Jiaqi Zheng

Minimizing Transient Congestion during Network Update in Data Centers

To maximize data center network utilization, the SDN control plane needs to frequently update the data plane as the network conditions change. Since each switch updates its flow table independently and asynchronously, the state transition -- if done directly from the initial to the final stage -- may result in serious flash congestion and packet loss. Prior work strives to find a congestion-free update plan with multiple stages, each with the property that there will be no congestion independent of the update order. Yet congestion-free update requires part of the link capacity to be left vacant and decreases utilization of the expensive network infrastructure. Further, it involves solving a series of LP, which is slow and does not scale well. In this paper, we study the more general problem of minimizing transient congestion during network update, given the number of intermediate stages. This exposes the tradeoff between update speed and transient congestion, and allows an operator to navigate a broader design space for performing network update. We formulate the minimum congestion update problem (MCUP) as an optimization program and prove its hardness. We propose an approximation algorithm and a greedy improvement algorithm to find the update sequence in an efficient and scalable manner. Extensive experiments with Mininet show that our solution reduces update time by 50% and saves control overhead by 30% compared to state of the art.

Radiation Constrained Fair Wireless Charging

Recently wireless power transfer technology (WPT attracts considerable attention, its incurred electromagnetic radiatio (EMR), however, is largely overlooked by most existing literatures. In this paper, we first propose and study the radiation constrained fair wireless charging problem, i.e., maximizing the minimum utility of devices by adjusting the power of wireless chargers with no EMR intensity at any location in the field exceeding a given threshold Rt. To address this problem, we first adopt an area discretization method to transform it fro nonlinear to linear. Then, we propose two algorithms to deal with the reformulated problem. One is called Primal-Dual algorithm, which is semi-distributed and uses lagrangian dual and subgradient methods to solve the problem iteratively. The other is called area division algorithm. It is not only fully distributed and scalable with network size, but also provably achieves an approximation ratio of (1−ε). We conducted extensive simulations and built a field test-bed to verify our theoretical findings. Our simulations show that the approximation ratio of the area division algorithm holds; the Primal-Dual and area division algorithms can have comparable and over 90.9% performance of the optimal results, respectively; and both of the algorithms outperform a baseline algorithm by more than 37%.

Scheduling Congestion- and Loop-Free Network Update in Timed SDNs

Software-defined networks (SDNs) introduce interesting new opportunities in how network routes can be defined, verified, and changed over time. Despite the logically-centralized perspective offered, however, an SDN still needs to be considered a distributed system: rule updates communicated from the controller to the individual switches traverse an asynchronous network and may arrive out-of-order. This can lead to (temporary or permanent) inconsistencies and triggered much research over the last years. We, in this paper, initiate the study of algorithms for consistent network updates in “timed SDNs”—SDNs in which individual node updates can be scheduled at specific times. While technology enabling tightly synchronized SDNs is emerging, the resulting algorithmic problems have not been studied yet. This paper presents, implements and evaluates Chronus, a system which provides provably congestion- and loop-free network updates, while avoiding the flow table space headroom required by existing two-phase update approaches. We formulate the minimum update time problem as an optimization program and propose two polynomial-time algorithms which lie at the heart of Chronus: a decision algorithm to check feasibility and a greedy algorithm to find a good update sequence. Extensive experiments on Mininet and numerical simulations show that Chronus can substantially reduce transient congestion and save over 60% of the rules compared with the state of the art.

We've got you covered: Failure recovery with backup tunnels in traffic engineering

We present Sentinel, a novel failure recovery system for traffic engineering that pre-computes and installs backup tunnels to improve the robustness of software defined wide area networks (WANs). When a link fails, switches locally redirect traffic to backup tunnels and recover immediately in the data plane, thus substantially reducing the transient congestion compared to reactive rescaling. On the other hand Sentinel completely avoids the bandwidth headroom required by existing proactive approaches like FFC, and improves efficiency of operating the expensive WAN. We make several technical contributions in designing Sentinel. We formulate traffic engineering with backup tunnels (TE-BT) as optimization programs. We propose an approximation algorithm to efficiently solve the problem. We further present a concrete design and implementation of the system based on Openflow group tables for backup tunnels. Extensive experiments on Mininet and numerical simulations show that similar to FFC, Sentinel reduces congestion by 45% compared with rescaling, and its algorithm runs much faster than FFC. Sentinel only introduces a small number of additional forwarding rules and can be readily implemented on todays Openflow switches.

Congestion-Minimizing Network Update in Data Centers

The SDN control plane needs to frequently update the data plane as the network conditions change. Since each switch updates its flow table independently and asynchronously, the transition of data plane state—if done directly from the initial to the final stage—may result in serious flash congestion. Prior work strives to find a congestion-free update plan with multiple stages, each with the property that there will be no congestion independent of the update order. Yet congestion-free update may prevent the network from being fully utilized. It also requires solving a series of LP which is time-consuming. In this paper, we propose congestion-minimizing update and focus on two general problems: The first is to find routing at each intermediate stage that minimizes transient congestion for a given number of intermediate stages. The second is to find the minimum number of intermediate stages and an update plan for a given maximum level of transient congestion. We formulate them as two optimization programs and prove their hardness. We propose a set of algorithms to find the update plan in a scalable manner. Extensive experiments with Mininet show that our solution reduces update time by 50 percent and saves control overhead by 38 percent compared to prior work.

Charging Task Scheduling for Directional Wireless Charger Networks

This paper studies the problem of cHarging tAsk Scheduling for direcTional wireless chargEr networks (HASTE), i.e., given a set of rotatable directional wireless chargers on a 2D area and a series of offline (online) charging tasks, scheduling the orientations of all the chargers with time in a centralized offline (distributed online) fashion to maximize the overall charging utility for all the tasks. We prove that HASTE is NP-hard. Then, we prove that a relaxed version of HASTE falls within the realm of maximizing a submodular function subject to a partition matroid constraint, and propose a centralized offline algorithm that achieves (1-ρ)(1-1/e) approximation ratio to address HASTE where ρ is the switching delay of chargers. Further, we propose a distributed online algorithm and prove it achieves 1/2(1-ρ)(1-1/e) competitive ratio. We conduct simulations, and field experiments on a testbed consisting of 8 off-the-shelf power transmitters and 8 rechargeable sensor nodes. The results show that our distributed online algorithm achieves 92.97% of the optimal charging utility, and outperforms the comparison algorithms by up to 26.19% in terms of charging utility.

Cache Assisted Randomized Sharing Counters in Network Measurement

This paper proposes a new counter architecture for network measurement called Cache Assisted and randomizEd ShAring counteRs (CAESAR). One of the greatest challenges for per-flow traffic measurement is designing an online measurement module to keep up with the rapid growth of link speed. To address this challenge, we use a fast on-chip memory as the cache before the slow off-chip SRAM counters, thereby decreasing the accesses per flow to off-chip counters to improve time efficiency without any packet loss. We use randomized sharing counters among multiple flows in SRAM to achieve a compact data structure with high storage efficiency. By removing the impact from other flows sharing counters with a specific flow, we theoretically analyze the expectation and confidence interval of its estimated flow size accurately. In this paper, we use the real-world network traces for software simulations and FPGA experiments on the Xilinx Virtex-7 FPGA chip to validate our theoretical findings. The results show that CAESAR is up to 92.4% and 90% faster than prior work CASE and RCS respectively, and CAESAR reduces the average relative error of CASE and RCS by more than half.

Heterogeneous Wireless Charger Placement with Obstacles

This paper considers the problem of Heterogeneous wIreless charger Placement with Obstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies, on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of 1/2 -- ϵ. We conduct both simulations and field experiments to evaluate the performance of our algorithm and other five comparison algorithms. The results show that our algorithm outperforms the comparison algorithms by at least 33.49% on average.