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Theory and Algorithms for Scheduling Deadline-constrained Packets in Single-hop and Multi-hop Wireless Networks
This dissertation considers the problem of scheduling deadline-constrained packets in networks, an increasingly relevant problem, which due to the rise of time-sensitive applications such as teleconferencing and video streaming, has recently received renewed attention. To accommodate a diverse range of environments and scenarios, our work investigates single-hop and multi-hop networks, across various traffic models and network conditions, including wired and wireless settings.
We propose algorithms in each setting, with their performance evaluated by considering commonly used benchmarks in the related literature, such as the attained fraction of the real-time capacity region achieved in single-hop networks and the maximization of the cumulative weight of packets reaching their destinations within their deadlines in multi-hop networks. We explore traffic which is either worst-case, or stochastic, and provide different performance guarantees in each case.
The first part of our study focuses on scheduling real-time traffic in single-hop wireless networks with conflict-graph interference models. We propose randomized policies that achieve higher real-time efficiency ratios, compared to state-of-the-art existing algorithms, such as the Largest-Deficit-First algorithm. The research then extends to single-hop wireless networks with unreliable links due to channel fading, designing randomized algorithms that achieve efficiency ratios strictly higher than traditional scheduling algorithms, such as Maximum-Weight Scheduling.
The dissertation proceeds to examine online admission, routing, and scheduling algorithms for multi-hop wireless networks under a general interference graph model. It presents online algorithms that are competitive with the optimal offline algorithms and provides upper bounds on performance which demonstrate the asymptotic optimality of these results. Simulation results illustrate significant improvements over prior approaches.
Furthermore, the research addresses the problem of scheduling packets with end-to-end deadline constraints in both wired and wireless multi-hop networks, in the case of stochastic traffic. It illustrates the first near-optimal approximation algorithms under nontrivial assumptions on traffic and link capacity, showcasing significant improvements over worst-case algorithms in practical settings.
In conclusion, this dissertation contributes scheduling algorithms for deadline-constrained packet delivery in single and multi-hop networks, under various traffic and interference models, and in both wired and wireless settings. The proposed algorithms materially improve the state-of-the-art performance guarantees in each case
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
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