With the rapid development of geolocation technology, the volume of spatio-temporal trajectory data has surged. This data is widely used in fields such as geographic information systems and mobile computing, but its storage and query processing present significant challenges. Current methods of offline indexing are inefficient and cannot be updated in real-time. To address this issue, this paper proposes a concept of the online index that supports real-time storage and indexing of trajectory data and significantly reduces indexing time and storage space requirements. Based on this concept, two vector-based online trajectory indexing methods are proposed in this paper. The first is an online trajectory indexing method based on vector extraction (VBIndex), which offers the advantages of high efficiency and less storage space. The second is an online trajectory indexing method based on road-network matching (RAIndex), which further improves the vector-based indexing efficiency when road network involved. Through experiments with real datasets, the proposed algorithms were evaluated, confirming their superiority in terms of indexing construction time and storage space. Furthermore, we have theoretically proven that queries based on this index are accurate, and statistical analysis is feasible. Both algorithms have a time complexity of$O(N)$in indexing construction, demonstrating good performance.

As mobile edge computing technology advances rapidly, latency-sensitive and resource-intensive applications are being offloaded to edge servers to enhance Quality of Service (QoS) for users. Traditional monolithic architectures, however, struggle to meet the escalating service and traffic requirements of distributed users due to their inherent inflexibility. In response to these challenges, microservices architecture, characterized by scalability and flexibility, has been adopted for dynamic deployment at the network edge. However, the deployment of these lightweight, dependency-rich components in a way that minimally impacts the makespan and maximizes quality of service is complex. Current studies often overlook the deployment of microservices with specific dependencies within constrained environments of edge server clusters and communication links. This paper introduces practical and effective strategies for the deployment and updating of microservices, tailored to various application contexts. Initially, two scenarios are analyzed: one constrained by bandwidth with unlimited storage, and the other by storage with unlimited bandwidth. For each scenario, optimal solutions are developed using a novel enhanced graph construction method. The study progresses to a more intricate scenario involving comprehensive constraints on storage, computation, and communication resources. An optimized deployment method is proposed, utilizing main path embedding followed by an innovative simulated annealing algorithm for iterative refinement. This method is validated by demonstrating that the main path coincides with the critical path. Furthermore, the dynamic reallocation of edge resources is explored through a critical path-based updating algorithm that optimizes microservice locations to reduce overall makespan. Extensive experiments demonstrate that our strategies outperform existing representative benchmark approaches in terms of overall performance and microservice deployment efficiency.

Microservices have become an important design paradigm for large-scale distributed systems, offering flexible provisioning options. A fundamental challenge is the exponential growth of the solution space with the number of user requests, posing challenges to efficient provisioning and scheduling when aiming to balance cost and latency under resource constraints in large-scale dynamic edge environments. To tackle this problem, we formulate a joint optimization model for microservice provisioning and routing that integrates cost efficiency and latency reduction while accounting for uncertainties in the origin location of requests. To establish a unified framework that facilitates decision-making, we propose an integer linear programming (ILP) model that captures the dependencies between microservices in the service chain. Our Scalable optimization framework with Cost-efficiency and Latency reduction (SoCL) comprises three stages: an initial partitioning guarantees latency bounds, a pre-provisioning stage considers provisioning cost, and a multi-scale combination stage balances cost and latency through parallel and serial local search. Extensive experiments conducted across diverse scenarios based on a commonly used dataset demonstrate that the proposed SoCL framework significantly increases cost efficiency and decreases latency compared to established baselines, while reducing execution time up to one order of magnitude compared to obtaining the optimal solution by optimizer.

Content delivery in vehicular edge networks faces critical challenges due to dynamic user mobility, unpredictable content request patterns, and limited storage at edge nodes. To tackle these problems, we propose a distributed online framework that jointly performs proactive caching at roadside units (RSUs) and hierarchical prefetching from the cloud to macro base stations (MBSs), enabling real-time adaptation to spatiotemporal variations in content demand across different time scales. Our goal is to minimize content transmission latency while satisfying system-wide resource and cost constraints. The proposed Vehicular-based Online Proactive caching and Prefetching (VOPP), integrates trajectory-based user mobility prediction with future content demand estimation to guide online distributed caching. At the RSU level, we formulate a distributed online convex optimization model with fine-grained gradient updates and inter-agent coordination based on real-time mobility patterns. At the MBS level, we construct a multi-step predicted content set using user mobility and request forecasts, and define a value density metric that combines popularity and delay reduction. On both levels, additional subsequent refinement steps ensure high-quality caching decisions. Extensive simulations based on a real-world GPS dataset of 10,357 taxi trajectories in Beijing demonstrate that VOPP significantly reduces transmission delay and achieves robust performance across diverse mobility patterns and user densities, outperforming baseline methods.

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents, such as data breaches, are on the rise. Several inherent attributes of cloud environments, including data sharing, remote access, dynamic scalability, and scalability, pose significant challenges for the protection of cloud security. Even more concerning is the growing threat of Advanced Persistent Threats (APTs), which have become increasingly sophisticated and stealthy. As a more complex form of multi-stage attacks (MSAs), APTs follow a multi-step process that spreads malicious actions across different stages and blends them with legitimate operations, making intrusion detection particularly challenging. In this paper, we conduct an empirical study on the multi-stage characteristics of APT attacks specifically within cloud environments. Drawing from real-world attack scenarios, we analyze the behavioral patterns of APT attacks, evaluate the existing countermeasures, and identify ongoing challenges in defending against them. Our findings expose the limitations of conventional intrusion detection approaches and highlight the critical need for fine-grained and behavior-aware security mechanisms specifically designed for cloud environments. This study provides a deeper understanding of how APTs adapt their tactics to exploit cloud-specific vulnerabilities and offers insights for improving threat detection and response in modern cloud infrastructures.

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper investigates optimizing the service caching and updating problem within multi-stage and proposes a novel framework with three proposed strategies for the different stages to jointly optimize the delay and cost. At the initial service caching stage, we propose a basic caching strategy based on dynamic programming for the single-area scenario, taking into account the constraint of limited memory resources. To improve the caching strategy, we extend our consideration to the multiple-area scenario and design an improved algorithm based on tabu search. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on reinforcement learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

In cloud data centers, the exponential growth of data places increasing demands on computing, storage, and network resources, especially in multi-tenant environments. While this growth is crucial for ensuring Quality of Service (QoS), it also introduces challenges such as fluctuating resource requirements and static container configurations, which can lead to resource underutilization and high energy consumption. This article addresses online resource provisioning and efficient scheduling for multi-tenant environments, aiming to minimize energy consumption while balancing elasticity and QoS requirements. To address this, we propose a novel optimization framework that reformulates the resource provisioning problem into a more manageable form. By reducing the original multi-constraint optimization to a container placement problem, we apply the interior-point barrier method to simplify the optimization, integrating constraints directly into the objective function for efficient computation. We also introduce elasticity as a key parameter to balance energy consumption with autonomous resource scaling, ensuring that resource consolidation does not compromise system flexibility. The proposed Energy-Efficient and Elastic Resource Provisioning (EEP) framework comprises three main modules: a distributed resource management module that employs vertical partitioning and dynamic leader election for adaptive resource allocation; a prediction module using$\omega$-step prediction for accurate resource demand forecasting; and an elastic scheduling module that dynamically adjusts to tenant scaling needs, optimizing resource allocation and minimizing energy consumption. Extensive experiments across diverse cloud scenarios demonstrate that the EEP framework significantly improves energy efficiency and resource utilization compared to established baselines, supporting sustainable cloud management practices.

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

ASCII art detection and recognition is an important branch of current network information processing. However, due to ASCII art's text-based organization and image-based semantic expression, traditional natural language processing (NLP) and image recognition fail to yield ideal results. This paper designs an ASCII art localization and extraction algorithm based on string distance for highly mixed text and ASCII art, aiming to segment clean ASCII art for subsequent recognition. Additionally, an evaluation standard for ASCII art extraction effectiveness is defined. Experimental results show that the proposed algorithm performs well in locating and extracting ASCII art.

The vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm that provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.

Edge computing has emerged as a promising paradigm to fulfill the escalating demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement have become imperative to optimize performance and increase platform profit. In this paper, we investigate the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators (MNOs) while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. Then, we propose a novel two-stage method to decouple the problem, breaking down server deployment and service placement into two distinct yet interconnected stages. In stage I, the server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP), where the state space, action space, and penalty function are defined to effectively model the issue. We propose the SDQ algorithm to establish a relatively stable server deployment strategy. In stage II, the service placement is formulated as a constrained integer linear programming problem. We propose the SPIB-TDB algorithm to optimize service placement. Extensive experimentation validates the exceptional performance of our proposed algorithms in enhancing the profit of MNOs.

In the context of mobile edge computing, efficiently deploying microservices to reduce finish time and enhance user service quality is a challenging task. However, existing research still has certain deficiencies in considering microservice deployment within edge server clusters and communication link constraints. To address this issue, we propose three microservice deployment strategies by offering flexibility and adaptability for various application scenarios. We initially consider two straightforward scenarios: one with unlimited storage resources under the bandwidth constraint, and the other with unlimited bandwidth resources under the storage constraint. For each of these two scenarios, we introduce a novel enhanced graph construction method and design two optimal solutions. For Scenario 3, which involves complex constraints on server capacity, computational capability, and communication resources, we present an optimization method based on main path partitioning and the simulated annealing algorithm. We effectively tackle challenges arising from server capacity, computational capability, and communication resource limitations. Across multiple experimental setups, our approach significantly improves microservice deployment efficiency and overall performance compared to traditional strategies.

Abstract Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay‐sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited‐processor fog nodes (UPFN) and limited‐processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP‐hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near‐optimal solution bounded by has been found. m is the number of fog nodes, and is the upper bound on the Lipschitz constant of the delay function. Another one is the application‐driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms.

The vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm which provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.

Parallel fuzzing relies on hardware resources to guarantee test throughput and efficiency. In industrial practice, it is well known that parallel fuzzing faces the challenge of task division, but most works neglect the important process of corpus allocation. In this paper, we proposed a group-based corpus scheduling strategy to address these two issues, which has been accepted by the LLVM community. And we implement a parallel fuzzer based on this strategy called glibFuzzer. glibFuzzer first groups the global corpus into different subsets and then assigns different energy scores and different scores to them. The energy scores were mainly determined by the seed size and the length of coverage information, and the difference score can describe the degree of difference in the code covered by different subsets of seeds. In each round of key local corpus construction, the master node selects high-quality seeds by combining the two scores to improve test efficiency and avoid task conflict. To prove the effectiveness of the strategy, we conducted an extensive evaluation on the real-world programs and FuzzBench. After 4×24 CPU-hours, glibFuzzer covered 22.02% more branches and executed 19.42 times more test cases than libFuzzer in 18 real-world programs. glibFuzzer showed an average branch coverage increase of 73.02%, 55.02%, 55.86% over AFL, PAFL, UniFuzz, respectively. More importantly, glibFuzzer found over 100 unique vulnerabilities.

Android kernel fuzzing is a research area of interest specifically for detecting kernel vulnerabilities which may allow attackers to obtain the root privilege. The number of Android mobile phones is increasing rapidly with the explosive growth of Android kernel drivers. Interface aware fuzzing is an effective technique to test the security of kernel driver. Existing researches rely on static analysis with kernel source code. However, in fact, there exist millions of Android mobile phones without public accessible source code. In this paper, we propose a hybrid interface recovery method for fuzzing kernels which can recover kernel driver interface no matter the source code is available or not. In white box condition, we employ a dynamic interface recover method that can automatically and completely identify the interface knowledge. In black box condition, we use reverse engineering to extract the key interface information and use similarity computation to infer argument types. We evaluate our hybrid algorithm on on 12 Android smartphones from 9 vendors. Empirical experimental results show that our method can effectively recover interface argument lists and find Android kernel bugs. In total, 31 vulnerabilities are reported in white and black box conditions. The vulnerabilities were responsibly disclosed to affected vendors and 9 of the reported vulnerabilities have been already assigned CVEs.

Fog computing is an emerging paradigm that brings the computing capabilities close to distributed IoT devices, which provides networking services between end devices and traditional cloud data centers. One important mission is to further reduce the monetary cost of fog resources while meeting the ever-growing demand of multiple users. In this paper, we focus on minimizing the total cost for multiple mobile users to provide an efficient resource provisioning scheme in fog computing. The total cost includes two aspects: the replication cost and the transmission cost. We consider two cases for the resource provision problem by focusing on different cost models. First, one simple case where users can only upload one replication is discussed, and an optimal solution is proposed by converting the original problem into one of bipartite graph matching. Then we consider a more complicated case that each user can upload multiple replications on fog nodes in the resource provisioning. For different transmission cost models, the transmission cost is related to the distance of each pair of fog nodes. This problem is proven to be NP-hard. We first propose a non-adaptive algorithm which is proved to be bounded by 2/3W+1/3OPT. Another 3+ε-approximation algorithm is proposed based on local search, which has better performance with higher complexity. Extensive simulations also prove the efficiency of our schemes.

Task resource allocation has always been an important issue in cloud-based data center networks (DCNs). This paper considers provisioning the maximum admissible load (MAL) of virtual machines (VMs) in physical machines (PMs) with underlying tree-structured DCNs using the hose model for communication. The limitation of static load distribution is that it assigns tasks to nodes in a once-and-for-all manner, and thus, requires a priori knowledge of program behavior. To avoid load redistribution during a run time where the load grows, we introduce maximum elasticity scheduling, which has the maximum growth potential subject to the node and link capacities. Given a tree-based topology, this paper aims to find the schedule with the maximum elasticity across both nodes and links. We have found a distributed linear solution based on message passing, and we discuss several extensions of the model. We conclude the paper by presenting various simulation results.

Recently, fog computing has become a highly virtualized platform that provides computation, storage, and networking services between end devices and traditional cloud data centers. In this paper, we address the resource provision (RP)problem for delay-sensitive users in cooperative fog computing. Our objective is to find a feasible provision scheme that minimizes the total monetary cost proportional to the number of fog nodes for network operators under the deadline and capacity constraints by considering the cooperation of fog nodes. We consider two cases of our RP problem: the Unlimited-Processor Fog Nodes (UPFN)case and the Limited-Processor Fog Nodes (LPFN)case. For the UPFN case, each fog node has unlimited processors. The requests on each fog node can be processed in parallel ideally, i.e. with no scheduling delay. The LPFN case corresponds to a more realistic scenario where the scheduling delay is non-eligible. In either case, our RP problem is proven to be NP-hard. For the UPFN case, we propose two greedy algorithms which iteratively remove fog nodes according to their global or local cooperative influences until there is no feasible provision that can guarantee users' deadlines. For the LPFN case, it is not trivial to check the existence of a feasible provision due to the interactive influence on the scheduling delay for requests. We find a near-optimal solution with bound [8/3]OPT+[(ε2)/(8mα)] using the continuous congestion game and check the feasibility, where m is the number of fog nodes and α is a constant value related to the delay function. Extensive simulations demonstrate the efficiency of our schemes.

Data Center Networks (DCNs) have become more extensively applied in cloud computing in recent years. One important mission for DCNs is to satisfy the fluctuation of on-demand resources for tenants. Existing works fail to fully consider the placement techniques and the elasticity of the physical resource in the DCN at the same time during the scaling of virtual clusters (VCs). To address this, we use elasticity to measure the scaling potential of VCs in terms of both computation and communication resources. In this paper, we consider elastic scaling for existing VCs to maximize the elasticity with the constraint of communication cost in the DCN. We achieve this through a resource allocation scheme, VCS, which comes with provable optimality guarantees for single VC scaling. After that, we extend our scheme for multiple VCs scaling, and we prove that scaling multiple VCs for the over-time elasticity maximization problem is NP-hard. We propose heuristic algorithms MVCS and OMVCS for both offline and online conditions for the multiple VCs scaling. Extensive simulations demonstrate that our elastic VC scaling placement schemes outperform existing state-of-the-art methods in terms of flexibility in the DCN.