (COTS) camera test-bed.

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