2,608 research outputs found
Index Policies for Optimal Mean-Variance Trade-Off of Inter-delivery Times in Real-Time Sensor Networks
A problem of much current practical interest is the replacement of the wiring
infrastructure connecting approximately 200 sensor and actuator nodes in
automobiles by an access point. This is motivated by the considerable savings
in automobile weight, simplification of manufacturability, and future
upgradability.
A key issue is how to schedule the nodes on the shared access point so as to
provide regular packet delivery. In this and other similar applications, the
mean of the inter-delivery times of packets, i.e., throughput, is not
sufficient to guarantee service-regularity. The time-averaged variance of the
inter-delivery times of packets is also an important metric.
So motivated, we consider a wireless network where an Access Point schedules
real-time generated packets to nodes over a fading wireless channel. We are
interested in designing simple policies which achieve optimal mean-variance
tradeoff in interdelivery times of packets by minimizing the sum of
time-averaged means and variances over all clients. Our goal is to explore the
full range of the Pareto frontier of all weighted linear combinations of mean
and variance so that one can fully exploit the design possibilities. We
transform this problem into a Markov decision process and show that the problem
of choosing which node's packet to transmit in each slot can be formulated as a
bandit problem. We establish that this problem is indexable and explicitly
derive the Whittle indices. The resulting Index policy is optimal in certain
cases. We also provide upper and lower bounds on the cost for any policy.
Extensive simulations show that Index policies perform better than previously
proposed policies
A High Reliability Asymptotic Approach for Packet Inter-Delivery Time Optimization in Cyber-Physical Systems
In cyber-physical systems such as automobiles, measurement data from sensor
nodes should be delivered to other consumer nodes such as actuators in a
regular fashion. But, in practical systems over unreliable media such as
wireless, it is a significant challenge to guarantee small enough
inter-delivery times for different clients with heterogeneous channel
conditions and inter-delivery requirements. In this paper, we design scheduling
policies aiming at satisfying the inter-delivery requirements of such clients.
We formulate the problem as a risk-sensitive Markov Decision Process (MDP).
Although the resulting problem involves an infinite state space, we first prove
that there is an equivalent MDP involving only a finite number of states. Then
we prove the existence of a stationary optimal policy and establish an
algorithm to compute it in a finite number of steps.
However, the bane of this and many similar problems is the resulting
complexity, and, in an attempt to make fundamental progress, we further propose
a new high reliability asymptotic approach. In essence, this approach considers
the scenario when the channel failure probabilities for different clients are
of the same order, and asymptotically approach zero. We thus proceed to
determine the asymptotically optimal policy: in a two-client scenario, we show
that the asymptotically optimal policy is a "modified least time-to-go" policy,
which is intuitively appealing and easily implementable; in the general
multi-client scenario, we are led to an SN policy, and we develop an algorithm
of low computational complexity to obtain it. Simulation results show that the
resulting policies perform well even in the pre-asymptotic regime with moderate
failure probabilities
Dynamic Control of Tunable Sub-optimal Algorithms for Scheduling of Time-varying Wireless Networks
It is well known that for ergodic channel processes the Generalized
Max-Weight Matching (GMWM) scheduling policy stabilizes the network for any
supportable arrival rate vector within the network capacity region. This
policy, however, often requires the solution of an NP-hard optimization
problem. This has motivated many researchers to develop sub-optimal algorithms
that approximate the GMWM policy in selecting schedule vectors. One implicit
assumption commonly shared in this context is that during the algorithm
runtime, the channel states remain effectively unchanged. This assumption may
not hold as the time needed to select near-optimal schedule vectors usually
increases quickly with the network size. In this paper, we incorporate channel
variations and the time-efficiency of sub-optimal algorithms into the scheduler
design, to dynamically tune the algorithm runtime considering the tradeoff
between algorithm efficiency and its robustness to changing channel states.
Specifically, we propose a Dynamic Control Policy (DCP) that operates on top of
a given sub-optimal algorithm, and dynamically but in a large time-scale
adjusts the time given to the algorithm according to queue backlog and channel
correlations. This policy does not require knowledge of the structure of the
given sub-optimal algorithm, and with low overhead can be implemented in a
distributed manner. Using a novel Lyapunov analysis, we characterize the
throughput stability region induced by DCP and show that our characterization
can be tight. We also show that the throughput stability region of DCP is at
least as large as that of any other static policy. Finally, we provide two case
studies to gain further intuition into the performance of DCP.Comment: Submitted for journal consideration. A shorter version was presented
in IEEE IWQoS 200
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