With the increasing adoption of Large Language Models (LLMs), there are proposals to replace human Teaching Assistants (TAs) with LLM-based AI agents for providing feedback to students. In this paper, we explore a new hybrid model where human TAs receive AI-generated feedback for CS1 programming exercises, which they can then review and modify as needed. We conducted a large-scale randomized intervention with 185 CS1 undergraduate students, comparing the efficacy of this hybrid approach against manual feedback and direct AI-generated feedback.

The Internet congestion control landscape is rapidly evolving. Since the introduction of BBR and the deployment of QUIC, it has become increasingly commonplace for companies to modify and implement their own congestion control algorithms (CCAs). To respond effectively to these developments, it is crucial to understand the state of CCA deployments in the wild. Unfortunately, existing CCA identification tools are not future-proof and do not work well with modern CCAs and encrypted protocols like QUIC. In this paper, we articulate the challenges in designing a future-proof CCA identification tool and propose a measurement methodology that directly addresses these challenges. The resulting measurement tool, called Nebby, can identify all the CCAs currently available in the Linux kernel and BBRv2 with an average accuracy of 96.7%. We found that among the Alexa Top 20k websites, the share of BBR has shrunk since 2019 and that only 8% of them responded to QUIC requests. Among these QUIC servers, CUBIC and BBR seem equally popular. We show that Nebby is extensible by extending it for Copa and an undocumented family of CCAs that is deployed by 6% of the measured websites, including major corporations like Hulu and Apple.

Since its introduction in 2015, QUIC has seen rapid adoption and is set to be the default transport stack for HTTP3. Given that developers can now easily implement and deploy their own congestion control algorithms in the user space, there is an imminent risk of the proliferation of QUIC implementations of congestion control algorithms that no longer resemble their corresponding standard kernel implementations.

Packet loss due to link corruption is a major problem in large warehouse-scale datacenters. The current state-of-the-art approach of disabling corrupting links is not adequate because, in practice, all the corrupting links cannot be disabled due to capacity constraints. In this paper, we show that, it is feasible to implement link-local retransmission at sub-RTT timescales to completely mask corruption packet losses from the transport endpoints. Our system, LinkGuardian, employs a range of techniques to (i) keep the packet buffer requirement low, (ii) recover from tail packet losses without employing timeouts, and (iii) preserve packet ordering. We implement LinkGuardian on the Intel Tofino switch and show that for a 100G link with a loss rate of 10−3, LinkGuardian can reduce the loss rate by up to 6 orders of magnitude while incurring only 8% reduction in effective link speed. By eliminating tail packet losses, LinkGuardian improves the 99.9th percentile flow completion time (FCT) for TCP and RDMA by 51x and 66x respectively. Finally, we also show that in the context of datacenter networks, simple out-of-order retransmission is often sufficient to significantly mitigate the impact of corruption packet loss for short TCP flows.

Packet corruption loss is a serious problem in datacenter networks. A large-scale study by Microsoft reported that the number of packets lost due to corruption is comparable to those lost due to congestion. Previous attempts to mitigate the impact of packet corruption loss seek to avoid the faulty links by routing around them, at the cost of reduced link capacities and disruption to the rest of the network.

The QUIC standard is expected to replace TCP in HTTP 3.0. While QUIC implements a number of the standard features of TCP differently, most QUIC stacks re-implement standard congestion control algorithms. This is because these algorithms are well-understood and time-tested. However, there is currently no systematic way to ensure that these QUIC congestion control protocols are implemented correctly and predict how these different QUIC implementations will interact with other congestion control algorithms on the Internet.

Since its introduction in 2016, BBR has grown in popularity rapidly and likely already accounts for more than 40% of the Internet's downstream traffic. In this paper, we investigate the following question: given BBR's performance benefits and rapid adoption, is BBR likely to completely replace CUBIC just like how CUBIC replaced New Reno?

The Internet’s congestion control landscape is currently in the midst of an unprecedented paradigm shift. A recent measurement study found that BBR, a congestion control algorithm introduced by Google in 2016, has seen rapid adoption and is deployed at more than 20% of the Alexa Top 20,000 websites. Encouraging early deployment results from Google, Dropbox and Spotify suggest that BBR could potentially replace traditional loss-based congestion control algorithms like CUBIC. In this paper, we study the interactions between CUBIC and BBR and show that the underlying interactions can be modeled as a normal form game. Our game-theoretic analysis and testbed measurements suggest that while BBR seems to achieve somewhat better performance than CUBIC on the Internet today, this advantage will decrease as the proportion of BBR flows increases. The distribution of congestion control algorithms on the Internet would likely reach a Nash Equilibrium, where no flow has the incentive to switch from CUBIC to BBR, or vice versa. We also found that the distribution of CUBIC and BBR flows in this Nash Equilibrium will be dependent mainly on the size of the bottleneck buffer, and marginally on the RTT distribution of the flows. Our results suggest that the future Internet will likely be more heterogeneous and that buffer sizing will continue to have a significant impact on Internet congestion control.

In datacenter networks, flows need to complete as quickly as possible because the flow completion time (FCT) directly impacts user experience, and thus revenue. Link failures can have a significant impact on short latency-sensitive flows because they increase their FCTs by several fold. Existing link failure management techniques cannot keep the FCTs low under link failures because they cannot completely eliminate packet loss during such failures. We observe that to completely mask the effect of packet loss and the resulting long recovery latency, the network has to be responsible for packet loss recovery instead of relying on end-to-end recovery. To this end, we propose Shared Queue Ring (SQR), an on-switch mechanism that completely eliminates packet loss during link failures by diverting the affected flows seamlessly to alternative paths. We implemented SQR on a Barefoot Tofino switch using the P4 programming language. Our evaluation on a hardware testbed shows that SQR can completely mask link failures and reduce tail FCT by up to 4 orders of magnitude for latency-sensitive workloads.

Automated program repair has been used to provide feedback for incorrect student programming assignments, since program repair captures the code modification needed to make a given buggy program pass a given test-suite. Existing student feedback generation techniques are limited because they either require manual effort in the form of providing an error model, or require a large number of correct student submissions to learn from, or suffer from lack of scalability and accuracy. In this work, we propose a fully automated approach for generating student program repairs in real-time. This is achieved by first re-factoring all available correct solutions to semantically equivalent solutions. Given an incorrect program, we match the program with the closest matching refactored program based on its control flow structure. Subsequently, we infer the input-output specifications of the incorrect program's basic blocks from the executions of the correct program's aligned basic blocks. Finally, these specifications are used to modify the blocks of the incorrect program via search-based synthesis. Our dataset consists of almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. Our experimental results suggest that our method is more effective and efficient than recently proposed feedback generation approaches. About 30% of the patches produced by our tool Refactory are smaller than those produced by the state-of-art tool Clara, and can be produced given fewer correct solutions (often a single correct solution) and in a shorter time. We opine that our method is applicable not only to programming assignments, and could be seen as a general-purpose program repair method that can achieve good results with just a single correct reference solution.

Duty-cycling is generally adopted in existing sensor networks to reduce power consumption and these networks depend on neighbor discovery protocols to ensure that nodes wake up and discover each other. For different neighbor discovery protocols, the discovery latency is determined by two factors: the wake-sleep pattern and slot size. To the best of our knowledge, previous works on neighbor discovery have thus far been focused on improving the wake-sleep pattern. In this paper, we investigate the extent to which we can improve discovery latency by reducing the slot size. We found that by reducing the slot size, i.e., reducing the listening time in active slots, the collisions between beacons and synchronization between nodes become more severe, which can lead to discovery failures that are not predicted by existing theoretical models. We show that we can mitigate these effects by reducing the number of beacons and introducing randomization. We propose a new continuous-listening-based neighbor discovery algorithm called Spotlight. Our evaluations with a practical sensor testbed suggest that Spotlight can achieve a 50% reduction in discovery latency over existing state-of-the-art neighbor discovery protocols without increasing power consumption in existing sensor networks.

TCP does not work well in modern cellular networks because the current congestion-window-based (cwnd-based) congestion control mechanism intimately couples congestion control and packet dispatch, which provides TCP with only indirect control of the effective data rate. The throughput degradation arising from the cwnd-based mechanism is especially serious when the uplink is congested. We describe PropRate, a new rate-based TCP algorithm that directly regulates the packets in the bottleneckbuffer to achieve a trade-off in terms of delay and throughput along a more efficient frontier than conventional cwnd-based TCP variants. To the best of our knowledge, PropRate is the first TCP algorithm that allows an application to set and achieve a target average latency, if the network conditions allow for it. Also, unlike the cwnd-based TCP mechanism, our new rate-based TCP mechanism is significantly more resilient to saturated uplinks in cellular networks. PropRate does not require modifications at the receiver and is amenable to practical deployment in the base stations and proxies in mobile cellular networks.

No abstract available.

Neighbor discovery is essential for docking applications, where mobile nodes communicate with static nodes situated at various rendezvous points. In existing neighbor discovery protocols, the probabilistic protocols perform well in the average-case but have a periodic, unpredictable and unbounded discovery latency. While the deterministic protocols can provide a bounded worst-case discovery latency, they achieve this by sacrificing the average-case performance. In this paper, we propose a new synchronization technique, called Mobility-Assisted Slot index Synchronization (MASS). MASS improves the average-case performance of deterministic neighbor discovery protocols via slot index synchronization, without incurring additional energy consumption. We evaluate MASS through both theoretical analysis and simulations of the real traces from a tourist tracking system deployed at Mogao Grottoes, a famous cultural heritage site in China. We show that MASS can reduce the average discovery latency of state-of-the-art deterministic neighbor discovery protocols by up to 2 orders of magnitude.

Recent advances in unmanned aerial vehicle (UAV) technologies have made it possible to deploy an aerial video surveillance system to provide an unprecedented aerial perspective for ground monitoring in real time. Multiple UAVs would be required to cover a large target area, and it is difficult for users to visualize the overall situation if they were to receive multiple disjoint video streams. To address this problem, we designed and implemented SkyStitch, a multiple-UAV video surveillance system that provides a single and panoramic video stream to its users by stitching together multiple aerial video streams. SkyStitch addresses two key design challenges: (i) the high computational cost of stitching and (ii) the difficulty of ensuring good stitching quality under dynamic conditions. To improve the speed and quality of video stitching, we incorporate several practical techniques like distributed feature extraction to reduce workload at the ground station, the use of hints from the flight controller to improve stitching efficiency and a Kalman filter-based state estimation model to mitigate jerkiness. Our results show that SkyStitch can achieve a stitching rate that is 4 times faster than existing state-of-the-art methods and also improve perceptual stitching quality. We also show that SkyStitch can be easily implemented using commercial off-the-shelf hardware.

In this paper, we describeFairMesh, which is the first attempt at mitigating the unfairnessarising from physical layer capture (PLC)in 802.11 mesh networks. In the presence of PLC, which is surprisingly common in practical mesh networks, existing state-of-art solutions either fail to correctly identify the sender that needs to be throttled or are too aggressive in reducing the sending rate. FairMesh is able to accurately detect unfairness quickly and employs a simple$CW_{min}$adjustment algorithm to achieve approximate max-min fairness. Our key insight is that the nodes that cause an unfair situation to arise and can act to remedy it are often distinct from the ones that can accurately assess the degree of unfairness. To the best of our knowledge, we are the first to decouple the detection and assessment of unfairness from the remedial action. A key strength of our approach is itssimplicity, which makes it amenable for deployment in practical 802.11 mesh networks to allow an arbitrary number of flows to operate concurrently without modifications to the 802.11 MAC. We show via simulation and with experiments on a 20-node outdoor 802.11 wireless mesh testbed that FairMesh has many desirable properties. First, it is fully distributed and has negligible control overhead. Second, it achieves approximate max-min fairness, and can be modified to support a different notion of fairness (e.g., proportional fairness). Third, it can handle multiple (more than two) competing links and can scale up to mesh networks with tens of nodes. Fourth, it remains efficient under high data rates and high loss rates. Finally, FairMesh interacts well with TCP and maintains good fairness when a multi-hop flow competes with a single-hop flow.

Previous work on 802.1 1x (Wi-Fi) power control have focused almost exclusively on the mitigation of interference from data frames. Since Wi-Fi wireless access points are increasingly ubiquitous, it is now common for an 802.1 1x client to be able to receive signals from more than 10 APs simultaneously in a modern urban operating environment. At such high densities, we found that the interference from the MAC acknowledgment (ACK) frames can potentially reduce throughput by several fold. We show that by dynamically adjusting the transmission power of the ACK frames, a simple ACK power control algorithm, which we call MinPACK, can significantly mitigate interference and increase throughput by a median of 31%, while simultaneously improving fairness. MinPACK is complementary to existing data frames power control algorithms, and adapts rapidly to dynamic environments.

Given the growing popularity of peer-to-peer file distribution in commercial applications, it is important to understand the challenges of using p2p file-sharing protocols for file distribution, and how extreme conditions such as flash crowds affect the efficiency of file distribution. In this light, there is a need to understand the impact of the utilization of available bandwidth on the performance of peer-assisted file distribution systems. With a simple measurement study on PlanetLab, we identified distinct phases in peer bandwidth utilization over the download duration. Based on the evolution of the utilization of available peer bandwidth over time, we formulated an analytical model for flash crowds in homogeneous and heterogeneous bandwidth swarms. The model estimates the instantaneous download rate and the average file download time with 10 percent error for swarms up to 160 peers. Our model can be used to predict the scalability of the system when the number of peers increases, and to provision for flash crowds by estimating the server bandwidth to achieve a minimum quality of service. Lastly, we demonstrate how our model is applied to new p2p protocols to understand their design and performance problems.

It is not uncommon for the uplink buffers of cellular data networks to be saturated when the uplink bandwidths are low. This can cause the ACK packets for a downlink TCP flow to be severely delayed. Since existing TCP implementations are ACK-clocked, the downstream flow will suffer significant degradation, causing the downlink to be under-utilized. We present a new TCP variant, called TCP Receiver-Rate Estimation (TCP-RRE), that addresses this problem directly by eliminating ACK clocking. Instead, it uses TCP timestamps to estimate the receiving rate at the receiver, which it then uses to determine the sending rate. We show that TCP-RRE is able to improve download speeds by 2 to 4 times compared to existing TCP variants in both simulation and on real commercial cellular data networks. Our solution is practical because it is compatible with existing TCP implementations, requires no modifications to existing mobile devices, and is thus immediately deployable in existing ISP proxies.

We design and evaluate a new type of wireless mesh nodes called Dyntenna nodes that are equipped with steerable omnidirectional antenna. Designed for 3D wireless mesh networks, these nodes adaptively adjust the antenna orientation to increase throughput by improving the Received Signal Strength Indicator (RSSI) reading between nodes. We demonstrate the importance of being able to programmatically orient the antenna, by presenting the measurement results from our 3D urban mesh testbed. We propose a simple antenna adjustment algorithm that can improve the throughput for 26% of one-hop paths and 35% of multi-hop paths by a median value of 31% and 46%, respectively. Our algorithm converges quickly and typically probes less than 10% of all possible antenna orientations on average.

The capacity of access links has increased dramatically in recent times, and bottlenecks are moving deeper into the Internet core. When bottlenecks occur in a core (or AS-AS peering) link, it is possible to use additional detour paths to improve the end-to-end throughput between a pair of source and destination nodes. We propose and evaluate a new massively multipath (mPath) source routing algorithm to improve end-to-end throughput for high-volume data transfers. We demonstrate that our algorithm is practical by implementing a system that employs a set of proxies to establish one-hop detour paths between the source and destination nodes. Our algorithm can fully utilize the available access link bandwidth when good proxied paths are available, without sacrificing TCP-friendliness, and achieves throughput comparable to TCP when such paths cannot be found. For 40 percent of our test cases on PlanetLab, mPath achieved significant improvements in throughput. Among these, 50 percent achieved a throughput of more than twice that of TCP.

We show that the performance of downloads in a 3G/HSPA mobile network can be significantly degraded by a concurrent upload that saturates the uplink buffer on the mobile device. In particular, we found in some instances that download speeds can be reduced by over an order of magnitude from 2,000 kbps to 100 kbps. To mitigate this problem, we propose a new algorithm called Receiver-side Flow Control (RSFC) that regulates the uplink buffer on 3G/HSPA data senders. It uses a feedback loop to monitor the available upload capacity and dynamically adjusts the TCP receiver window (rwnd) accordingly. We evaluated RSFC on the 3G/HSPA networks of three different mobile ISPs and show that for one of them, RSFC can improve the download throughput from less than 400 kbps to up to 1,400 kbps. In the presence of a concurrent upload, RSFC can also reduce website load times from more than 2 minutes to less than 1 minute 90% of the time. Our technique is compatible with existing TCP implementations and can easily be deployed at 3G web proxies without requiring any modification to existing mobile devices.

It is well known that the pervasive IEEE 802.11 MAC is intrinsically unfair. In particular, in the topology shown in Fig. 1(a), when links AB and CD both carry backlogged transmissions, the packets from sender A experience persistent collisions at node B while sender C enjoys collision-free transmission to D. Node A can transmit successfully only if it is able to "insert" its packets into the small inter-packet gaps of C's packets. Thus, we refer to the topology in Fig. 1(a) as the unfair topology and to C and A as the superior and inferior nodes respectively.

Peer-to-peer file-sharing protocols, such as BitTorrent, have been widely used to improve the performance and scalability of file distribution systems. In this paper, we study the performance of peer-assisted file distribution systems with heterogeneous peers. Based on a measurement study of BitTorrent on PlanetLab, we made two key observations: (i) there is a fixed pattern in the utilization of the available bandwidth over the course of a download, and (ii) peers enjoy an amount of service that is commensurate with their contribution. Building on these insights, we developed an analytical model to estimate the download time for each class of peers in a well-provisioned peer-assisted file distribution system based on BitTorrent. Our model accurately predicts the download time and achieves an average error rate of 16.5% for heterogeneous swarms up to 150 nodes in size. We demonstrate how it can be used to estimate the server capacity for achieving a specific quality of service in a large heterogeneous swarm.

A large percentage of links in low-power wireless sensor networks are of intermediate quality. To the best of our knowledge, opportunistic exploitation is currently the only way to use these links. However, such exploitation requires overhearing which consumes a significant amount of energy. In this paper, we propose a new approach to exploit intermediate quality (IQ) links through channel diversity with a new protocol, called IQ Link Transformation Protocol (ILTP), that does not require overhearing. ILTP transforms IQ links into good links thus allowing us to exploit such links continuously rather than using them only opportunistically. Our key insight is that the packet reception ratios (PRR) across different channels on IQ links are not correlated and it is common on such links to find channels that change in quality on the time scale of a few minutes. Consequently, when the link quality of a channel is bad, it is highly likely that a good channel can be found and its quality will remain good for at least a few minutes. Our evaluations on three large-scale test beds demonstrate that ILTP is able to consistently transform the IQ links into good links. We observe that even a poor link with a PRR of 0.05 can be transformed into a good link with a PRR greater than 0.9. When ILTP is integrated with CTP, the default collection tree protocol for TinyOS, the average number of transmissions per end-to-end packet delivery is reduced by 24% to 58%.

Geographic routing is a promising approach for point-to-point routing in wireless sensor networks, but it requires the availability of geographic coordinates. Location devices like GPS do not work indoors and they are often not cost-effective for ubiquitous deployment on a large scale. While it is possible to manually configure coordinates for small sensor networks, it is infeasible to do the same for large-scale networks with thousands of nodes. We present Particle Swarm Virtual Coordinates (PSVC), a distributed virtual coordinate assignment algorithm that employs Particle Swarm Optimization to compute virtual coordinates for geographic routing. PSVC converges faster, achieves a lower hop stretch, and scales well up to large networks of 3,200 nodes compared to NoGeo. Also, PSVC makes no assumptions on the network topology and can naturally be extended to three-dimensional (3D) wireless sensor networks.

Geographic routing is of interest for sensor networks because a point-to-point primitive is an important building block for data-centric applications. While there is a significant body of work on geographic routing algorithms for two-dimensional (2D) networks, geographic routing for practical three-dimensional (3D) sensor networks is relatively unexplored. We show that existing 2D geographic routing algorithms like CLDP/GPSR and GDSTR perform poorly in practical 3D sensor network deployments and describe GDSTR-3D, a new 3D geographic routing algorithm that uses 2-hop neighbor information in greedy forwarding and 2D convex hulls to aggregate node location information. We compare GDSTR-3D to existing algorithms, including CLDP/GPSR, GDSTR, AODV, VRR and S4, both in a real wireless sensor testbed and with TOSSIM simulations to show that GDSTR-3D is highly scalable, requires only a modest amount of storage and achieves routing stretch close to 1.

Commercial entities have started using peer-to-peer (P2P) algorithms for file distribution. For example, Sub Pop Records and Blizzard Entertainment use BitTorrent [2] to distribute large files to their clients. Given that last-mile bandwidths to home users are increasing rapidly, a P2P approach is perhaps the only viable solution to avoid a bottleneck at the servers. Furthermore, the fact that P2P algorithms exploit the bandwidth available to the clients and reduces the provisioning costs at the server, makes P2P even more attractive.

An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code.

We present a new approach for generating virtual coordinates that produces usable coordinates quickly and improves the routing performance of existing geographic routing algorithms. Starting from a set of initial coordinates derived from a set of elected perimeter nodes, greedy embedding spring coordinates (GSpring) detects possible dead ends and uses a modified spring relaxation algorithm to incrementally adjust virtual coordinates to increase the convexity of voids in the virtual routing topology. This reduces the probability that packets will end up in dead ends during greedy forwarding. The coordinates derived by GSpring achieve routing stretch that is up to 50% lower than that for NoGeo, the best existing algorithm for deriving virtual Euclidean coordinates for geographic routing. For realistic network topologies with obstacles, GSpring coordinates achieves from between 10 to 15% better routing stretch than actual physical coordinates.

We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks

Existing geographic routing algorithms depend on the planarization of the network connectivity graph for correctness, and the planarization process gives rise to a well-defined notion of "faces". In this paper, we demonstrate that we can improve routing performance by storing a small amount of local face information at each node. We present a protocol, path vector exchange (PVEX), that maintains local face information at each node efficiently, and a new geographic routing algorithm, greedy path vector face routing (GPVFR), that achieves better routing performance in terms of both path stretch and hop stretch than existing geographic routing algorithms by exploiting available local face information. Our simulations demonstrate that GPVFR/PVEX achieves significantly reduced path and hop stretch than greedy perimeter stateless routing (GPSR) and somewhat better performance than greedy other adaptive face routing (GOAFR+) over a wide range of network topologies. The cost of this improved performance is a small amount of additional storage, and the bandwidth required for our algorithm is comparable to GPSR and GOAFR+ in quasi-static networks.

In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events

Distributed randomized network coding, a robust approach to multicasting in distributed network settings, can be extended to provide Byzantine modification detection without the use of cryptographic functions is presented in this paper.