Network topology construction in this paper refers to designing the structural layouts and configuration rules among network devices according to natural language requirements in network simulation. Relatedly, Infrastructure as Code (IaC) enables the configuration and management of network devices through machine-readable code. Although there exist IaC generation approaches powered by Large Language Models (LLMs), they only focus on generating isolated configurations without consideration for holistic topology structure, leading to failure to form a complete, functional topology. Additionally, due to the LLMs’ limited knowledge of industry-specific device images, existing approaches struggle to adapt to diverse industry scenarios.In this paper, we introduce IntelliTopo, which, to the best of our knowledge, is the first IaC generation framework targeted at industrial network topology construction. Specifically, IntelliTopo enhances the capabilities of LLMs through two novel mechanisms: (1) Through semantic topology parsing, we enhance the LLMs’ understanding of the holistic topology structure; (2) Through domain-aware image retrieval, the outputs of IntelliTopo are more aligned with real-world industry scenarios. Deployed on our PaaS system, the IntelliTopo service has operated continuously for 3 months, handling 50+ network simulation tasks across 10+ industries. IntelliTopo reduces average network topology deployment time from days to hours while requiring less computational power for LLM reasoning. This work bridges the gap between high-level requirements and executable infrastructure, providing a scalable solution for network topology construction.

Recent studies reveal that Large Language Models (LLMs) exhibit strong sequential reasoning capabilities, allowing them to replace specialized time-series models and serve as foundation models for complex time-series analysis. To activate the capabilities of LLMs for time-series tasks, numerous studies have attempted to bridge the gap between time series and linguistics by aligning textual representations with time-series patterns. However, it is a non-trivial endeavor to losslessly capture the infinite time-domain variability using natural language, leading to suboptimal alignment performance. Beyond representation, contextual differences, where semantics in time series are conveyed by consecutive points, unlike in text by individual tokens, are often overlooked by existing methods. To address these, we propose S$^2$TS-LLM, a simple yet effective framework to repurpose LLMs for universal time series analysis through the following two main paradigms: (i) a spectral symbolization paradigm transforms time series into frequency-domain representations characterized by a fixed number of components and prominent amplitudes, which enables a limited set of symbols to effectively abstract key frequency features; (ii) a contextual segmentation paradigm partitions the sequence into blocks based on temporal patterns and reassigns positional encodings accordingly, thereby mitigating the structural mismatch between time series and natural language. Together, these paradigms bootstrap the LLMs' perception of temporal patterns and structures, effectively bridging time series and linguistics. Extensive experiments show that S$^2$TS-LLM can serve as a powerful time series analyzer, outperforming state-of-the-art methods across time series tasks.

Task assignment is a fundamental problem in spatial crowdsourcing. In many spatial crowdsourcing platforms, such as Didi, AMAP, and Uber, there are hybrid tasks, including real-time and reservation-type tasks, which are with different constraints and unevenly distributed in spatial and temporal. For these hybrid tasks, most existing studies suffer from low task completion rate and low profit for two reasons: firstly, they focus on homogeneous tasks with uniform constraints, and assign hybrid tasks separately; secondly, they cannot effectively address the uneven distribution of hybrid tasks. Inspired by this, we delve into the problem of online hybrid task assignment (HyTAO) with the goal of maximizing total revenue by simultaneously assigning both real-time and reservation-type tasks online for the first time. We prove the NP-hardness of the offline version of the HyTAO problem. To solve HyTAO effectively, we utilize a cross-platform cooperation model to tackle the challenge of non-uniform distribution. Following this, we design a binary tree-based search algorithm, namely BTS, which is capable of uniformly processing various types of tasks and quickly searching for available workers. Additionally, we discuss the parallel optimization strategies of BTS. To further enhance performance, we develop TBTS, which identifies tasks with high increased revenue based on a threshold. Finally, we conduct a comprehensive analysis of the complexity and competitive ratio of both BTS and TBTS. Extensive experiments are performed to demonstrate the efficiency of our approaches.

Route travel time estimation (RTTE) is crucial in intelligent transportation systems. Performing aggregation is a fundamental operation in RTTE and is widely used in the traffic prediction and route calculation stages. Observations have revealed that aggregation operations in RTTE are influenced by the road network structure and aggregation requests, resulting in irregular data access, redundant processing, and workload imbalance. Existing architectures have not addressed these issues effectively. In this study, we begin by characterizing the execution pattern of performing aggregation operations on an Intel Core CPU. Guided by these characterizations, we propose an aggregation accelerator that utilizes a hierarchical reduction architecture (HRA) to perform aggregations in RTTE efficiently. Specifically, we construct an inverted table based on the road network and aggregation requests. Building upon the concept of hierarchical reduction, we design an HRA to accelerate aggregation operations which reduces irregular data access and eliminates redundant processing. Additionally, we introduce a reconfiguration mode for HRA to mitigate workload imbalance issues. Compared to a benchmark method executed on an Intel Core CPU, our design achieves an average$10\times$speedup.

In this article, we investigate code-oriented partitioning computation offloading strategy for multiple user equipments (UEs) and multiple mobile edge computing servers with limited resources (i.e., limited computing power and waiting task queues with finite capacity). This article aims to develop an offloading strategy to decide the execution location, CPU frequency, and transmission power for UE while minimizing the execution overhead (i.e., a weighted sum of energy consumption and computational time) of UE's applications, which is an NP-hard problem. To achieve the objective, first, we transform the problem into a convex optimization problem and find the optimal solution. Second, we propose a decentralized computation offloading strategy (DCOS) algorithm for UE, and define a dictionary data structure for recording the strategy of the UE to reduce the algorithm complexity. Finally, the effectiveness of DCOS, and the impact of various key parameters on the strategy and overhead are demonstrated by simulation experiments.

Crowdsensing is a popular method that leverages a crowd of sensor users to collect data. For many crowdsensing applications, the collected raw data need to be preprocessed before further analysis, and the preprocessing work is mainly done by the crowdsourcer. However, as the amount of collected data increases, this type of preprocessing approach has many disadvantages. In this article, we construct monetary-based incentive mechanisms to motivate users to preprocess the collected raw data for the crowdsourcer. For two common crowdsensing scenarios, we propose two system models, which are the single-task-multiple-participants (STMP) model and the multiple-tasks-multiple-participants (MTMP) model. In the STMP model, we design an incentive mechanism based on game theory and prove that there is a Nash equilibrium. In the MTMP model, we develop an incentive mechanism based on an auction and demonstrate that the incentive mechanism has the desirable properties of truthfulness, individual rationality, profitability, and computational efficiency. Furthermore, the utility maximization problems of the crowdsourcer and users are simultaneously considered in our incentive mechanisms. Through theoretical analysis and extensive experiments, we evaluate the performance of our incentive mechanisms.