Related papers: Spritz: Path-Aware Load Balancing in Low-Diameter …

Emerging artificial intelligence (AI) and machine learning (ML) workloads present new challenges of managing the collective communication used in distributed training across hundreds or even thousands of GPUs. This paper presents STrack, a…

Inter-datacenter networks connect dozens of geographically dispersed datacenters and carry traffic flows with highly variable sizes and different classes. Adaptive flow routing can improve efficiency and performance by assigning paths to…

Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows. As the standard TCP congestion control mechanism is designed for achieving "fairness" among competing flows…

Recently, different communities in computer science, telecommunication and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along…

Efficient load-balancing mechanisms are critical for maximizing performance and increasing the quality of service (QoS) of data center networks (DCNs). Obtaining the optimal QoS while minimizing resource consumption remains a significant…

The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of spatial data in real-time. The current scale of spatial data cannot be handled using…

A well-known technique for enhancing the performance and stability of content distribution is the use of multiple dissemination flows. Multipath TCP (MPTCP), the most popular multiflow protocol on the Internet, allows receivers to exploit…

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging energy harvesting…

This paper presents a scalability and load balancing study of the All-Path protocols, a family of distributed switching protocols based on path exploration. ARP-Path is the main protocol and it explores every possible path reaching from…

DPillar has recently been proposed as a server-centric datacenter network and is combinatorially related to (but distinct from) the well-known wrapped butterfly network. We explain the relationship between DPillar and the wrapped butterfly…

This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mechanisms, such as Round Robin (RR) and Weighted Round Robin…

In current Data Center Networks (DCNs), Equal- Cost MultiPath (ECMP) is used as the de-facto routing protocol. However, ECMP does not differentiate between short and long flows, the two main categories of flows depending on their duration…

The proliferation of IoT devices in smart cities challenges 6G networks with conflicting energy-latency requirements across heterogeneous slices. Existing approaches struggle with the energy-latency trade-off, particularly for massive scale…

Source-controlled routing has been proposed as a way to improve flexibility of future network architectures, as well as simplifying the data plane. However, if a packet specifies its path, this precludes fast local re-routing within the…

With the rapid growth of artificial intelligence (AI) workloads in datacenters, the Ultra Ethernet Consortium (UEC) has defined a new high-performance transport layer to deliver the required performance at scale. A core component of this…

Internet traffic continues to grow exponentially, calling for switches that can scale well in both size and speed. While load-balanced switches can achieve such scalability, they suffer from a fundamental packet reordering problem. Existing…

This paper proposes an application-aware multipath packet forwarding framework that integrates Machine Learning Techniques (MLT) and Software Defined Networks (SDN). As the Internet provides a variety of services and their performance…

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause…

Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a great opportunity for applying optical network technologies. Optical networks with reconfigurable…

Large-scale distributed training in production data centers place significant demands on network infrastructure. In particular, significant load balancing challenges arise when processing AI/ML workloads, consisting of low-entropy, bursty…