Related papers: FlowDyn: Towards a Dynamic Flowlet Gap Detection u…
In the context of an efficient network traffic engineering process where the network continuously measures a new traffic matrix and updates the set of paths in the network, an automated process is required to quickly and efficiently…
Most load balancing techniques implemented in current data centers tend to rely on a mapping from packets to server IP addresses through a hash value calculated from the flow five-tuple. The hash calculation allows extremely fast packet…
Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A…
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is…
Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications.…
Emerging reconfigurable datacenters allow to dynamically adjust the network topology in a demand-aware manner. These datacenters rely on optical switches which can be reconfigured to provide direct connectivity between racks, in the form of…
Fast flow models accelerate the iterative sampling process by learning to directly predict ODE path integrals, enabling one-step or few-step generation. However, we argue that current fast-flow training paradigms suffer from two fundamental…
Recent data center applications rely on lossless networks to achieve high network performance. Lossless networks, however, can suffer from in-network deadlocks induced by hop-by-hop flow control protocols like PFC. Once deadlocks occur,…
In this paper, we design, implement, and evaluate Polyphony, a system to give network operators a new way to control and reduce the frequency of poor tail latency events in multi-class data center networks, on the time scale of minutes.…
A flooding attack in wireless sensor networks is a type of threat that shortens the lifetimes of the sensor networks. Although flooding attack prevention techniques have been proposed, if a continuous flooding attack occurs, the sensor node…
Network administrators want to detect TCP-level packet reordering to diagnose performance problems and attacks. However, reordering is expensive to measure, because each packet must be processed relative to the TCP sequence number of its…
In networks, availability is of paramount importance. As link failures are disruptive, modern networks in turn provide Fast ReRoute (FRR) mechanisms to rapidly restore connectivity. However, existing FRR approaches heavily impact…
In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation…
Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. In this paper, we propose a novel load balancing algorithm in cloud environments that performs…
Data-parallel (DP) load balancing has emerged as a first-order bottleneck in large-scale LLM serving. When a model is sharded across devices via tensor parallelism (TP) or expert parallelism (EP) and replicated across many DP workers, every…
Wind farm flow control has been a key research focus in recent years, driven by the idea that a collectively operating wind farm can outperform individually controlled turbines. Control strategies are predominantly applied in an open-loop…
This paper first presents a parallel solution for the Flowshop Scheduling Problem in parallel environment, and then proposes a novel load balancing strategy. The proposed Proportional Fairness Strategy (PFS) takes computational performance…
Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training has been shown to improve the straightness of flow…
The continuous growth of big data applications with high computational and scalability demands has resulted in increasing popularity of cloud computing. Optimizing the performance and power consumption of cloud resources is therefore…
Our study uses the centralized, flexible, dynamic, and programmable structure of Software-Defined networks (SDN) to overcome the problems. Although SDN effectively addresses the challenges present in traditional networks, it still requires…