12,715 research outputs found
A Switching Fluid Limit of a Stochastic Network Under a State-Space-Collapse Inducing Control with Chattering
Routing mechanisms for stochastic networks are often designed to produce
state space collapse (SSC) in a heavy-traffic limit, i.e., to confine the
limiting process to a lower-dimensional subset of its full state space. In a
fluid limit, a control producing asymptotic SSC corresponds to an ideal sliding
mode control that forces the fluid trajectories to a lower-dimensional sliding
manifold. Within deterministic dynamical systems theory, it is well known that
sliding-mode controls can cause the system to chatter back and forth along the
sliding manifold due to delays in activation of the control. For the prelimit
stochastic system, chattering implies fluid-scaled fluctuations that are larger
than typical stochastic fluctuations. In this paper we show that chattering can
occur in the fluid limit of a controlled stochastic network when inappropriate
control parameters are used. The model has two large service pools operating
under the fixed-queue-ratio with activation and release thresholds (FQR-ART)
overload control which we proposed in a recent paper. We now show that, if the
control parameters are not chosen properly, then delays in activating and
releasing the control can cause chattering with large oscillations in the fluid
limit. In turn, these fluid-scaled fluctuations lead to severe congestion, even
when the arrival rates are smaller than the potential total service rate in the
system, a phenomenon referred to as congestion collapse. We show that the fluid
limit can be a bi-stable switching system possessing a unique nontrivial
periodic equilibrium, in addition to a unique stationary point
Real Time in Plan 9
We describe our experience with the implementation and use of a hard-real-time scheduler for use in Plan 9 as an embedded operating system
Sprinklers: A Randomized Variable-Size Striping Approach to Reordering-Free Load-Balanced Switching
Internet traffic continues to grow exponentially, calling for switches that
can scale well in both size and speed. While load-balanced switches can achieve
such scalability, they suffer from a fundamental packet reordering problem.
Existing proposals either suffer from poor worst-case packet delays or require
sophisticated matching mechanisms. In this paper, we propose a new family of
stable load-balanced switches called "Sprinklers" that has comparable
implementation cost and performance as the baseline load-balanced switch, but
yet can guarantee packet ordering. The main idea is to force all packets within
the same virtual output queue (VOQ) to traverse the same "fat path" through the
switch, so that packet reordering cannot occur. At the core of Sprinklers are
two key innovations: a randomized way to determine the "fat path" for each VOQ,
and a way to determine its "fatness" roughly in proportion to the rate of the
VOQ. These innovations enable Sprinklers to achieve near-perfect load-balancing
under arbitrary admissible traffic. Proving this property rigorously using
novel worst-case large deviation techniques is another key contribution of this
work
Dynamically optimal treatment allocation using Reinforcement Learning
Devising guidance on how to assign individuals to treatment is an important
goal in empirical research. In practice, individuals often arrive sequentially,
and the planner faces various constraints such as limited budget/capacity, or
borrowing constraints, or the need to place people in a queue. For instance, a
governmental body may receive a budget outlay at the beginning of a year, and
it may need to decide how best to allocate resources within the year to
individuals who arrive sequentially. In this and other examples involving
inter-temporal trade-offs, previous work on devising optimal policy rules in a
static context is either not applicable, or sub-optimal. Here we show how one
can use offline observational data to estimate an optimal policy rule that
maximizes expected welfare in this dynamic context. We allow the class of
policy rules to be restricted for legal, ethical or incentive compatibility
reasons. The problem is equivalent to one of optimal control under a
constrained policy class, and we exploit recent developments in Reinforcement
Learning (RL) to propose an algorithm to solve this. The algorithm is easily
implementable with speedups achieved through multiple RL agents learning in
parallel processes. We also characterize the statistical regret from using our
estimated policy rule by casting the evolution of the value function under each
policy in a Partial Differential Equation (PDE) form and using the theory of
viscosity solutions to PDEs. We find that the policy regret decays at a
rate in most examples; this is the same rate as in the static case.Comment: 67 page
Full duplex switched ethernet for next generation "1553B" -based applications
Over the last thirty years, the MIL-STD 1553B data bus has been used in many embedded systems, like aircrafts, ships, missiles and satellites. However, the increasing number and complexity of interconnected subsystems lead to emerging needs for more communication bandwidth. Therefore, a new interconnection system is needed to overcome the limitations of the MIL-STD 1553B data bus. Among several high speed networks, Full Duplex Switched Ethernet is put forward here as an attractive candidate to replace the MIL-STD 1553B data bus. However, the key argument against Switched Ethernet lies in its non-deterministic behavior that makes it inadequate to deliver hard timeconstrained communications. Hence, our primary objective in this paper is to achieve an accepted QoS level offered by Switched Ethernet, to support diverse "1553B"-based applications requirements. We evaluate the performance of traffic shaping techniques on Full Duplex Switched Ethernet with an adequate choice of service strategy in the switch, to guarantee the real-time constraints required by these specific 1553B-based applications. An analytic study is conducted, using the Network Calculus formalism, to evaluate the deterministic guarantees offered by our approach. Theoretical analysis are then investigated in the case of a realistic "1553B"-based application extracted from a real military aircraft network. The results herein show the ability of profiled Full Duplex Switched Ethernet to satisfy 1553B-like real-time constraints
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