We hypothesize that it is possible to obtain significant gains in operational efficiency through the application of simple analysis techniques to firewall rule sets. This paper describes our experiences with a firewall analysis tool and metrics that we have designed and used to help manage large production rule sets. Firewall rule sets typically become increasingly unwieldy over time. It is com...

Limited authority of actuators implies that real control signals are always constrained, and in almost all cases, this produces a degradation in the performance of the system. It is thus of practical importance to understand the fundamental aspects of this performance degradation. In this paper, we take an initial step by proposing a way to characterize the performance limitations that arise in...

SwitchKV is a new key-value store system design that combines high-performance cache nodes with resourceconstrained backend nodes to provide load balancing in the face of unpredictable workload skew. The cache nodes absorb the hottest queries so that no individual backend node is over-burdened. Compared with previous designs, SwitchKV exploits SDN techniques and deeply optimized switch hardware...

We present a framework for designing cheap control architectures of embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent's embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the cl...

In this work, a finite-horizon zero-sum linear-quadratic differential game, modeling pursuit-evasion problem, was considered. the game’s cost function, of control minimizing player (the minimizer/the pursuer) much smaller than maximizing maximizer/the evader) and state variable. This smallness expressed by positive small multiplier (a parameter) square L2-norm minimizer’s in function. Parameter...

We consider stochastic sequential learning problems where the learner can observe the average reward of several actions. Such a setting is interesting in many applications involving monitoring and surveillance, where the set of the actions to observe represent some (geographical) area. The importance of this setting is that in these applications, it is actually cheaper to observe average reward...