14,301 research outputs found
Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks
This paper investigates the cross-layer optimization of fast video delivery
and caching for minimization of the overall video delivery time in a two-hop
relaying network. The half-duplex relay nodes are equipped with both a cache
and a buffer which facilitate joint scheduling of fetching and delivery to
exploit the channel diversity for improving the overall delivery performance.
The fast delivery control is formulated as a two-stage functional non-convex
optimization problem. By exploiting the underlying convex and quasi-convex
structures, the problem can be solved exactly and efficiently by the developed
algorithm. Simulation results show that significant caching and buffering gains
can be achieved with the proposed framework, which translates into a reduction
of the overall video delivery time. Besides, a trade-off between caching and
buffering gains is unveiled.Comment: 7 pages, 4 figures; accepted for presentation at IEEE Globecom, San
Diego, CA, Dec. 201
Marginal productivity index policies for problems of admission control and routing to parallel queues with delay
In this paper we consider the problem of admission control of Bernoulli arrivals to a
buffer with geometric server, in which the controller’s actions take effect one period
after the actual change in the queue length. An optimal policy in terms of marginal
productivity indices (MPI) is derived for this problem under the following three
performance objectives: (i) minimization of the expected total discounted sum of
holding costs and rejection costs, (ii) minimization of the expected time-average sum of
holding costs and rejection costs, and (iii) maximization of the expected time-average
number of job completions. Our employment of existing theoretical and algorithmic
results on restless bandit indexation together with some new results yields a fast
algorithm that computes the MPI for a queue with a buffer size of I performing only
O(I) arithmetic operations. Such MPI values can be used both to immediately obtain the
optimal thresholds for the admission control problem, and to design an index policy for
the routing problem (with possible admission control) in the multi-queue system. Thus,
this paper further addresses the problem of designing and computing a tractable
heuristic policy for dynamic job admission control and/or routing in a discrete time
Markovian model of parallel loss queues with one-period delayed state observation
and/or action implementation, which comes close to optimizing an infinite-horizon
problem under the above three objectives. Our approach seems to be tractable also for
the analogous problems with larger delays and, more generally, for arbitrary restless
bandits with delays
Throughput-driven floorplanning with wire pipelining
The size of future high-performance SoC is such that the time-of-flight of wires connecting distant pins in the layout can be much higher than the clock period. In order to keep the frequency as high as possible, the wires may be pipelined. However, the insertion of flip-flops may alter the throughput of the system due to the presence of loops in the logic netlist. In this paper, we address the problem of floorplanning a large design where long interconnects are pipelined by inserting the throughput in the cost function of a tool based on simulated annealing. The results obtained on a series of benchmarks are then validated using a simple router that breaks long interconnects by suitably placing flip-flops along the wires
Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with
renewable energy source, where the transmission power of a node comes from both
the conventional utility power (AC power) and the renewable energy source. We
assume the transmission power of each node is a function of the local channel
state, local data queue state and local energy queue state only. In turn, we
consider two delay optimization formulations, namely the decentralized
partially observable Markov decision process (DEC-POMDP) and Non-cooperative
partially observable stochastic game (POSG). In DEC-POMDP formulation, we
derive a decentralized online learning algorithm to determine the control
actions and Lagrangian multipliers (LMs) simultaneously, based on the policy
gradient approach. Under some mild technical conditions, the proposed
decentralized policy gradient algorithm converges almost surely to a local
optimal solution. On the other hand, in the non-cooperative POSG formulation,
the transmitter nodes are non-cooperative. We extend the decentralized policy
gradient solution and establish the technical proof for almost-sure convergence
of the learning algorithms. In both cases, the solutions are very robust to
model variations. Finally, the delay performance of the proposed solutions are
compared with conventional baseline schemes for interference networks and it is
illustrated that substantial delay performance gain and energy savings can be
achieved
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