HTTP underpins modern Internet services, and providers enforce quotas to regulate HTTP API traffic for scalability and reliability. When requests exceed quotas, clients are throttled and must retry. Server-side enforcement protects the service. However, when independent clients’ usage counts toward a shared quota, server-only controls are inefficient; clients lack visibility into others’ load, causing their retry attempts to potentially fail. Indeed, retry timing is important since each attempt incurs costs and yields no benefit unless admitted. While centralized coordination could address this, practical limitations have led to widespread adoption of simple client-side strategies like exponential backoff. As we show, these simple strategies cause excessive retries and significant costs. We design adaptive client-side mechanisms requiring no central control, relying only on minimal feedback. We present two algorithms: ATB, an offline method deployable via service workers, and AATB, which enhances retry behavior using aggregated telemetry data. Both algorithms infer system congestion to schedule retries. Through emulations with real-world traces and synthetic datasets with up to 100 clients, we demonstrate that our algorithms reduce HTTP 429 errors by up to 97.3% compared to exponential backoff, while the modest increase in completion time is outweighed by the reduction in errors.

We introduce Hermes, a general-purpose networking architecture built on an overlay of reconfigurable proxies. Hermes delegates networking responsibilities from applications and services to the overlay proxies. It employs a range of proxying and tunneling techniques, utilizes HTTP as its core component, and incorporates assisting components to facilitate service delivery, enhance communication, and improve end-users' experience. To substantiate these benefits, we prototyped Hermes and demonstrated its ability to efficiently address service and communication challenges. We showed that Hermes enables end-to-end solutions for compatibility with legacy applications and protocols and reliable delivery in highly disadvantaged networking conditions. Furthermore, Hermes demonstrated its ability to provide end-to-end, business-logic-driven handling of general IP traffic and to serve as a communication pipeline for Named Data Networking, facilitating the development and adoption of future networking architectures.

This paper systematically evaluates the performance of QUIC/HTTP3 (H3) and TCP/HTTP2 (H2) in proxy-enhanced environments. H3 integrates UDP-based flow-controlled streams, built-in TLS, multiplexing, and connection migration to better support modern web communication. While prior studies show that H3 can outperform or underperform H2 depending on network conditions, the role of proxies and connection migration remains underexplored. We assess a variety of H2 and H3 client implementations across, particularly in lossy networks and proxy setups. Our findings show that proxies can significantly enhance H2 performance, yielding a 90% improvement in single-stream downloads under severe impairments when used with the BBR congestion control algorithm. In contrast, proxies have minimal impact on H3, which maintains consistent performance due to its internal mechanisms. H3 excels under high-loss and high-latency conditions, leveraging connection migration and multiplexing to deliver up to 88.36% improvement in migration scenarios and 81.5% in extreme loss cases. While optimized H2 can match H3 in some settings, H3 is more robust overall, showing less sensitivity to proxies, impairments, and congestion control variations.

We have been working to create a novel and practical software infrastructure to enable networking researchers to develop, evaluate, and demonstrate networked systems, services, and protocols using modern real-world devices and platforms. As a first demonstration of this, we will show an Envoy sidecar overlay network supporting smartphone requested web traffic over a network with intermittent links with real time network monitoring and audience participation.

Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers need to prioritize service requests and even reject some of low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including the ability to control the migration of virtual functions. We show the problem is more practical and comprehensive than the previous studies, and propose an MILP formulation of it along with two solving algorithms. The first algorithm is a fast polynomial-time heuristic that calculates an initial feasible solution to the problem. The second algorithm is an exact method that utilizes the initial feasible solution to achieve the optimal solution quickly. Using extensive simulations, we evaluate the algorithms and show the proposed heuristic can find a feasible solution in at least 83% of the simulation runs in less than 7 seconds, and the exact algorithm can achieve 25% more profit 8 times faster than the state-of-the-art MILP solving methods.

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices.This paper proposes an SDN-based solution, executable both offline and online, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline model allows to optimize the problem for a batch of flow requests, but is computationally expensive, especially the variant where flows can be split up over parallel paths. For practical, dynamic situations, an online approach is proposed that periodically recalculates the optimal solution for all requested flows, while using shortest path routing and a greedy heuristic for bandwidth allocation for the intermediate flows.Afterwards, the offline approaches are evaluated through simulations while the online approach is validated through physical experiments with SDN switches, both in a scenario with 500 best-effort and 50 emergency flows. The results show that the offline algorithm is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. As a proof-of-concept, a physical setup with Zodiac switches effectively validates the feasibility of the online approach in a realistic setup.

Service function chaining along with network function virtualization enable flexible and rapid provisioning of network services to meet increasing demand for short-lived services with diverse requirements. In this paradigm, the main question to be answered is how to deploy the requested services by means of creating virtual network function (VNF) instances and routing the traffic between them, according to the services specifications. In this paper, we define the energy aware service deployment problem, and present the ILP formulation of it by considering limited traffic processing capacity of VNF instances and management concerns. We apply the Benders decomposition technique to decompose the problem into two smaller problems: master and sub-problem. As it is NP-Hard to find a non-trivial solution to the ILP master problem, we resort to the relaxed LP version of the problem. Then, we design methods based on the feasibility pump and duality theorem to rapidly calculate a near-optimal integer solution. The extensive simulation results show even in a network with 24 switches and 40 servers, our algorithm can deploy 35 requests in less than 3 seconds while the total power consumption is only about 1.3 times of the optimal solution obtained by the exhaustive exact approach. Moreover, it significantly outperforms the prominent SFC deployment algorithms in the fat-tree topology.