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

Multicommodity Multicast, Wireless and Fast

We study rumor spreading in graphs, specifically multicommodity multicast problem under the wireless model: given source-destination pairs in the graph, one needs to find the fastest schedule to transfer information from each source to the corresponding destination. Under the wireless model, nodes can transmit to any subset of their neighbors in synchronous time steps, as long as they either transmit or receive from at most one transmitter during the same time step. We improve approximation ratio for this problem from O~(n^(2/3)) to O~(n^((1/2) + epsilon)) on n-node graphs. We also design an algorithm that satisfies p given demand pairs in O(OPT + p) steps, where OPT is the length of an optimal schedule, by reducing it to the well-studied packet routing problem. In the case where underlying graph is an n-node tree, we improve the previously best-known approximation ratio of O((log n)/(log log n)) to 3. One consequence of our proof is a simple constructive rule for optimal broadcasting in a tree under a widely studied telephone model

On Characterizing the Data Access Complexity of Programs

Technology trends will cause data movement to account for the majority of energy expenditure and execution time on emerging computers. Therefore, computational complexity will no longer be a sufficient metric for comparing algorithms, and a fundamental characterization of data access complexity will be increasingly important. The problem of developing lower bounds for data access complexity has been modeled using the formalism of Hong & Kung's red/blue pebble game for computational directed acyclic graphs (CDAGs). However, previously developed approaches to lower bounds analysis for the red/blue pebble game are very limited in effectiveness when applied to CDAGs of real programs, with computations comprised of multiple sub-computations with differing DAG structure. We address this problem by developing an approach for effectively composing lower bounds based on graph decomposition. We also develop a static analysis algorithm to derive the asymptotic data-access lower bounds of programs, as a function of the problem size and cache size

Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms

Electric vehicles (EVs) offer an attractive long-term solution to reduce the dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs with different EV battery charging rate constraints, that is distributed across a smart power grid network requires a coordinated charging schedule to minimize the power generation and EV charging costs. In this paper, we study a joint optimal power flow (OPF) and EV charging problem that augments the OPF problem with charging EVs over time. While the OPF problem is generally nonconvex and nonsmooth, it is shown recently that the OPF problem can be solved optimally for most practical power networks using its convex dual problem. Building on this zero duality gap result, we study a nested optimization approach to decompose the joint OPF and EV charging problem. We characterize the optimal offline EV charging schedule to be a valley-filling profile, which allows us to develop an optimal offline algorithm with computational complexity that is significantly lower than centralized interior point solvers. Furthermore, we propose a decentralized online algorithm that dynamically tracks the valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system, and the simulations show that the online algorithm performs almost near optimality ($<1$ relative difference from the offline optimal solution) under different settings.Comment: This paper is temporarily withdrawn in preparation for journal submissio

Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry

Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas

Cross-layer schemes for performance optimization in wireless networks

Wireless networks are undergoing rapid progress and inspiring numerous applications. As the application of wireless networks becomes broader, they are expected to not only provide ubiquitous connectivity, but also support end users with certain service guarantees. End-to-end delay is an important Quality of Service (QoS) metric in multihop wireless networks. This dissertation addresses how to minimize end-to-end delay through joint optimization of network layer routing and link layer scheduling. Two cross-layer schemes, a loosely coupled cross-layer scheme and a tightly coupled cross-layer scheme, are proposed. The two cross-layer schemes involve interference modeling in multihop wireless networks with omnidirectional antenna. In addition, based on the interference model, multicast schedules are optimized to minimize the total end-to-end delay. Throughput is another important QoS metric in wireless networks. This dissertation addresses how to leverage the spatial multiplexing function of MIMO links to improve wireless network throughput. Wireless interference modeling of a half-duplex MIMO node is presented. Based on the interference model, routing, spatial multiplexing, and scheduling are jointly considered in one optimization model. The throughput optimization problem is first addressed in constant bit rate networks and then in variable bit rate networks. In a variable data rate network, transmitters can use adaptive coding and modulation schemes to change their data rates so that the data rates are supported by the Signal to Noise and Interference Ratio (SINR). The problem of achieving maximum throughput in a millimeter-wave wireless personal area network is studied --Abstract, page iv