Serverless computing is a popular cloud computing paradigm that enables developers to build applications at the function level, known as serverless applications. The Serverless Application Model (AWS SAM) is the most widely adopted configuration schema. However, misconfigurations pose a significant challenge due to the complexity of serverless configurations and the limitations of traditional data-driven techniques. Recent advancements in Large Language Models (LLMs), pre-trained on large-scale public data, offer promising potential for identifying and explaining misconfigurations. In this article, we present SlsDetector , the first framework that harnesses the capabilities of LLMs to perform static misconfiguration detection in serverless applications. SlsDetector utilizes effective prompt engineering with zero-shot prompting to identify configuration issues. It designs multi-dimensional constraints aligned with serverless configuration characteristics and leverages the Chain of Thought technique to enhance LLM inferences, alongside generating structured responses. We evaluate SlsDetector on a curated dataset of 110 configuration files, which includes correct configurations, real-world misconfigurations, and intentionally injected errors. Our results show that SlsDetector , based on ChatGPT-4o (one of the most representative LLMs), achieves a precision of 72.88%, recall of 88.18%, and F1-score of 79.75%, outperforming state-of-the-art data-driven methods by 53.82, 17.40, and 49.72 percentage points, respectively. We further investigate the generalization capability of SlsDetector across recent LLMs, including Llama 3.1 (405B) Instruct Turbo, Gemini 1.5 Pro, and DeepSeek V3, with consistently high effectiveness.

Existing tracking-by-detection Multi-Object Tracking methods mainly rely on associating objects with tracklets using motion and appearance features. However, variations in viewpoint and occlusions can result in discrepancies between the features of current objects and those of historical tracklets. To tackle these challenges, this paper proposes a novel Spatial-Temporal Tracklet Graph Matching paradigm (STAR). The core idea of STAR is to achieve long-term, reliable object association through the association of ``tracklet clips (TCs)". TCs are segments of confidently associated multi-object trajectories, which are linked through graph matching. Specifically, STAR initializes TCs using a Confident Initial Tracklet Generator (CITG) and constructs a TC graph via Tracklet Clip Graph Construction (TCGC). In TCGC, each object in a TC is treated as a vertex, with the appearance and local topology features encoded on the vertex. The vertices and edges of the TC graph are then updated through message propagation to capture higher-order features. Finally, a Tracklet Clip Graph Matching (TCGM) method is proposed to efficiently and accurately associate the TCs through graph matching. STAR is model-agnostic, allowing for seamless integration with existing methods to enhance their performance. Extensive experiments on diverse datasets, including MOTChallenge, DanceTrack, and VisDrone2021-MOT, demonstrate the robustness and versatility of STAR, significantly improving tracking performance under challenging conditions.

Serverless computing is a burgeoning cloud computing paradigm that allows developers to implement applications at the function level, known as serverless applications. Amazon Web Services (AWS), the leading provider in this field, offers Serverless Application Model (AWS SAM), a widely adopted configuration schema for configuring functions and managing resources. However, misconfigurations pose a major challenge during serverless application development, and existing methods are not applicable. To our knowledge, the configuration characteristics and misconfiguration detection for serverless applications have not been well explored. To address this gap, we collect and analyze 733 real-world serverless application configuration files using AWS SAM to understand their characteristics and challenges. Based on the insights, we designPHOENIX, a misconfiguration detection approach for serverless computing.PHOENIXlearns configuration patterns from uniform representations of configurations and identifies potential misconfigurations that deviate from these patterns. To evaluatePHOENIX, we construct a dataset comprising 35 injected misconfigurations and 70 real-world misconfigurations with confirmed causes. Our results show thatPHOENIXdetects 100% of the injected misconfigurations and identifies 97.14% of real-world misconfigurations, significantly outperforming the state-of-the-art tool.

Serverless computing is a popular cloud computing paradigm that has found widespread adoption across various online workloads. It allows software engineers to develop cloud applications as a set of functions (called serverless functions ). However, accurately measuring the performance (i.e., end-to-end response latency) of serverless functions is challenging due to the highly dynamic nature of the environment in which they run. To tackle this problem, a potential solution is to apply checks of performance testing techniques to determine how many repetitions of a given serverless function across a range of inputs are needed to cater to the performance fluctuation. However, the available literature lacks performance testing approaches designed explicitly for serverless computing. In this article, we propose the first serverless computing-oriented performance testing (SCOPE) approach. SCOPE takes into account the unique performance characteristics of serverless functions, such as their short execution durations and on-demand triggering. As such, SCOPE is designed as a fine-grained analysis approach. SCOPE incorporates the accuracy check and the consistency check to obtain the accurate and reliable performance of serverless functions. The evaluation shows that SCOPE provides testing results with 97.25% accuracy, 33.83 percentage points higher than the best currently available technique. Moreover, the superiority of SCOPE over the state-of-the-art holds on all functions that we study.

When the edge between two nodes is not measured, is there any hint to know the edge property, and will the inferred edge property be useful? To answer these questions, this paper uniformly defines the properties of unmeasurable edges in generic graphs. For an unmeasurable edge$(i,j)$, it is called rangeable if its length is unique in any realization of the graph, rigid if the number of its possible lengths is finite, and flexible if it has infinite possible lengths. The rangeable edge can provide deterministic hidden knowledge as if the edge is measured. A condition for an unmeasured edge being rangeable in 2D space is firstly proposed, based on which a centralized identification algorithm (DRE) is designed. However, the centralized rangeable edge identification has the overhead of global information collection. Therefore distributed condition and algorithm to identify rangeable edges are further investigated. We prove that an unmeasurable edge$(i,j)$is rangeable if there are at least two Disjoint Minimally Rigid Branches (DMRBs) between$i$and$j$. The unmeasurable edge$(i,j)$is rigid and flexible when the number of DMRB is one and zero, respectively. A distributed Branching and Blacklisting (BB) algorithm is proposed to find DMRBs, so that rangeable edges are identified distributively. Then, the applications of rangeable, rigid, and flexible edges are discussed. Experimental evaluations show that the centralized and distributed algorithms can identify a rich set of unmeasurable but rangeable edges in distance graphs, even more than the number of directly measured edges. Moreover, BB has a similar identification performance as the centralized DRE algorithm and outperforms existing distributed unmeasurable edge inference algorithms significantly.

Ranging-based localization is a fundamental problem in the Internet of Things and unmanned aerial vehicle networks. However, the nodes’ limited-ranging scope and users’ broad coverage purpose inevitably cause network sparsity or subnetwork sparsity. The performances of existing localization algorithms are extremely unsatisfactory in sparse networks. A crucial way to deal with the sparsity is to exploit the hidden knowledge provided by the unmeasured edges, which inspires this work to propose a hypothesis-based Joint Edge Inference and Localization algorithm called InferLoc . InferLoc mines the Unmeasured but Inferable Edges (UIEs). Each UIE is an unmeasured edge, but it is restricted through other edges in the network to be inside a rigid component, so it has only a limited number of possible lengths. We propose an efficient method to detect UIEs and geometric approaches to infer possible lengths for UIEs in 2D and 3D networks. The inferred possible lengths of UIEs are then treated as multiple hypotheses to determine the node locations and the lengths of UIEs simultaneously through a joint graph optimization process. In the joint graph optimization model, to make the 0/1 decision variables for hypotheses selection differentiable, differentiable functions are proposed to relax the 0/1 selections, and rounding is applied to select the final length after the optimization converges. We also prove the condition when a UIE can contribute to sparse localization. Extensive experiments show remarkably better accuracy and efficiency performances of InferLoc than the state-of-the-art network localization algorithms. In particular, it reduces the localization errors by more than 90% and speeds up the convergence time more than 100 times than that of the widely used G2O-based methods in sparse networks.

The barycentric linear localization (BLL) methods provide a lightweight, distributed way to calculate locations for resource-limited IoT devices. A crucial requirement for BLL is that the nodes participating in the iterative location propagation are localizable. Otherwise, the unlocalizable nodes will continuously pose error information in the location propagation process, making even the theoretically localizable nodes converge to the wrong locations. However, the research on node localizability in BLL is much lacked, greatly limiting the application scope of BLL. In specific, BLL node localizability is detected on a generated graph$\mathcal {G^{A}}$. For any node, its neighbors appear in$\mathcal {G^{A}}$only when the neighbors can form triangle(s), so that$\mathcal {G^{A}}$is much sparser than the original$\mathcal G$. Thus, the node localizability condition in BLL is harder to be satisfied than that in traditional localization methods. Moreover, the distributed algorithm to detect BLL localizable nodes is still open. This paper thoroughly investigates the node localizability conditions and distributed localizable node detection algorithms in BLL. At first, an efficient and fully distributed Negative Edge Inference (NEI) algorithm is proposed for each node to infer implicit edges in its neighborhood. NEI strengthens the distance graph by revealing more distance constraints so that enables more neighboring triangles. Then a new sufficient condition, i.e., the recursive three disjoint path condition (Recursive-3DP) on the strengthened distance graph is proposed to identify BLL localizable nodes much more accurately. Secondly, a distributed Path Extension and Pruning (PEP) algorithm is proposed for distributed localizable node detection. PEP is proved to detect all the theoretically Recursive-3DP nodes in the strengthened distance graph. A Fast-PEP algorithm is further proposed, which misses very limited Recursive-3DP nodes while bringing significant improvement in efficiency. PEP and Fast-PEP guarantee to identify BLL localizable nodes in$2H$rounds, where$H$is the maximum hop number of the node disjoint paths. Finally, by using NEI and PEP (Fast-PEP), a localizability-aware BLL (LABEL) method is proposed, which correctly identifies localizable nodes and guarantees their correct location convergence. Extensive analysis and experiments show the advantages in localizability and location accuracy of the proposed schemes over the state-of-the-art methods.

Determining whether nodes can be uniquely localized, called localizability detection, is a concomitant problem in network localization. Localizability detection under the traditional Non-Linear Localization (NLL) schema has been well explored, whereas localizability under the emerging Barycentric coordinate-based Linear Localization (BLL) schema has not been well investigated. Non-awareness of the node localizability in BLL may cause theoretically localizable nodes to converge to wrong locations because their locations are impacted by the wrong locations of the unlocalizable nodes through the iterative location propagation. In this article, the deficiency of existing localizability theories and algorithms in BLL is firstly investigated and then a necessary condition and a sufficient condition for BLL node localizability detection are proposed. Based on these two conditions, an efficient Iterative Maximum Flow (IMF) algorithm is designed to identify BLL localizable nodes, and only localizable nodes are selected to enable a Localizability Aware Barycentric Linear Localization (LABLL) algorithm, which can guarantee the locations of the localizable nodes converging correctly. The proposed IMF and LABLL algorithms are validated by both theoretical analysis and experimental evaluations.

Partitioning the Maximal Redundant Rigid Components (MRRC) and Maximal Global Rigid Components (MGRC) in generic 2D graphs are critical problem for network structure analysis, network localizability detection, and localization algorithm design. This article presents efficient algorithms to partition MRRCs and MGRCs and develops an open-sourced toolbox, GPART, for these algorithms to be conveniently used by the society. We firstly propose conditions and an efficient algorithm to merge the over-constrained regions to form the maximal redundant rigid components (MRRC). The detected MRRCs are proved to be maximal and all the MRRCs are guaranteed to be detected. The time to merge the over-constrained regions is linear to the number of nodes in the over-constrained components. To detect MGRCs, the critical problem is to decompose 3-connected components in each MRRC. We exploit SPQR-tree based method and design a local optimization algorithm, called MGRC_acce to prune the unnecessary decomposition operations so that the SPQR-tree functions can be called much less number of times. We prove the MGRCs can be detected inside MRRCs using at most O(mn ) time. Then a GPART toolbox is developed and extensively tested in graphs of different densities. We show the proposed MRRC and MGRC detection algorithms are valid and MGRC_acce greatly outperforms the direct SPQR-tree based decomposition algorithm. GPART is outsourced at https://github.com/inlab-group/gpart .

Inferring network topology via inter-node distance measurements is an important problem. It is challenging when the distance measurements are sparse because the lack of edge constraints may lead to ambiguous realizations that differ greatly from the ground truth. The flipping ambiguities are caused by binary vertex cut sets in 2D and triple vertex cut sets in 3D, which are calledseparators. This paper investigates conditions on whether the flipping ambiguities caused by these separators can be disambiguated using neighborhood, full graph, and component-level conditions. Accordingly, local flipping-free condition (LFFC), global flipping-free condition (GFFC), and component-based flipping free condition (CFFC) are proposed. Then a disambiguating framework based on a combinatorial application of these conditions is proposed. It detects separators and first disambiguate separators locally by LFFC, which converts the graph to a binary tree, whose leaf nodes are flipping-free components and edges are LFFC unsolvable separators. Then the CFFC condition is further applied to disambiguate LFFC unsolvable separators between components. If$k$and$g$separators are disambiguated by LFFC and CFFC respectively, the number of ambiguous solutions for network localization will be reduced by${2^{k+g}}$times. Finally, the flipping-free components realize node coordinates in their local coordinate systems and a residue-based weighted component stitching algorithm (RWCS) is proposed to iteratively synchronize components’ local coordinates to generate global coordinates of the network. Extensive simulations show the LFFC, CFFC and RWCS frameworks are efficient, which resolve a major portion of flipping ambiguities and greatly improve the localization accuracy than the state of art algorithms in various sparse network settings.

Inferring nodes' locations by inter-node measurements is a crucial problem in the IoT era. Despite the various approaches to this problem, obtaining accurate results is still challenging when the measurements are noisy, sparse, or uneven. Such unsatisfactory measurements are, however, inevitable for the general consideration of the deployment cost and the limited sensing scope.This paper proposes a Hierarchical Graph Optimization (HGO) framework to address the network localization problem when the measurements are sparse and noisy. It firstly efficiently extracts the dense sub-graphs and realizes their local structures in local coordinate systems. The local structures of dense components are rather accurate for the local sufficiency of the measurements. Then, the noises of the inter-edges that sparsely connect the dense sub-graphs are found as the main course of the network localization errors. A close-loop condition is derived and two denoising algorithms are proposed to set up linear equation arrays to correct the noises of these critical edges. After that, a projection algorithm is proposed to realize a smoothed backbone graph using the corrected critical edges, and finally, a hierarchical registration method is proposed to register the realized backbone and the dense sub-components to produce the global network structure. A parallel implementation is further developed, which speeds up HGO in large scale networks. Extensive simulations verify that HGO consistently outperforms existing network localization algorithms in terms of accuracy, efficiency, and reliability under various measurement settings.

The boundary detection of continuous objects has become an important research challenge in Wireless Sensor Networks (WSNs), where improving the accuracy of boundary and reducing the energy consumption are the primary factors to be considered. To address this challenge, this article proposes a tow-stage boundary area detection scheme in duty-cycled WSNs, and sensor nodes are deployed in a dense fashion. Experimental evaluation result shows that the refinement procedure can refine the boundary area, where half of the initial boundary faces area should be reduced in most situations.