Low-orbit mega-constellations promote the arrival of the era of the Internet of Things (IoT) in 6G, and satellite computing power networks (SCPN) provide a resilient resource for services in IoT. Satellite resources are scarce due to volume and power constraints, forcing most tasks to be processed on the ground and hindering SCPNs practical application. To meet the elastic resource requirements of IoT, SCPN requires that each satellite node share its resources with others. However, the closedness of traditional satellite architecture cannot meet this requirement. Given that, this paper introduces a novel efficient satellite hardware architecture designed for accelerating SCPN named Efficient Satellite Hardware Architecture (ESHA), which is capable of fast and flexible satellite resource sharing. Through reorganizing the satellite system, ESHA enables the satellites to share resources among modules to aggregate resources for tasks in IoT. Besides, this paper presents a queuing network method to model and analyze ESHA and evaluate its performance. The experimental results show that, for the same type of parallel tasks, the speedup ratio of this architecture is 2.4 times higher than that of the traditional satellite architecture, the task completion time is reduced by 48%, and the utilization of satellite resources is effectively improved.

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet their application to graph structure analysis, particularly in community search, remains underexplored. Community search, a fundamental task in graph analysis, aims to identify groups of nodes with dense interconnections, which is crucial for understanding the macroscopic structure of graphs. In this paper, we propose GraphCS, a comprehensive benchmark designed to evaluate the performance of LLMs in community search tasks. Our experiments reveal that while LLMs exhibit preliminary potential, they frequently fail to return meaningful results and suffer from output bias. To address these limitations, we introduce CS-Agent, a dual-agent collaborative framework to enhance LLM-based community search. CS-Agent leverages the complementary strengths of two LLMs acting as Solver and Validator. Through iterative feedback and refinement, CS-Agent dynamically refines initial results without fine-tuning or additional training. After the multi-round dialogue, Decider module selects the optimal community. Extensive experiments demonstrate that CS-Agent significantly improves the quality and stability of identified communities compared to baseline methods. To our knowledge, this is the first work to apply LLMs to community search, bridging the gap between LLMs and graph analysis while providing a robust and adaptive solution for real-world applications.

With the rapid development of the Internet of Things (IoT), data centers are facing growing performance demands for flow deadline requirements, other to the low latency and high throughput. To address congestion issues that affect the performance of data center networks, this paper proposes D2C4, a deadline-aware data-credit coupled congestion control scheme. By tightly coupled data-credit integration using an ECN-based feedback loop, D2C4 minimizes credit waste while a credit-driven scheduler meets IoT applications’ flow deadlines. The key innovations of D2C4 include: 1) Data-credit coupling for performance gains; 2) ECN-based credit rate control reducing waste; 3) Deadline-sensitive credit-based flow scheduling to meet flow deadline demands. We conduct extensive experiments of D2C4 through small-scale DPDK-based tests and large-scale OMNeT++ simulations. Experimental results show that D2C4 outperforms existing protocols in flow completion time, throughput, deadline miss ratio, and packet drop.

Shortest path query on road networks is a fundamental problem to support many location-based services and wide variant applications. Contraction Hierarchies(CH) is widely adopted to accelerate the shortest path query by leveraging shortcuts among vertices. However, the state-of-the-art CH construction method named$\mathsf{VCHCons}$suffers from inefficiencies due to their strong reliance on pre-determined vertex order. This leads to the generation of a large number of invalid shortcuts and the limit of parallel processing capability. Motivated by it, in this paper, an innovative CH construction algorithm called$\mathsf{ECHCons}$is devised following an edge-centric paradigm, which addresses the issue of invalid shortcut production by introducing a novel edge-ordering strategy. Furthermore, it optimizes shortcut calculation within a dynamically constructed optimal subgraph, which is significantly smaller than the original network, thus shrinking the traversal space during index construction. To further enhance efficiency and overcome the limitations in parallelism inherent to$\mathsf{VCHCons}$, our approach leverages batch contraction of edges and introduces a well-defined lower bound technique to unlock more efficient parallel computation resources. Our approach provides both theoretical guarantee and practical advancement in CH construction. Extensive and comprehensive experiments are conducted on real road networks. The experimental results demonstrate the effectiveness and efficiency of our proposed approach.

Congestion control is crucial for the overall performance of data center networks and still faces considerable challenges. Recently, credit-driven congestion control has been emerging to enable precise flow control for current high-speed and highly dynamic data centers. However, existing credit-driven methods essentially separate credit and data packets, i.e., credits can fully regulate data packets, but they receive little feedback from the data packets. Accordingly, these approaches inevitably struggle with lossy credits and impaired throughput. To address the issue, we present data-credit-coupling congestion control named DC4. For a better understanding of the relationship between data and credit, we revisit the principle of credit-based congestion control and make the first attempt to explore the art of presenting the data-credit plane architecture. Based on the proposed data-credit framework, DC4 transforms the interaction between credit and data packets from one-way control to two-way coordination to achieve mutual benefits and dynamic balances between the credit and data packets. We conduct extensive experiments to evaluate the performance of our design and compare it with state-of-the-art protocols, including HPCC, ExpressPass, and Aeolus. Experimental results show that DC4 outperforms data-credit-separated approaches in terms of the flow completion time, throughput, and credit waste.

Congestion control is crucial to the overall performance of datacenter networks and still faces great challenges, especially when network traffic exhibits complicated and time-varying patterns, from long-running flows to burst short-lived flows. Recently congestion control techniques based on in-network-telemetry (INT) are emerging as the promising approach to enable precise flow control. Existing INT-based method mainly relies on receiver ACK packets to transfer INT data and acquire link load information. Their control precision in term of response time, however, is still beyond one round-trip time (RTT), since the receiver ACK feedback needs at least one RTT and the sender cannot obtain INT data of one flow within the first RTT. In this paper, we make the first attempt to explore INT-based techniques and break the one-RTT barrier efficiently. We come up with an ultra-precise and efficient congestion control algorithm, called UECC. UECC utilizes both switch-based and host-based INT simultaneously, and realizes accurate, timely and low-overhead flow control. We tackle the challenging issues of cooperation between the two types of INT and the additional overhead introduced by confirmation packets, and conduct extensive experiments to evaluate the performance of our design. The results show that UECC achieves 42% reduction in average queue length compared to the state-of-the-art INT-based method, and 5% reduction in 95th-percentile flow completion time.

Proactive transports nowadays have drawn much attention because of fast convergence, near-zero queueing and low latency. Proactive protocols, however, need an extra RTT to allocate ideal sending rate for new flows. To solve this, some studies, such as pHost, Homa, send unscheduled packets with line rate in the first RTT, which will causes severe network congestion. To avoid the queue buildup, Aeolus directly drops unscheduled packets when congestion occurs. Nevertheless, based on our experiment, a considerable part of small flows (0-100 KB) will be completed in the first RTT under 100 Gbps network, so that dropping unscheduled packets will severely affect performance of the small flows. In this paper we propose SSP, a new scheme aimed to eliminate the extra RTT delay and improve the flow completion time (FCT) of small flows under the proactive mechanism. Like pHost and Homa, SSP sends unscheduled packets at line rate when new flow arrives. Different from Aeolus, SSP selectively drops scheduled packets once queue buildup happens in the switch, thus protecting unscheduled packets which are more likely belong to small flows. Besides, based on the short-job-first (SJF) principle, we give relative higher priorities for small flows at the sender. Our simulation results with realistic workloads show that SSP can improve the FCT of small flows significantly. Specifically, under Web Search workload, SSP facilitates nearly 63% of 0-100 KB flows to complete one RTT faster. Also, SSP reduces the tail FCT by 56.8% at the 99th percentile compared with Expresspass and 29.2% compared with Aeolus while not leads to large queue buildup.

Recent proposals have leveraged emerging credit-based proactive transports to achieve high throughput low latency datacenter network transports. Particularly, those transports that employ hop-by-hop credits have the merits of fast convergence, low buffer occupancy, and strong congestion avoidability. However, they fairly transmit long flows and latency-sensitive short flows, which will cause the transmission latency of short flows and the average flow completion time increased. Although flow scheduling mechanisms have studied extensively to accelerate short flow transmission, they are hard to be directly applied in credit-based transports. The root cause is that most traditional flow scheduling mechanisms mainly work in the long queue containing flows in various sizes, while credit-based proactive transports maintain the extremely short bounded queue, near zero. Based on this observation, this paper makes the first attempt to accelerate short-flow scheduling in credit-based proactive transport, and proposed FastCredit. FastCredit can be used as a general building block to expedite short flows in credit-based proactive transports. In FastCredit, we schedule credit transmission at both receivers and switches to indirectly perform flow scheduling, and develop a mechanism to mitigate credit waste and improve network goodput. Compared to the state-of-the-art credit-based transport protocol, FastCredit reduces average flow completion time to 0.78x and greatly improves the short flow transmission latency to 0.51x in realistic workloads. Especially, FastCredit reduces average flow completion time to 0.76x under incast circumstances and 0.62x in many-to-one traffic mode. Furthermore, FastCredit still maintains the advantages of short queue and high throughput.

Bursty traffic and thousands of concurrent flows incur inevitable congestion in data center networks (DCNs) and then affect the overall performance. Various transport protocols are developed to mitigate the network congestion, including reactive and proactive protocols. Reactive schemes to handling congestion after congestion arises are common to current DCNs. However, with the growth of scale and link speed, reactive schemes such as DCTCP encounter the significant problem of slow responding to congestion. On the contrary, proactive protocols are designed to avoid congestion, and they have the advantages of zero data loss, fast convergence and low buffer occupancy (e.g., credit-reservation protocols). But in actual deployment scenario, it is hard to guarantee one protocol to be deployed in every server at one time. When credit-reservation protocol is deployed to DCNs step-by-step, the network is converted to multi-protocol state and faces the following fundamental challenges: (i) unfairness, (ii) high bu er occupancy, and (iii) heavy tail delay. Therefore, we propose EC4, which is for converging ECN-based and credit-reservation protocols with minimal modification. To the best of our knowledge, EC4is the first to address how to harmonize proactive and reactive congestion control. Targeting the common ECN-based protocol-DCTCP, EC4leverages the Forward Explicit Congestion Notification (FECN) to deliver realtime congestion information and redefines feedback control. After evaluation, the results show that EC4e ectively addresses the unfair link allocation. Furthermore, even workloads at 0.6 does not cause buffer overflow, thus largely eliminating the timeouts problem.

Endpoint congestion is a bottleneck in high-performance computing (HPC) networks and severely impacts system performance, especially for latency-sensitive applications. For long messages (or flows) whose duration is far larger than the round-trip time (RTT), endpoint congestion can be effectively mitigated by proactive or reactive counter-measures such that the injection rate of each source is dynamically controlled to a proper level. However, many HPC applications produce a hybrid traffic, a mix of short and long messages, and are dominated by short messages. Existing proactive congestion avoidance methods face the great challenge of scheduling the rapidly changing traffic pattern caused by these short messages. In this paper, we leverage the advantages of proactive and reactive congestion avoidance techniques and propose the Packet-chaining Reservation Protocol (PCRP) to make a dynamic balance between flows following proactive scheduling and packets subjected to reactive network conditions. We select the chaining packets as a flexible reservation granularity between the whole flow and one packet. We allow small flows to be speculatively transmitted without being discarded and give them higher priority over the entire network. Our PCRP can respond quickly to network conditions and effectively avoid the formation of endpoint congestion and reduce the average flow delay. We conduct extensive experiments to evaluate our PCRP and compare it with the state-of-the-art proactive reservation-based protocols, Speculative Reservation Protocol (SRP) and Bilateral Flow Reservation Protocol (BFRP). The simulation results demonstrate that in our design the flow latency can be reduced by 50.2% for hotspot traffic and 28.38% for uniform traffic.