Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.

Current AI training clusters widely use RoCEv2 to improve the communication efficiency of the interconnect networks across machines. RoCEv2 relies on Priority Flow Control (PFC) to ensure a lossless network. However, PFC brings certain side effects, such as head-of-line blocking, congestion spreading, and deadlock. Numerous studies have been proposed to eliminate the side effects. However, unlike traditional high-performance computing applications, distributed machine learning (DML) is not 100 % loss-intolerant. In light of this observation, this paper proposes a loss-tolerant flow control (LTFC). LTFC does not rely on PFC to ensure a lossless environment, but to control packet loss ratio within the tolerance threshold. Compared to the traditional trigger condition, LTFC reduces the likelihood that the PFC will be triggered. Additionally, we replace RoCEv2's default Go-back-N mechanism with a non-retransmission mechanism to eliminate retransmission latency. We demonstrate the bounded-loss tolerance feature of DML on our testbed and evaluate the performance of LTFC in large-scale simulations. Simulation results show that LTFC reduces the average flow completion time (FCT) by up to 26.9 % and tail FCT by up to 16.9 % compared to existing solutions.

The satellite-to-ground link is susceptible to eavesdropping from various open-space locations. In this paper, we investigate a new eavesdropping scenario in a multiconstellation satellite network, where very low earth orbit (VLEO) satellites eavesdrop on low earth orbit (LEO) constellations from legitimate positions. We model the VLEO eavesdropping risks in a theoretical way and propose a crossinterference scheme called LEO active cross-interference (LEOACI) to protect satellite-to-ground downlinks from eavesdropping attacks. The scheme prevents eavesdropping by scheduling spare satellites to transmit artificial noise (AN) over the protected link. Finally, we conduct simulations to evaluate the performance of the LEO-ACI scheme and it shows that VLEO eavesdroppers can possess high eavesdropping coverage and long duration. Meanwhile, the LEO network can ensure that 98.5 % of downlinks are free from eavesdropping at the cost of 12.3 % of the satellite-to-ground capacity degradation.

Intelligent computing center (ICC) is a new type of data center constructed with intelligent computing power, such as graphic processing units (GPUs) and artificial intelligence acceleration cards. With billions of parameters, the emergence of large models (e.g., ChatGPT) presents a significant demand of computing power. It may be challenging for a single ICC to provide the required computing power during large model training. Thus, ICC interconnections (ICCI) will become a typical and effective solution to provide intensive computing power. Due to human activities, traditional computing tasks (e.g., transaction processing and online entertainment) exhibit a tidal effect of computing demand, which leads to the tidal variation of remaining computing resources. Moreover, distributed model training (DMT) tasks are likely to cover peaks and valleys of the tidal effect in computing power. In this case, it is easy for DMT tasks to cause an ICC to become a hotspot (i.e., computing load in an ICC exceeds a desired threshold), which significantly degrades the reliability and performance of the ICC. This paper proposes DeepHM, a deep reinforcement learning-based hotspot management strategy through task migration in ICCI networks. To comprehensively consider the bandwidth metrics of the ICCI network, we further propose a dynamic wavelength allocation strategy, i.e., DeepHM-DWA. Simulation results show that the DeepHM and DeepHM-DWA reduce the hotspot compute unit time blocks by 19% and 18% with fewer number of migrated workers while balancing the computing load among multiple ICCs. DeepHM and DeepHM-DWA reduce the average completion time ratio of the DMT tasks by 2% and 5%, respectively.

In recent years, research on satellite networks has gained significant attention, with their capability for seamless global coverage and meeting real-time communication demands serving as a key solution to address deficiencies in ground communication network coverage and to improve the real-time transmission of services. Traditional satellite networks, originally employed for singular purposes such as data relay, are gradually transitioning to satellite internet to support various Internet-based services. The integration of satellites with ground networks, known as the Satellite-Terrestrial Integrated Network (STIN), has become an inevitable trend, making the deployment of ground stations (GSs) a critical issue in the STIN construction. Traditional GS deployment strategies are insufficient to meet the real-time demands of emerging services. In this context, a GS deployment strategy based on the data center-user gravity model (GSD-DG) is proposed, where the influence of all data center factors on GS deployment is considered. This approach takes into account constraints such as satellite connectivity, user traffic, and data center gravity. The integration of GS with data centers plays a pivotal role in enhancing the internet service latency performance. Simulation results indicate that the proposed strategy significantly reduces service latency by 24.5% compared to the benchmark, providing a more effective GS deployment solution to further optimize the STIN service latency performance.

Leading by the development of Starlink, the low earth orbit (LEO) satellite networks are expected to be the customer-grade Internet infrastructure that will co-work with the terrestrial networks for carrying Internet traffic. In this paper, focusing on network usage fluctuations that are caused by human activity variations in work and rest time slots, we introduce the concept of time zones into the global satellite network and develop a time-zone-aware traffic model. Accordingly, we propose a relay-based routing algorithm to balance the workload of the satellites and the inter-satellite links over different time zones. Simulation results show that the proposed model and algorithm can reduce the blocking ratio by up to 34.8% and increase the bandwidth utilization ratio by up to 45.6%, with a limited cost in average connection latency.

Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications’ latencies as the primary constraint, we model the problem of “VM placement and workload assignment” as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenter's resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

The development of reusable rockets makes it possible to launch a massive number of low earth orbit satellites for Internet access. These satellites are expected to be connected by inter-satellite links (ISLs) to provide global Internet service to end-users on the surface of the earth. From the infrastructure’s perspective, the ISLs can be implemented with lasers, forming the optical satellite networks. The design of terrestrial optical networks is usually traffic-driven, meaning more links/bandwidth are deployed to the traffic-intensive areas. But, the orbiting nature of satellites determines that laser ISLs are also moving at high velocities, making the presence of corresponding bandwidth loosed coupled with a specific area. As a result, the traffic-driven network design methodology will be inapplicable to the optical satellite networks. In this work, we model the optical satellite networks and define the laser ISL planning problem, Accordingly, we propose an ISL removal algorithm to remove parts of the ISLs from the grid-mesh topology to improve the average bandwidth utilization ratio (i.e., bandwidth efficiency) while maintaining networks’ reliability and availability. We conduct a simulation study based on the Starlink constellation to evaluate the grid-mesh topology and to investigate the proposed algorithm’s impact on the network performance. Results show that the bandwidth resource in the grid-mesh topology can hardly be used efficiently and removing certain ISLs properly can improve the bandwidth efficiency of optical satellite networks significantly. We also gain another interesting insight that maintaining too many ISLs will not improve, but degrade the network availability.