arXiv subjects

Roch Guerin

Publications and source records attributed to Roch Guerin.

8 recordsLinked to original sources

arXiv2026

On the Benefits of Traffic "Reprofiling" -- The Multiple Hops Case -- Part II

Delivering hard delay guarantees over packet networks is increasingly important to applications ranging from automotive systems, avionics, industrial control, etc. Traffic control and schedulers play an essential role in enforcing such guarantees. In this paper, we focus on ``simple'' static priority and FIFO schedulers, and explore how reprofiling flows entering the network, i.e., proactively shaping them to a different traffic profile, can deliver delay guarantees with less bandwidth. To that end, we formulate a joint optimization framework and develop efficient algorithms to solve it. Extensive evaluations across both realistic and synthetic topologies demonstrate that, as with more sophisticated schedulers, reprofiling flows is beneficial. They also highlight an intuitive coupling between a scheduler's capability and its ability to leverage more complex reprofiling solutions.

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arXiv2024

Optimizing Edge Offloading Decisions for Object Detection

Recent advances in machine learning and hardware have produced embedded devices capable of performing real-time object detection with commendable accuracy. We consider a scenario in which embedded devices rely on an onboard object detector, but have the option to offload detection to a more powerful edge server when local accuracy is deemed too low. Resource constraints, however, limit the number of images that can be offloaded to the edge. Our goal is to identify which images to offload to maximize overall detection accuracy under those constraints. To that end, the paper introduces a reward metric designed to quantify potential accuracy improvements from offloading individual images, and proposes an efficient approach to make offloading decisions by estimating this reward based only on local detection results. The approach is computationally frugal enough to run on embedded devices, and empirical findings indicate that it outperforms existing alternatives in improving detection accuracy even when the fraction of offloaded images is small.

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arXiv2024

On the Benefits of Traffic "Reprofiling" -- The Multiple Hops Case -- Part I

This paper considers networks where user traffic is regulated through deterministic traffic profiles, e.g., token buckets, and requires hard delay bounds. The network's goal is to minimize the resources it needs to meet those bounds. The paper explores how reprofiling, i.e., proactively modifying how user traffic enters the network, can be of benefit. Reprofiling produces ``smoother'' flows but introduces an up-front access delay that forces tighter network delays. The paper explores this trade-off and demonstrates that, unlike what holds in the single-hop case, reprofiling can be of benefit} even when ``optimal'' schedulers are available at each hop.

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arXiv2024

On the Benefits of Traffic "Reprofiling'' -- The Single Hop Case

The need to guarantee hard delay bounds to traffic flows with deterministic traffic profiles, e.g., token buckets, arises in a number of network settings. Of interest are solutions that offer such guarantees while minimizing network bandwidth. The paper explores a basic building block towards realizing such solutions, namely, a single hop configuration. The main results are in the form of optimal solutions for meeting local deadlines under schedulers of varying complexity and therefore cost. The results demonstrate how judiciously modifying flows' traffic profiles, i.e., reprofiling them, can help simple schedulers reduce the bandwidth they require, often performing nearly as well as more complex ones.

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arXiv2023

Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.

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arXiv2022

Adaptive Edge Offloading for Image Classification Under Rate Limit

This paper considers a setting where embedded devices are used to acquire and classify images. Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy. When local classification is deemed inaccurate, devices can decide to offload the image to an edge server with a more accurate but resource-intensive model. Resource constraints, e.g., network bandwidth, however, require regulating such transmissions to avoid congestion and high latency. The paper investigates this offloading problem when transmissions regulation is through a token bucket, a mechanism commonly used for such purposes. The goal is to devise a lightweight, online offloading policy that optimizes an application-specific metric (e.g., classification accuracy) under the constraints of the token bucket. The paper develops a policy based on a Deep Q-Network (DQN), and demonstrates both its efficacy and the feasibility of its deployment on embedded devices. Of note is the fact that the policy can handle complex input patterns, including correlation in image arrivals and classification accuracy. The evaluation is carried out by performing image classification over a local testbed using synthetic traces generated from the ImageNet image classification benchmark. Implementation of this work is available at https://github.com/qiujiaming315/edgeml-dqn.

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arXiv2021

Impact of Distributed Rate Limiting on Load Distribution in a Latency-sensitive Messaging Service

The cloud's flexibility and promise of seamless auto-scaling notwithstanding, its ability to meet service level objectives (SLOs) typically calls for some form of control in resource usage. This seemingly traditional problem gives rise to new challenges in a cloud setting, and in particular a subtle yet significant trade-off involving load-distribution decisions (the distribution of workload across available cloud resources to optimize performance), and rate limiting (the capping of individual workloads to prevent global over-commitment). This paper investigates that trade-off through the design and implementation of a real-time messaging system motivated by Internet-of-Things (IoT) applications, and demonstrates a solution capable of realizing an effective compromise. The paper's contributions are in both explicating the source of this trade-off, and in demonstrating a possible solution.

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arXiv2015

FixRoute: A Unified Logic and Numerical Tool for Provably Safe Internet Traffic Engineering

The performance of networks that use the Internet Protocol is sensitive to precise configuration of many low-level parameters on each network device. These settings govern the action of dynamic routing protocols, which direct the flow of traffic; in order to ensure that these dynamic protocols all converge to produce some 'optimal' flow, each parameter must be set correctly. Multiple conflicting optimization objectives, nondeterminism, and the need to reason about different failure scenarios make the task particularly complicated. We present a fast and flexible approach for the analysis of a number of such management tasks presented in the context of BGP routing. The idea is to combine {\em logical} satisfiability criteria with traditional {\em numerical} optimization, to reach a desired traffic flow outcome subject to given constraints on the routing process. The method can then be used to probe parameter sensitivity, trade-offs in the selection of optimization goals, resilience to failure, and so forth. The theory is underpinned by a rigorous abstraction of the convergence of distributed asynchronous message-passing protocols, and is therefore generalizable to other scenarios. Our resulting hybrid engine is faster than either purely logical or purely numeric alternatives, making it potentially feasible for interactive production use.

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