John Z. F. Pang

Dynamic Service Function Chaining in SDN-enabled networks with middleboxes

Network functions typically need to be visited in a specific order to meet certain objectives, giving rise to the notion of Service Function Chaining. Software-Defined-Networking enables fine-grained traffic routing optimization while satisfying correct traversal of network functions. In this work, we investigate the problem of maximizing throughput in SDN-enabled networks with respect to service chaining specifications under both traditional and new constraints. Besides the algorithm design, we also derive rigorous performance bounds. In the offline traffic routing case, we propose Traffic-Merging-Algorithm and prove that, although the underlying optimization problem is generally NP-hard, our algorithm can efficiently compute the optimal solution in practical settings. In the online traffic routing case, we propose the Primal-Dual-Update-Algorithm, which comes with a system parameter that trades off the algorithms throughput competitiveness and its meeting of QoS requirements, and prove that our online algorithm achieves optimal tradeoff. We demonstrate that our solutions can be used to address practical problems by conducting simulation-based evaluation over backbone and data center topologies.

Battery Swapping Assignment for Electric Vehicles: A Bipartite Matching Approach

This paper formulates an optimal station assignment problem for electric vehicle (EV) battery swapping that takes into account both temporal and spatial couplings. The goal is to reduce the total EV cost and station congestion due to temporary shortage in supply of available batteries. We show that the problem is reducible to the minimum weight perfect bipartite matching problem. This leads to an efficient solution based on the Hungarian algorithm. Numerical results suggest that the proposed solution provides a significant improvement over a greedy heuristic that assigns nearest stations to EVs.

Load-side Frequency Regulation with Limited Control Coverage

Increasing renewable energy increases uncertainty in energy systems. As a consequence, generator-side control for frequency regulation, impacted by the slow reaction of generators to meet urgent needs, may no longer suffice. With increasing integration of smart appliances which are able to sense, communicate and control, load-side control can help alleviate the aforementioned problem as it reacts fast and helps to localize disturbances. However, almost all existing methods for optimal load-side control require full information control coverage in the system. Framing the problem as an optimization problem and applying saddle-point dynamics, we obtain a control law that rebalances power and asymptotically stabilizes frequency after a disturbance. We generalize previous work to design a controller which only requires partial control coverage over all nodes, yet still achieves secondary frequency control. We verify these results via simulation.

The efficiency of open access in platforms for networked cournot markets

This paper studies how the efficiency of an online platform is impacted by the degree to which access of platform participants is open or controlled. The study is motivated by an emerging trend within platforms to impose increasingly finegrained control over the options available to platform participants. While early online platforms allowed open access, e.g., Ebay allows any seller to interact with any buyer; modern platforms often impose matches directly, e.g., Uber directly matches drivers to riders. This control is performed with the goal of achieving more efficient market outcomes. However, the results in this paper highlight that imposing matches may create new strategic incentives that lead to increased inefficiency. In particular, in the context of networked Cournot competition, we prove that open access platforms guarantee social welfare within 7/16 of the optimal; whereas controlled allocation platforms can have social welfare unboundedly worse than optimal.

Stabilization of Power Networks via Market Dynamics

This work investigates the increasing interactions between power network dynamics and market dynamics. A dynamical spot pricing mechanism for rational market behavior of generators and loads is designed to model market dynamics, which provably drives a power network to an equilibrium operating point, achieving secondary frequency control and economic dispatch.

Efficient Online Station Assignment for EV Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that make battery swapping requests to a central operator, with the aim of minimizing cost to EVs and congestion at service stations. Inspired by a polynomial-time solvable offline solution via a bipartite matching approach, we develop an efficient online station assignment algorithm that provably achieves a tight (optimal) competitive ratio under mild conditions.

Networked Cournot Competition in Platform Markets: Access Control and Efficiency Loss

This paper studies network design and efficiency loss in online platforms using the model of networked Cournot competition. We consider two styles of platforms: open access platforms and discriminatory access platforms. In open access platforms, every firm can connect to every market, while discriminatory access platforms limit connections between firms and markets in order to improve social welfare. Our results provide tight bounds on the efficiency loss of both open access and discriminatory access platforms. For open access platforms, we show that the efficiency loss at a Nash equilibrium is upper bounded by 3/2. In the case of discriminatory access platforms, we prove that, under an assumption on the linearity of cost functions, a greedy algorithm for optimizing network connections can guarantee the efficiency loss at a Nash equilibrium is upper bounded by 4/3.

Distributed Optimal Frequency Control Considering a Nonlinear Network-Preserving Model.

This paper addresses the distributed optimal frequency control of power systems considering a network-preserving model with nonlinear power flows and excitation voltage dynamics. Salient features of the proposed distributed control strategy are fourfold, first, nonlinearity is considered to cope with large disturbances, second, only a part of generators are controllable, third, no load measurement is required, fourth, communication connectivity is required only for the controllable generators. To this end, benefiting from the concept of “virtual load demand,” we first design the distributed controller for the controllable generators by leveraging the primal-dual decomposition technique. We then propose a method to estimate the virtual load demand of each controllable generator based on local frequencies. We derive incremental passivity conditions for the uncontrollable generators. Finally, we prove that the closed-loop system is asymptotically stable and its equilibrium attains the optimal solution to the associated economic dispatch problem. Simulations, including small and large-disturbance scenarios, are carried on the New England system, demonstrating the effectiveness of our design.