Ensemble Temporal Prediction Model-as-a-Service (ETP-MaaS) has become crucial in fields like financial modeling and cloud monitoring. Existing solutions fail to co-optimally address a two-fold challenge of dynamic collaboration and heterogeneity, treating models as independent entities and employing simplistic worker allocation rules. However, at the model level, data volatility means that optimal performance requires identifying and weighting constantly shifting subgroups of base models, not just individual ones; at the system level, these model groups must be efficiently mapped to a pool of heterogeneous and dynamically available workers. To this end, we introduce WIET, an efficient ETP-MaaS system that co-optimizes model weighting and worker allocation. For adaptive weighting, WIET identifies evolving group behaviors among base models and propose a novel group temporal locality-enhanced weighting method. Additionally, WIET develops an efficient, multi-dimensional worker allocation method powered by hybrid heuristic optimization, effectively reducing bottlenecks and resource waste. Experiments show WIET consistently outperforms state-of-the-art methods in terms of accuracy, latency, and resource usage across various workloads and tasks.

Cloud requests and infrastructures are gradually showing a trend of geographical distribution, and the scale is getting bigger and bigger. Due to a lack of information about the network, resources, and other tenants, tenant-made resource region decisions will incur more unnecessary costs than those made by the cloud provider. For such a complex scenario, public cloud providers even put stringent efficiency requirements, requiring decision-making within 1 s for 100 K requests. In summary, there are three significant challenges for task placement in large-scale geographically distributed clouds: complex topology, large scale, and lagging information. Existing studies either focus on intra-regional task placement only or ignore the complex request topology with large-scale and strict decision delay constraints, and most of them need to consider the impact of information lag on decision-making. To address these challenges, we design a large-scale task placement framework called Lattice. Despite the tough challenges posed on a large scale, we find that there are many similar relationships among complex topologies. Therefore, combining offline characterization and online placement, we design a data structure and algorithm based on pattern matching topology compression and interval retrieval, a multi-dimensional parallel placement architecture, and a competition-aware state representation strategy to achieve efficient and high-quality task deployment. Simulation results based on real-world datasets show that our algorithm can achieve an efficiency of 1.74x-15.01x that of existing methods while maintaining similar decision quality. We have open-sourced our algorithm, experimental environment, and experimental data.

Financial risks exert a significant influence on the interbank market, contributing to systemic risk within the banking system. Simulating risk contagion is essential for the prevention of systemic risks. However, existing simulation methods encounter several challenges: the dynamic nature of interbank risk contagion, the effectiveness of interbank lending relationships, and biases inherent in risk contagion theories concerning loss measurement. In response to these challenges, this article establishes an autonomous lending mechanism for banks to endogenously form a dynamic interbank network and introduces the lending relationship generation method evolutionary game method based on multiobjective particle swarm optimization (EG-MOPSO), grounded in real scenarios. Furthermore, to accurately reflect risk contagion losses in practical contexts, this study presents a contagion method that integrates Eisenberg–Noe (EN) and DebtRank, characterizing the contagion process from both bankruptcy risk and actual default risk perspectives. Finally, utilizing the real indicator SRISK, model comparisons, and ablation experiments are conducted to validate the practical effectiveness of various innovative solutions, alongside simulation experiments designed to explore the impact of interest rates on the interbank lending market, thereby providing valuable risk prevention insights for both individual banks and government entities.

Workload prediction is a key step in artificial intelligence for IT operations (AIOps) on cloud platforms, enabling proactive application management for performance assurance, cost reduction, and energy optimization. With the popularity of microservice architectures, user requests are now handled collaboratively by multiple service instances, so the workload variation is no longer the individual behavior of each instance but the group behavior of multiple instances. However, existing approaches typically analyze each instance independently and fail to explicitly model group-level workload evolution, leading to suboptimal predictions. To address this issue, we propose MGroup, a workload group behavior-aware multi-instance workload prediction method. First, we define the concept of highly-coupled multi-instance workload group behavior and its evolution, shifting the analytical focus from individuals to groups; second, we propose an adaptive method for identifying and characterizing the workload group behavior based on both static and dynamic correlations, shifting from similarity-based to correlation-based representation; third, we propose a multi-instance parallel prediction neural network that jointly captures local and global workload evolution, shifting from implicit modeling to explicit modeling. Based on this approach, we design a workload prediction system tailored to cloud-native applications. Finally, experimental results on public datasets show that MGroup reduces MAE by 14.62%-21.60% and RMSE by 21.97%-29.27% compared to existing state-of-the-art methods, which provides an effective solution for realizing workload prediction for cloud-native applications.

The combination of edge and cloud brings new opportunities and challenges to the data placement problem. Expanding from cloud to edge allows data to be placed closer to user, which relieves bandwidth pressure on cloud and reduces latency. Factors such as varying request preferences and regional correlations are becoming increasingly evident. However, they are largely ignored by existing methods. To this end, we combine the distribution of user requests and server location to construct a spatial distribution vector of the request and propose a data placement strategy based on it. Within edge-cloud environment, the proposed strategy can adapt to different request distributions and place data in a targeted manner. Considering the impact of data volume, we assign different weights for placing data on different servers and update them dynamically in response to request fulfillment. To address the imbalance in requests and data volume in network traffic composition, we analyze the similarity of some data request distribution through the vectors, and co-place the data with high similarity to satisfy user demands. Through experiments on real base station distributions and Foursquare dataset, our proposed method reduces the latency by 17.98% to 38.72% and the log ratio of total cost ranges from 0.13 to 0.30 compared to SOTA algorithms.

Microservice architecture is widely adopted in cloud computing for building large-scale distributed systems. However, due to frequent calls between microservices, such systems become increasingly complex and fragile, leading to reduced reliability of the system behavior. Therefore, implementing process discovery and constructing process models for the historical logs of microservice systems becomes crucial to analyzing system behavior. However, existing process discovery methods do not effectively capture the synchronous or asynchronous call relations between microservices at the endpoint level, limiting the ability to analyze the impact of microservice interactions on system performance. This paper proposes a novel process discovery capable of capturing call relations in microservice systems at the endpoint level. First, the call events related to the microservice calls are extracted from the interleaved fragmented raw logs by event serialization to form event sequences. Then, an invocation tree is defined and constructed, and pruning is applied to obtain the subsequence that can be used to derive call relations correctly. Based on the defined event relations, the call relations between endpoints are discovered. Finally, the$\alpha$-based miner is applied to perform the mining and generation of the process model to obtain the defined microservice call net. Experimental results show that, compared to state-of-the-art methods, the proposed approach can accurately and effectively build the microservice system process model.

In multi-access edge computing (MEC), intelligent services are pre-deployed across various edge servers to enable users to offload their tasks and enhance their experience. While dual-timescale frameworks have been proposed to balance long-term service stability and short-term task flexibility, existing approaches largely overlook the intricate dependencies among services and tasks, leading to poor scalability and suboptimal performance in real-world dynamic environments. To bridge this gap, we introduce a spatiotemporal-aware (ST-aware) dual timescale solution for joint service deployment and dependent task offloading in MEC. We first model the joint problem as an integer nonlinear programming (INLP) problem and analyze its NP-hardness. We then reformulate it as a partially observable markov decision process (POMDP) and propose DualFlare, a novel decentralized framework that integrates a greedy global priority task offloading strategy at small timescale and an ST-collaboration service deployment mechanism at large timescale. Our approach leverages a centralized training with decentralized execution (CTDE) paradigm enhanced by knowledge distillation, enabling edge servers to learn globally coordinated yet locally adaptive deployment policies. Extensive experiments demonstrate that DualFlare outperforms state-of-the-art baselines by a significant margin, reducing average user latency by 53.23%, energy consumption by 68.77%, and violation rate by 40.70%, while also achieving remarkable scalability in large-scale MEC scenarios.

In a cluster, a state manager is often used to maintain the states of all hosts and provide the states to schedulers for task scheduling. With the evolution of cloud computing, cluster scales have been expanding. In a large-scale cluster, the state manager, constrained by its limited processing capacity, tends to synchronize with hosts periodically. As a result, resources released by tasks remain marked as occupied in the state manager's view and cannot be reallocated until the next synchronization, which is harmful to the utilization of cluster resources. We refer to these unsynchronized but released resources as Shadow Resources (SR). According to our theoretical analysis, for a cluster with 4k hosts that appears fully loaded in the state manager's view, the shadow resources can account for up to 6.22%. Most existing studies overlook shadow resources, while a few attempt to mitigate their impact by introducing host task queues. However, this approach relies on idealized task runtime prediction and increases system complexity, making it challenging to implement in practice. To overcome the challenges of collecting fleeting shadow resources and utilizing synchronized resources effectively, we propose Shadow Resource Management Architecture (SRMA), which can be embedded into a large-scale cluster under global management. SRMA logically partitions the cluster and collects the shadow resources within each partition through frequent state synchronization. An external SR load balancer, employing the Max-Resource partition First (MRF) algorithm, assigns tasks to SR schedulers, which allocate the collected shadow resources for the tasks. Experiments show that SRMA effectively utilizes shadow resources, achieving near-ideal resource utilization without synchronization delays. It also outperforms other related architectures in task latency and throughput time and provides a benefit equivalent to an increase of 8.5% in the resources of the original cluster.

Resource wastage is common in GPU clusters running deep learning training (DLT) tasks. Studies show that improper allocation of CPU resources is one of the main factors contributing to this waste. Existing methods can predict the task durations under different GPU and CPU joint allocation schemes in advance and then guide resource allocation scheduling to reduce resource wastage. However, the existing prediction methods are challenging in meeting the prediction accuracy and efficiency requirements of the complex combination of GPUs and CPUs. Meanwhile, the existing resource allocation scheduling methods only consider individual task optimization and cannot achieve global optimal allocation. To address these weaknesses, we propose CoreTuner, a predicting and scheduling framework for the joint allocation of CPU and GPU. Firstly, we define a CPU influence model and an Optimal CPU Core Count (OCCC) metric that quantifies how increasing CPU cores beyond a certain threshold can degrade performance. Secondly, based on this model and the OCCC metric, we propose a multi-schemes duration prediction algorithm that combines sampling and extrapolation, achieving accurate multi-schemes prediction with only one sampling, balancing prediction efficiency and accuracy. Finally, we designed a cluster-level dynamic optimal scheduling algorithm. This algorithm prioritizes resource allocation to the tasks that are most sensitive to resources, thereby reducing the overall execution time of tasks across the cluster. Experimental results show that CoreTuner can significantly improve the cluster’s performance: the task makespan, average turnaround time, and GPU utilization increase by up to 51.3%, 75.3%, and 56.54%, respectively, compared to the existing algorithms.

RoCE (RDMA over Converged Ethernet) performs well in a lossless network. However, its performance drops dramatically when packet loss occurs. This limitation stems from the shortcomings of traditional solutions in packet loss recovery, which are explicitly reflected in the two stages of packet loss detection and packet loss retransmission. For packet loss detection, they either rely on implicit detection, which leads to misjudgment or use explicit notification methods that cannot effectively detect multiple lost packets. For packet loss retransmission, they either adopt the Go-back-N mechanism, which is inefficient or implement selective retransmission that still fails to retransmit all missing segments accurately. To address these issues, we propose SNACK, a novel packet recovery mechanism that combines explicit notification and selective NACK(negative acknowledgment) retransmission. SNACK brings two key improvements. First, it precisely detects lost packets through dynamic window monitoring. Second, it adapts to retransmission using SNACK frames, which can selectively request missing packets. Our NS-3 simulation results show that SNACK achieves strong performance under packet loss. When the loss rate is 1%, SNACK keeps the throughput within 10% of the lossless case. In multiflow scenarios, it reduces the average FCT (Flow Completion Time) by 24.8% compared to existing methods. These results demonstrate that SNACK provides efficient loss recovery in various network conditions.

The scaler and scheduler of serverless system are the two cornerstones that ensure service quality and efficiency. However, existing scalers and schedulers are constrained by static thresholds, scaling latency, and single-dimensional optimization, making them difficult to agilely respond to dynamic workloads of functions with different characteristics. This paper proposes a game theory-based scaler and a dual-layer optimization scheduler to enhance the resource management and task allocation capabilities of serverless systems. In the scaler, we introduce the Hawkes process to quantify the “temperature” of function as an indicator of their instantaneous invocation rate. By combining dynamic thresholds and continuous monitoring, this scaler enables that scaling operations no longer lag behind changes of function instances and can even warm up beforehand. For scheduler, we refer to bin-packing strategies to optimize the distribution of containers and reduce resource fragmentation. A new concept of “CPU starvation degree” is introduced to denote the degree of CPU contention during function execution, ensuring that function requests are efficiently scheduled. Experimental analysis on ServerlessBench and Alibaba clusterdata indicates that compared to classical and state-of-the-art scalers and schedulers, the proposed scaler and scheduler achieve at least a 149% improvement in the Quality-Price Ratio, which represents the trade-off between performance and cost.

The on-demand resource model of serverless computing has driven its growing popularity. However, stateful applications require external mechanisms to manage their state, often through dynamic migration for resource allocation. This migration causes function locations to change dynamically, necessitating the scheduling of state data accordingly. Consequently, both stateful functions and their associated states must adapt efficiently to environmental changes-a co-adjustment relationship that is often overlooked or oversimplified in existing systems. Therefore, we design and build DesFaaS, a cross-layer joint dynamic deployment system, to provide an open source solution supporting real Kubernetes clusters for the runtime management of serverless stateful applications with variable locations. Specifically, first, we propose a cross-layer joint dynamic deployment framework, introducing three-layer hybrid migration media and designing a three-layer state management architecture. The designed automated management processes and interfaces support efficient function migration and state management under different load environments. Second, we propose an adaptive co-scheduling method for multi-function state data to optimize the access latency and resource utilization of state data in the system and support in-situ computing by collecting and analyzing the cost. Finally, we developed a prototype system, DesFaaS, and conducted real and simulated experiments, which demonstrated that DesFaaS has 19.36%, 39.06%, and 5.36% improvement in service performance, cost efficiency, and energy consumption compared with the state-of-the-art system.

Owing to remarkable advances in 5G and IoT, Mobile Edge Computing (MEC) has been extensively applied to meet the high latency requirements of computation-intensive applications. This paper explores the optimal resource management in MEC involving multiple competitive edge computing servers (ECSs), and presents a Competition-aware differential Pricing and resource Allocation method (ComPA). First, we propose a differential pricing mechanism that jointly analyzes the computing capability and resource usage rate to comprehensively quantify each MU’s use of ECS resources and give a differential per-second price, alleviating the resource underutilization in traditional schemes. Then, to co-optimize ECSs and MUs and ensure all ECSs have a fair chance to increase revenue, a differential pricing-based hierarchy game is developed. Specifically, ECSs and MUs play a Stackelberg game where ECSs price for higher profits and MUs subsequently offload at least-cost. Meanwhile, a non-cooperative game among competing ECSs is formulated. We design a price renewal algorithm that incorporates choice probabilities to find a suboptimal solution iteratively, offering ECSs the most competitive final pricing. MU’s optimal offloading decision is finally derived through convex optimization. Extensive experiments validate the notable superiority of ComPA over other advanced solutions in boosting ECS revenue and MU experience.

This work on model checking of unbounded Petri nets either not really concern the$\omega$-component or only focus on the$\omega$symbols, which may lead to incorrect judgments. This article proposes a model checking approach of$\omega$-independent unbounded Petri nets. First, this approach can ensure the complete state space required for model checking by analyzing the enabled/unenabled marking set of conditionally enabled transition. Second, a comprehensive model checking process of$\omega$-independent unbounded PN is presented, including the generation of extended new modified reachability graph. Third, two theorems are presented to prove that extended new modified reachability graph includes complete reachable markings and sequences of transitions. Finally, the proposed new approach is illustrated through a practical example.

In serverless inference, complex prediction tasks are executed as workflows, relying on efficient state transfer across multiple functions. Serverless platforms typically deploy each function in a separate stateless container, depending on external processes for state management, which often results in suboptimal system utilization and increased latency. We introduce WasmFlow, a novel framework designed for serverless inference that ensures low latency and high throughput. This is achieved through process-level virtualization using WebAssembly. WasmFlow operates functions on a per-thread basis within compact WebAssembly modules, significantly reducing startup times and memory usage. The framework has two key features. (1) Efficient Memory Sharing: WasmFlow facilitates direct and rapid state transfer between functions using threads within the WebAssembly runtime. This is enabled through lightweight, lock-free, zero-copy intra-process communication, complemented by effective inter-process RPC. (2) System Optimizations: We further optimize WasmFlow with an advanced synchronization technique between functions, an affinity-aware workflow scheduler, and adaptive request batching. Implemented and integrated within the Kubernetes ecosystem, WasmFlow's performance was evaluated using synthetic workloads and realworld Azure traces, including typical serverless workflows and ML models. Our results demonstrate that WasmFlow dramatically outperforms existing serverless frameworks. It reduces P90 end-to-end latency by 74x and 78x, increases function density by n1.7x and 223x compared to Faasm and SPRIGHT, and improves system throughput by 12.3x and 8.8x over Knative and WasmEdge, respectively.

Edge storage offers low-latency services to users. However, due to strained edge resources and high costs, enterprises must choose the data that most warrant placement at the edge and place it in the right location. In practice, data exhibit temporal and spatial properties, and variability, which have a significant impact on their placement, but have been largely ignored in research. To address this, we introduce the concept of data temperature, which considers data characteristics over time and space. To consider the influence of spatial relevance among different regions for placing data, inspired by PageRank, we present a model using data temperature to assess the regional value of data, which effectively leverages collaboration within the edge storage system. We also propose a regional value-based algorithm (RVA) that minimizes cost while meeting user response time requirements. By taking into account the correlation between regions, the RVA can achieve lower latency than current methods when creating an equal or even smaller number of replicas. Experimental results validate the efficacy of the proposed method in terms of latency, success rate, and cost efficiency.

How to efficiently deploying the service components of a data-intensive application on cloud and edge servers to minimize its latency is one of the main challenges for service providers. Most existing studies consider either service deployment or data placement, rather than their joint optimization. This work considers the driving relationship between data and services in a heterogeneous environment including remote cloud and nearby edge servers, and aims to obtain a desired data placement and service deployment scheme while meeting user requirements for service quality. Firstly, we formulate the problem and decouple data placement from service deployment by polynomial reduction. Then, a priority-based data placement strategy is proposed, which can generate a data placement scheme. After that, the original problem is transformed into a classical assignment problem, and a service deployment strategy based on an improved Hungarian algorithm is proposed to obtain a service deployment scheme. Then, a dynamic adjustment strategy based on response weight is proposed to dynamically adjust the data placement and service deployment scheme in order to reduce response latency, and obtain the final scheme. Finally, a series of comparative experiments were conducted, pitting our algorithms against several baseline and SOTA algorithms. The results show that the proposed algorithms, in comparison to other algorithms, is capable of generating superior data placement and service deployment schemes to significantly reduce response latency.

Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build exper-imental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the large-scale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Exper-iments show that LGDCloudSim can simulate up to 5 X 108hosts and 107request concurrency. It also supports diverse scheduling architectures and different request types.

Recently, AI inference services have adopted ensemble architectures, which are widely recognized and used for their advanced performance. However, the existing ensemble inference services are mainly created and managed using the platform-as-a-service model with the static ensemble service architecture and rigid persistent resource allocation. This makes the highly heterogeneous inference requests rely on a fixed combination of basic learners and manual homogeneous resource management, resulting in insufficient precision, waste of resources, and high management costs. Serverless computing, represented by Functions-as-a-Service (FaaS), realizes transparent on-demand resource allocation to developers, which is suitable for ensemble inference services. Therefore, we propose a serverless proactive elastic scheduling solution PESEI for ensemble inference services. First, a two-level hierarchical dynamic ensemble service framework with joint model precision and overhead sensing is proposed to ensure inference precision while improving cost efficiency; second, a proactive elastic resource allocation algorithm with dynamic sensing of workload patterns is proposed to optimize the quality of service and cost efficiency for heterogeneous base learners; based on this, a prototype system is designed and developed to support the autonomous management of ensemble inference services, which implements the benign combination and adaptation of dynamic service architecture and elastic resource management. Real cluster experiments on public datasets demonstrate the effectiveness and robustness of PESEI, providing a new solution for building serverless ensemble inference services.

In recent years, serverless computing has become increasingly popular in the domain of microservices. Compared to serverful computing, serverless computing significantly reduces developers’ expenses due to its resource elasticity and on-demand allocation features. However, serverless computing suffers from long cold start time and high function invocation latency, leading to suboptimal service performance. Due to the dynamic workload, microservices exhibit varying demands for different runtime types over time, which is overlooked by existing approaches. Therefore, we propose the Adaptive Selecting Algorithm for Runtime Types of microservices, which optimizes resource usage in cloud service providers (CSPs) and ensures efficient execution of developers’ applications. Specifically, the algorithm dynamically switches microservices to the optimal runtime type by analyzing the workload patterns, resource requirements, and execution efficiency of microservices. We conducted experiments on real clusters, demonstrating that algorithm enhances the service quality of microservices while improving their cost efficiency.

In today’s ever-changing business environment, enterprises face numerous challenges, including rapid changes in customer needs, intense market competition, and swift technological advancements. Consequently, business process modeling has emerged as a pivotal strategy to tackle these challenges, optimize operations, and enhance efficiency. Traditionally, manual modeling required participants to possess domain knowledge and modeling skills, rendering the process highly complex and prone to errors. To address this, business process recommendation techniques have been introduced to effectively assist business process modeling by leveraging node information from business process repositories. However, existing research on process recommendation often overlooks crucial factors such as nodetype information and fails to integrate actual execution log data, resulting in suboptimal recommendation results. In this study, we consider the node-type information contained in the process to design the edge expansion model and combine it with the log information generated during the actual execution of the process to calculate the confidence level comprehensively and as the basis for the recommendation to conduct offline mining, the results of mining to assist business process analysts in completing the process modeling. Furthermore, we perform a comparative analysis between our proposed algorithm and mainstream algorithms in process recommendation, utilizing a real dataset. The results demonstrate the superiority of our method in terms of precision and branch structure recommendation.

Summary Concurrent software needs to be maintained over time, and the differences between continuous versions tend to be localized. The expense that simply reapplying standard model checking techniques to the new version as they evolve may be infeasible. The existing methods reuse partial state‐space to reduce the scope. However, it is obviously costly to analyze on the explosive interleaving space of the evolving concurrent programs. The conservative change‐impact analysis methods without considering the specific property and leveraging the verified result from the prior version often results in exploring redundant state‐space irrelevant to this property. Moreover, the impact of the deleted elements needs to be analyzed on old version, and their impact needs to be mapped to new version, bringing some dispensable costs. In this paper, we propose a change‐aware model checking method based on program dependence net (PDNet) for linear temporal logic (LTL). We first propose an incremental modeling method to construct a PDNet of new version by modification rules. Then, we propose a reuse checking algorithm to judge whether the verified result can be reused based on the PDNet slice. Finally, we implement change‐aware model checking tool (DAMER) and validate the advantages of our methods.

The performance of distributed ML training is largely determined by workers that generate gradients in the slowest pace, i.e., stragglers. The state-of-the-art load balancing approaches consider that each worker stores a complete dataset locally and the data fetching time can be ignored. They only consider the computation capacity of workers in equalizing the gradient computation time. However, we find that in scenarios of ML on distributed datasets, whether in edge computing or distributed data cache systems, the data fetching time is non-negligible and often becomes the primary cause of stragglers. In this paper, we present LOFT, an adaptive load balancing approach for ML upon distributed datasets at the edge. It aims to balance the time to generate gradients at each worker while ensuring the model accuracy. Specifically, LOFT features a locality-aware batching. It builds performance and optimization models upon data fetching and gradient computation time. Leveraging the models, it develops an adaptive scheme based on grid search. Furthermore, it offers Byzantine gradient aggregation upon Ring All-Reduce, which makes itself fault-tolerant under Byzantine gradients brought by a small batch size. Experiments with twelve public DNN models and four open datasets show that LOFT reduces the training time by up to 46%, while reducing the training loss by up to 67% compared to LB-BSP.

Nowadays, Electronic Toll Collection (ETC) control systems have been widely adopted to smoothen traffic flow on highways. However, as it is a complex business interaction system, there are inevitably flaws in its control logic process, such as the problem of vehicle fee evasion. We find that there is more than one way for vehicles to evade fees. This shows that it is difficult to ensure the completeness of its design. Therefore, it is necessary to adopt a novel formal method to model and analyze its design, detect flaws, and modify it. In this article, a Colored Petri net (CPN) is introduced to establish its model. To analyze and modify the system model more efficiently, a dynamic slicing method of CPN is proposed. First, a static slice is obtained from the static slicing criterion by backtracking. Second, considering all binding elements that can be enabled under the initial marking, a forward slice is obtained from the dynamic slicing criterion by traversing. Third, the dynamic slicing of CPN is obtained by taking the intersection of both slices. The proposed dynamic slicing method of CPN can be used to formalize and verify the behavior properties of an ETC control system, and the flaws can be detected effectively. As a case study, the flaw about a vehicle that has not completed the payment following the previous vehicle to pass the railing is detected by the proposed method.

Serverless computing is a popular cloud computing model that offers on-demand resource allocation and pay-as-you-go application execution. However, there are still challenges in allocating resources for workflow applications: inaccurate and inefficient resource estimation, high-latency inter-function communication, and long server readiness time. Therefore, we propose the heterogeneity-aware Proactive serverLess wOrkflow Elastic Allocation method (PLOEA) to address these issues and optimize infrastructure costs for cloud service providers (CSPs) while meeting the diverse needs of developers. Specifically, we propose a resource configuration estimation method for heterogeneous workflow applications that builds an ensemble multi-task expert classifier to analyze individual and common resource usage patterns, ensuring estimation accuracy and efficiency. Further, we propose a group allocation strategy for multiple applications that optimizes the spatiotemporal distribution of instances by considering the allocation urgency, communication affinity between functions, and the multi-core architecture of servers. Furthermore, we present a proactive server elastic scaling method that senses workload features, including workload level, trend, and magnitude changes, and combines them with CSP's attention differences to guide the server scaling size. Finally, experiments based on public datasets prove that PLOEA provides better service quality and cost efficiency than existing methods.

Each activity in the service workflow interacts with services as required to meet complex business needs and quickly adapt to market changes. The design of each activity's input/output interface parameters influences whether it can successfully map to appropriate and interactive services. In practice, suitable activity interface parameters should possess 3-features:$realism$,$relevance$, and$compatibility$, as popular parameters originating from the real world and closely related to activity semantics are apt to match user-expected services. However, existing research requires expert specification or ontology-based inference, resulting in outdated, inconsistent parameters that lack necessary elements, making it challenging to match expected services. Therefore, we propose an automated method combining Transformer and weighted HITS to recommend interface parameters with 3-features on activity function requirement. It filters similar Endpoints (EPs) based on the activity's semantics by supervised Transformer-based learning of multi-domain APIs and unsupervised EPs matching. Next, a node-weighted heterogeneous graph is built based on similar EPs and their interface parameter relationships. We then apply a node-weighted HITS to explore mutual gain relationships within the graph and calculate parameter compatibilities. Finally, a top-$k$non-redundant compatible parameter list and corresponding different formats are recommended for the activity. The method's effectiveness and efficiency are verified using a real API service dataset from RapidAPI.

Monolithic architectures are becoming increasingly difficult to cope with complex applications, and microservice architectures, which offer flexibility and logical independence in development and maintenance, are the new choice for companies and developers. Migrating a legacy monolithic architecture application to a microservice architecture rather than building it from scratch is considered an easy way to use it. To ensure that the migrated microservice applications can take advantage of their benefits, we need to propose a reasonable and effective microservice extraction method. Considering the single responsibility principle in the microservice design principle, most existing microservice extraction methods only pursue the high logical independence of the extraction results and pay little attention to whether the extraction results have good performance. Applications need to perform well, and studies have shown that poor microservice extraction schemes can negatively impact the performance of the migrated application. As a result, when extracting, we should also consider the performance of the results. A few studies consider the performance of extraction results, but only in terms of a few factors affecting performance, such as network overhead, rather than considering all factors affecting performance comprehensively, which leads to an inaccurate evaluation of performance. Therefore, oriented toward the most widely used managed languages today, we propose an effective Microservice Extraction method based on a Comprehensive Evaluation of logical independence and performance (MECE). Firstly, we propose a workflow-based approach to evaluate the performance of microservice extraction results by considering multiple influencing factors, focusing on the management cost ignored in existing studies, and designing an effective management cost evaluation model. After that, we propose a meta-heuristic search-based algorithm to obtain feasible microservice extraction results. In experiments based on actual deployments, the extraction results of the MECE method obtained a performance improvement of up to 46.15% without significant loss of logical independence compared to existing methods, which verifies the effectiveness of the method.

Accurately forecasting workloads can enable web service providers to achieve proactive runtime management for applications and ensure service quality and cost efficiency. For cloud-native applications, multiple containers collaborate to handle user requests, making each container’s workload changes influenced by workload group behavior. However, existing approaches mainly analyze the individual changes of each container and do not explicitly model the workload group evolution of containers, resulting in sub-optimal results. Therefore, we propose a workload prediction method, GROUP, which implements the shifts of workload prediction focus from individual to group, workload group behavior representation from data similarity to data correlation, and workload group behavior evolution from implicit modeling to explicit modeling. First, we model the workload group behavior and its evolution from multiple perspectives. Second, we propose a container correlation calculation algorithm that considers static and dynamic container information to represent the workload group behavior. Third, we propose an end-to-end multi-step-ahead prediction method that explicitly portrays the complex relationship between the evolution of workload group behavior and the workload changes of each container. Lastly, enough experiments on public datasets show the advantages of GROUP, which provides an effective solution to achieve workload prediction for cloud-native applications.

In mobile interactive systems, there exist several components that can move and interact with each other. The current methods fail to give a comprehensive description of their properties and have inadequate modeling and analysis capacity for them. This paper concludes three system properties called system connectivity, interaction soundness and data validity, and focuses on their modeling and analysis based on Variable Petri Nets (VPNs), which have recently been proposed and are able to describe their dynamic interactions. A VPN- based model including component nets and interaction structure nets is constructed given a mobile interactive system. It depicts its structure and event-driven dynamics. Three properties and their related analysis methods of a VPN-based model are presented. An example is given to demonstrate the proposed concepts and methods. Note to Practitioners—Due to the mobility and frequent disconnections, the correctness of mobile interaction systems, such as mobile robot systems and mobile payment systems, are often difficult to analyze. This paper introduces three critical properties of systems, called system connectivity, interaction soundness and data validity, and presents a related modeling and analysis method, based on a kind of Petri net called VPN. For a given system, a model including component nets and interaction structure nets is constructed by using VPNs. The component net describes the internal process of each component, while the interaction structure net reflects the dynamic interaction between components. Based on this model, three properties are defined and analyzed. The case study of a practical mobile payment system shows the effectiveness of the proposed method.

Microservices and Kubernetes are widely used in the development and operations of cloud-native applications. By providing automated placement and scaling, Kubernetes has become the main tool for managing microservices. However, existing work and Kubernetes fail to consider the dynamic competition and availability of microservices as well as the problem of shared dependency libraries among multiple microservice instances. To this end, this article proposes an integer nonlinear microservice placement model for Kubernetes with the goal of cost minimization. Specifically, we calculate the number of instances based on microservice availability and construct a model in which the total resource demand of multiple microservice instances exceeds the appropriate proportion of node resources when dynamic resource competition exists and the size of the shared dependency library is less than the node storage capacity. Finally, this article solves the microservice placement model using an improved genetic algorithm. The experimental results demonstrate that higher throughput is obtained with the same costs and that the same throughput is obtained with lower costs.

The interaction process in microservice architectures is highly complex, making it very challenging to ensure correct behavioral interactions. The few related works focus only on the verification of interaction soundness in the case of a specific bufferk-value, without considering how to get the suitable bufferk-value. To solve the above problems, this article proposes a method to find the maximumk-value for microservice systems with bounded buffers, which can maximize the analysis of valuable asynchronous interaction paths while avoiding the waste of computer memory resources. Specific contributions include, first, giving the relationship between bufferk-value and asynchronous interaction paths, interaction soundness, and its proof; second, proposing an iterative detection based on the additional added paths algorithm and its correctness proof, which leads to the conclusion that finding the maximumk-value is a decidable problem; finally, validating the proposing methods on ten classical cases of microservice systems, and analyzing effectiveness and performance. The experimental results show that the method can effectively find the maximumk-value of bounded buffers compared with existing methods and thus ensure the correct behavioral interactions of microservice systems.

With the rapid development of manufacturing and cloud computing, more and more emerged cloud services provide a promising way to perform complex requirements efficiently. Workflow offers an effective way to assemble disparate services and interacts with them to construct business logic for user requests. Meanwhile, workflow engines are responsible for the control of workflow execution. However, existing engines usually support interaction between workflows and services or computing resources by tight binding approaches, which lack flexibility and scalability. Therefore, a flexible and decoupled architecture is essential to support automatic workflow management. To fill this gap, this paper proposes a novelservice-centered cloud-native workflow architecture- SCAFE. The introduction of the service layer in SCAFE decouples the upper business services and lower execution resources, facilitating the independent and joint management of three execution objects (request - service - execution instance) involved in the cloud workflow lifecycle. We present a 2-stage scheduling model for the new architecture to support customized service optimization and resource-aware task scheduling. In addition, a fault-tolerant mechanism is integrated into resolving task execution exceptions quickly. We have successfully implemented a prototype tool to verify its flexibility and crucial functions, thus advancing the field workflow engines in cloud-native environments.

The workload predictor has attracted attention as a key component of the proactive service operation management framework. However, the request and resource workloads of cloud applications are highly dynamic. Existing approaches decompose the original workload into trend, seasonal, and random components, establish models accordingly, and then combine all outputs to generate results. Indeed, the random component usually has significant heteroscedasticity and noise, having little or even a negative effect on model accuracy improvement. In our model, trend and seasonal components are seen as macro workload changes, and the micro workload changes are obtained by an adaptive sliding window algorithm. Therefore, we propose an ensembling model named FAST for Forecasting workloads with Adaptive Sliding window and Time locality integration. Notably, we propose an adaptive sliding window algorithm that considers trend correlation, time correlation, and random fluctuations of workload for online regression to achieve higher accuracy with lower overhead; and for the error-based integration strategy, we propose a time locality concept for local-predictor behavior and develop a multi-class regression algorithm for model integration. Finally, we conduct experiments on Google cluster trace datasets which show FAST has better accuracy than all other state-of-the-art models for dynamic workloads.

Current IaaS providers deploy cheaper computing resources in newly built data centers and provide cross-regional network services to improve the interoperability of computing resources in different regions. Third-party service providers can use part of their budget to purchase cross-regional communication resources to use cheaper resources in remote areas to reduce the cost of processing massive task requests. The Q-percentile charging model is widely used in cross-regional communication resources billing, but there is little task scheduling research on that billing method. Therefore, this paper studies a geo-distributed task scheduling scenario using the Q-percentile charging model. We design a geo-scheduling algorithm specifically for Q-percentile charging model to allocate resources in the two dimensions of computing resources and communication resources. Furthermore, referring to three existing communication resource allocation strategies, we design three bandwidth allocation algorithms considering the Q-percentile charging characteristics to provide suitable solutions for different scenarios. We conducted experiments based on public well-known datasets such as LIGO workflow. Results show that, compared with the baseline, the scheduling algorithm proposed in this paper can reduce the task scheduling cost between geo-distributed data centers by 10%-20% based on various task loads and show differences in the applicability of different communication resource allocation strategies.

Hyperparameter tuning (HPT), which chooses a set of optimal hyperparameters for a learning algorithm, is critical to machine learning training. Unfortunately, the current resource provisioning approaches for HPT are unable to adjust resources adaptively according to the upward trends of HPT accuracy at runtime, resulting in low GPU utilization or HPT accuracy. On the other hand, dynamic resource provisioning approaches based on checkpointing are inefficient for HPT, because of high overhead of context switching and job restarting.

Recently, containers have become the primary deployment form for cloud applications. Predicting container workload accurately is critical to ensure the quality of service (QoS) and cost-efficiency of the applications and meet service level agreements (SLAs) with users. However, facing multiple challenges, including model unavailability due to insufficient data, model maladaptation due to dynamic workload changes, and model non-generalization due to changeable workload patterns in container workload prediction, existing methods have not yet provided a united and effective solution. To this end, we propose a novel integrated forecasting model named COIN that combines COmmon and INdividual changes in container workloads to ensure the availability, adaptivity, and generality of the prediction model based on transfer learning and online learning. Besides, we present a container similarity calculation algorithm for real cloud scenarios, which combines the static and dynamic information of containers and comprehensively depicts the similarity between containers. Through experiments based on two public datasets, the COIN model achieves a higher accuracy than existing state-of-the-art solutions, demonstrating the effectiveness and robustness of our proposed model, which provides a new solution to container workload prediction.

Mobile computing systems, service-based systems, and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics, such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called variable Petri net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect the link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method.

The properties of dynamic interactions in mobile-interactive systems are still difficult to analyze because of the complexity of systems. Thus, we have proposed a new Petri net called the variable petri net (VPN) recently, which specializes in describing dynamic interactions in systems. To make better use of VPN, this article focuses on the model checking method of VPN. It introduces the algorithm to transform a VPN to a Kripke structure that can describe both the system running states and the system connection states in VPN, and the method to transform a property to a temporal logic formula based on VPN and its Kripke structure. The Kripke structure can be optimized by considering the specific property about the system connection states and then be used to perform the targeted verification to the property by using a model checker. A practical example is given to demonstrate the proposed methods.

Autoscaling is one of the major features of Cloud Computing aiming to improve the Quality-of-Service(QoS) in response to fluctuating workloads. Existing state-of-the-art autoscaling methods for Kubernetes focus on single scaling mode, that is, only horizontal scaling and only vertical scaling. For horizontal scaling, a high resource usage rate cannot be guaranteed sometimes; and for vertical scaling, microservice instances appear a performance ceiling that does not grow indefinitely as the supply of resources increases. In this paper, we propose a novel combined scaling method called COPA. Based on the collected microservice performance data, real-time workload, expected response time, and microservice instances scheme at runtime, COPA uses the queuing network model to calculate a combined scaling scheme that aims to minimize the default cost and resource cost. We evaluated our approach in a Kubernetes cluster, and compare it with existing state-of-the-art autoscaling methods under four different workload types. Such experiments show a reduction of ×1.22 for resource cost while ensuring the QoS as compared to the baseline method.

Optical degradation blurs text shapes and edges, so existing scene text recognition methods have difficulties in achieving desirable results on low-resolution (LR) scene text images acquired in real-world environments. The above problem can be solved by efficiently extracting sequential information to reconstruct super-resolution (SR) text images, which remains a challenging task. In this paper, we propose a Parallelly Contextual Attention Network (PCAN), which effectively learns sequence-dependent features and focuses more on high-frequency information of the reconstruction in text images. Firstly, we explore the importance of sequence-dependent features in horizontal and vertical directions parallelly for text SR, and then design a parallelly contextual attention block to adaptively select the key information in the text sequence that contributes to image super-resolution. Secondly, we propose a hierarchically orthogonal texture-aware attention module and an edge guidance loss function, which can help to reconstruct high-frequency information in text images. Finally, we conduct extensive experiments on TextZoom dataset, and the results can be easily incorporated into mainstream text recognition algorithms to further improve their performance in LR image recognition. Besides, our approach exhibits great robustness in defending against adversarial attacks on seven mainstream scene text recognition datasets, which means it can also improve the security of the text recognition pipeline. Compared with directly recognizing LR images, our method can respectively improve the recognition accuracy of ASTER, MORAN, and CRNN by 14.9%, 14.0%, and 20.1%. Our method outperforms eleven state-of-the-art (SOTA) SR methods in terms of boosting text recognition performance. Most importantly, it outperforms the current optimal text-orient SR method TSRN by 3.2%, 3.7%, and 6.0% on the recognition accuracy of ASTER, MORAN, and CRNN respectively.

Microservices are widely used for flexible software development. Recently, containers have become the preferred deployment technology for microservices because of fast start-up and low overhead. However, the container layer complicates task scheduling and auto-scaling in clouds. Existing algorithms do not adapt to the two-layer structure composed of virtual machines and containers, and they often ignore streaming workloads. To this end, this article proposes an Elastic Scheduling for Microservices (ESMS) that integrates task scheduling with auto-scaling. ESMS aims to minimize the cost of virtual machines while meeting deadline constraints. Specifically, we define the task scheduling problem of microservices as a cost optimization problem with deadline constraints and propose a statistics-based strategy to determine the configuration of containers under a streaming workload. Then, we propose an urgency-based workflow scheduling algorithm that assigns tasks and determines the type and quantity of instances for scale-up. Finally, we model the mapping of new containers to virtual machines as a variable-sized bin-packing problem and solve it to achieve integrated scaling of the virtual machines and containers. Via simulation-based experiments with well-known workflow applications, the ability of ESMS to improve the success ratio of meeting deadlines and reduce the cost is verified through comparison with existing algorithms.

Data dependency, often presented as directed acyclic graph (DAG), is a crucial application semantics for the performance of data analytic platforms such as Spark. Spark comes with two built-in schedulers, namely FIFO and Fair scheduler, which do not take advantage of data dependency structures. Recently proposed DAG-aware task scheduling approaches, notably GRAPHENE, have achieved significant performance improvements but paid little attention to cache management. The resulted data access patterns interact poorly with the built-in LRU caching, leading to significant cache misses and performance degradation. On the other hand, DAG-aware caching schemes, such as Most Reference Distance (MRD), are designed for FIFO scheduler instead of DAG-aware task schedulers.In this paper, we propose and develop a middleware Dagon, which leverages the complexity and heterogeneity of DAGs to jointly execute task scheduling and cache management. Dagon relies on three key mechanisms: DAG-aware task assignment that considers dependency structure and heterogeneous resource demands to reduce potential resource fragmentation, sensitivity-aware delay scheduling that prevents executors from long waiting for tasks insensitive to locality, and priority-aware caching that makes the cache eviction and prefetching decisions based on the stage priority determined by DAG-aware task assignment. We have implemented Dagon in Apache Spark. Evaluation on a testbed shows that Dagon improves the job completion time by up to 42% and CPU utilization by up to 46% respectively, compared to GRAPHENE plus MRD.

This paper focuses on the behavior similarity of workflow nets (WF-nets). The similarity of two WF-nets reflects their consistent degree in behaviors. It explores the behavioral relations of subsets of transitions based on the interleaving semantics, and more accurate relations are defined than the existing work. Therefore, a more accurate similarity of two WF-nets (in their behaviors) can be obtained than that in the existing work that usually do not consider the loop and complex correspondence. By refining the interleaving relation in a behavioral profile into six types, this paper proposes the notion of a relation profile based on behavioral profile. Based on the relation profile of a WF-net, behavioral relation matrix can be constructed. Additionally, we refine the complex correspondence and generate a group of behavioral relation submatrices from the behavioral relation matrix. By using them we present a new formula to measure the behavior similarity of two WF-nets. Finally, examples illustrate that our method can measure the similarity degree more accurately.

The e-commerce business process net (EBPN) is a novel formal model for describing an e-commerce system and its interactive parts, such as shoppers, merchants, and the third-party payment platforms. Vulnerability analysis has a great impact on the trustworthiness of EBPN, which is an issue stemming from data inconsistency problems. Data inconsistency problems affect the consistency of the EBPN transaction analysis. The underlying causes of inconsistent data are closely related to concurrent operations, such as control flow and data flow. However, most of the existing detection methods have difficulties characterizing the vulnerabilities and interactions of control and data flows. In this paper, we propose a new method based on the dynamic data slice (DDS) that considers both transaction consistency and data state consistency. First, by analyzing control flow characteristics of EBPN, we obtain the dynamic slice. This dynamic slice is based on all paths of the EBPN reachability graph. Second, we perform the data inconsistency analysis by considering both transaction consistency and data-state consistency. Based on these, we construct a DDS to characterize the behavioral logic and the data-dependence information. The DDS acquires the dynamic data firing sequence. Based on that sequence and a given data marking, we can construct the DDSs for several types of EBPNs. Constructing the DDS can be completed in polynomial time. The DDS is designed to characterize the behavioral logic and data-dependence information. Based on these, we design a method to judge the data constraints. This method satisfies the EBPN need for transaction consistency by considering both the control and data states. In addition, according to the data-dependence information, we can lock the vulnerable regions caused by abnormal trading data in the system. Finally, we give a method to compute the vulnerability level.

In human-computer interactive systems (HCISs), there are not only autonomous robots completely controlled by computers but also semiautonomous robots requiring human control. To avoid the errors in a procedure of interaction, a control model is needed. This paper proposes a systematic strategy with specific algorithms to construct an interactive-control-model based on Petri nets owing to their ability to describe concurrence and other system features. Instead of cumbersome iterations of deadlock detection in the existing studies, this paper introduces the concept of implicit constraints and the related implicit-conflict-marking-search algorithm to excavate them. In the algorithm, only the status of a single robot is needed to analyze the system instead of the status of all system components, which is an important innovation in this paper since this can well help one resolve the state explosion issue. Several examples are provided to show the feasibility of the proposed method. The proposed idea in this paper can be readily applied to practical HCISs.

The tremendous growth of cloud computing and large-scale data analytics highlight the importance of reducing datacenter power consumption and environmental impact of brown energy. While many Internet service operators have at least partially powered their datacenters by green energy, it is challenging to effectively utilize green energy due to the intermittency of renewable sources, such as solar or wind. We find that the geographical diversity of internet-scale services can be carefully scheduled to improve the efficiency of applying green energy in datacenters. In this paper, we propose a holistic heterogeneity-aware cloud workload management approach, sCloud, that aims to maximize the system goodput in distributed self-sustainable datacenters. sCloud adaptively places the transactional workload to distributed datacenters, allocates the available resource to heterogeneous workloads in each datacenter, and migrates batch jobs across datacenters, while taking into account the green power availability and QoS requirements. We formulate the transactional workload placement as a constrained optimization problem that can be solved by nonlinear programming. Then, we propose a batch job migration algorithm to further improve the system goodput when the green power supply varies widely at different locations. Finally, we extend sCloud by integrating a flexible batch job manager to dynamically control the job execution progress without violating the deadlines. We have implemented sCloud in a university cloud testbed with real-world weather conditions and workload traces. Experimental results demonstrate sCloud can achieve near-to-optimal system performance while being resilient to dynamic power availability. sCloud with the flexible batch job management approach outperforms a heterogeneity-oblivious approach by 37 percent in improving system goodput and 33 percent in reducing QoS violations.

Summary With the vigorous development of the financial sector, financial risks are showing a tendency toward diversification, particularly regarding the customer credit risk of commercial banks. Therefore, the customer's credit risk is being considered by financial institutions, and a credit evaluating model has emerged as a result. Currently, research has concentrated on enhancing the precision of the model, ignoring the interpretability, which makes it difficult to apply in the industry. Compared to precision, studies related to the interpretable model are limited. In our previous work, we did not consider model operation time and stability. Therefore, this study proposes a hybrid model based on the RIPPER algorithm. First, according to the characteristics of credit card data sets, targeted special data pretreatment methods are proposed. Next, the RELIEF method for feature selection removes the redundant features and further improves the interpretability of the model. Then, to address the problem of the imbalanced distribution of credit card data sets, a synthetic minority class sampling algorithm is used to equalize the samples. Finally, default credit card users are predicted by taking advantage of the rules generated by the RIPPER algorithm. To test the performance of the model, we used Taiwanese credit card customer data for empirical research. We considered model accuracy and interpretability when comparing the proposed SPR‐RIPPER model with the existing mainstream models. The results of the experiments indicate that the proposed model achieves acceptable results. This study demonstrates that the proposed credit card user default prediction model, SPR‐RIPPER, has practical application value.

E-commerce business process nets (EBPNs) are a novel formal model for describing and validating e-commerce systems including interactive parties such as shopper, merchant, and third-party payment platform. Data errors and nondeterminacy of the data states during the trading process can be depicted with the help of EBPNs. However, the problem about how to analyze EBPNs remains largely open. To analyze their data-liveness, data-boundedness, and reachability, this paper presents two analysis methods. For EBPNs, reachability analysis is proposed based on a 3-D incidence matrix method. Additionally, reduction methods are proposed for a special EBPN. Finally, the validity and reliability of the proposed methods are illustrated via the examples of e-commerce systems.

Summary In the mobile environment, users will use mobile devices to enjoy different services, such as surfing the Internet, shopping online, or paying bills. And the network latency will greatly affect the performance of services, which influences the users using experience at the same time. Many service selection methods have treated network latency as QoS, but they do not consider the issue of user's mobility that resulting inconstantly changing of latency when existing information transmission between the user and services in mobile environment. So it is meaningful to take it into consideration to select globally optimal service to solve the practical problem. In this paper, we consider the user's movement, estimate the network latency by Euclidean distance, and propose a service composition algorithm in mobile environment to provide the best service scheme with the basic idea of dynamic programming. Finally, we make experiments by changing different factors and obtain lower latency compared with the traditional method; reflecting service composition algorithm in mobile environment can better adapt to the user's mobility and uncertain times of interaction between the user and services. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a fully expanded tree (FET) approach for one-place-unbounded Petri nets. The FET of a one-place-unbounded Petri net consists of all and only reachable markings from its initial marking. Its applications to liveness and deadlock analysis for such Petri nets are developed. The proposed method has a larger application scope than all the existing methods for them. Several examples are provided to show its superiority over the state-of-the-art methods.

Liveness of Petri nets means all activities in a modeled system can potentially take place and thus implies deadlock freedom. The research on liveness analysis approaches is inadequate. This paper proposes a liveness judgment reachability graph (LJRG) approach to analyze the liveness of ω-independent Petri nets. Such nets can be loosely explained as a class of unbounded Petri nets in which the changes of tokens in unbounded places are not related to each other. This paper proposes several algorithms to transform a new modified reachability tree to a new modified reachability graph and then transform it to an LJRG. It then develops the application of LJRG into the liveness analysis of ω-independent unbounded Petri nets. The proposed method provides a new theoretical method and important tool for the liveness analysis of unbounded Petri nets. It is illustrated via some examples.

To deal with urban traffic congestion, this paper proposes an actuated green wave method. It extends an arterial actuated bandwidth method to network situations. The arterial bandwidth is based on several key temporal differences to determine phase patterns and offsets. The network is decomposed in such a way that it only consists of arterial bandwidth problems. It adds intersections from the start point in a breadth-first-search manner until the target road network is covered. To further make the bandwidth method dynamic, gap-out logic is also embedded in each cycle using vehicle counts detected by trap detectors. Given detected real-time parameters, the timing plan is updated periodically to adapt to traffic variations. An offset transitioning logic is also proposed to smooth timing plan updates. Finally, simulation tests are conducted against a self-organized actuated control method, on a 52-intersection road network using VISSIM. The results show that for more saturated situations the proposed method outperforms the actuated method in terms of average delay, stops, link speeds and vehicle arrivals.

Summary With the rapid development of the network, stand‐alone crawlers are finding hard to find and gather information. Distributed crawlers are gradually accepted to solve this problem. This paper proposes a task scheduling strategy based on weighted round robin for small‐scale distributed crawler with formula weights for the current node based on crawling efficiency, implements a distributed crawler system with multithreading support and deduplication which takes the algorithm as core, and discusses some possible extensions and details. The design of the error recovery mechanism and the node table allows crawling nodes have flexible scalability and fault tolerance. Finally, we conducted some experiments to prove the good load balancing performance of the system. Concurrency and Computation: Practice and Experience, 2015.© 2015 Wiley Periodicals, Inc. Copyright © 2015 John Wiley & Sons, Ltd.

Traditional network anomaly detection involves developing models that rely on packet inspection. However, increasing network speeds and use of encrypted protocols make per-packet inspection unsuited for today’s networks. One method of overcoming this obstacle is aggregating packet header information and performing flow-based analysis where data flow patterns are examined rather than deep packet inspection. Many existing approaches are special purpose limited to detecting specific behavior. Also, the data reduction inherent in identifying anomalous flows hinders alert correlation. In this article, we propose and develop a dynamic anomaly detection approach for augmented network flows. We sketch network state during flow creation, enabling general-purpose threat detection. We describe an efficient flow augmentation approach based on the count-min sketch that provides per-flow-, per-node-, and per-network-level statistics parallel to flow record generation. We design and develop a support vector machine-based adaptive anomaly detection and correlation mechanism, which is capable of aggregating alerts without a priori alert classification and evolving models online. We further develop a lightweight evolving alert aggregation method and combine it with a confidence forwarding mechanism identifying a small percentage predictions for additional processing. We show effectiveness of our methods on both enterprise and backbone traces. Experimental results demonstrate its ability to maintain high accuracy without the need for offline training.

Recently, online shopping integrating third-party payment platforms (TPPs) introduces new security challenges due to complex interactions between Application Programming Interfaces (APIs) of Merchants and TPPs. Malicious clients may exploit security vulnerabilities by calling APIs in an arbitrary order or playing various roles. To deal with the security issue in the early stages of system development, this paper presents a formal method for modeling and verification of online shopping business processes with malicious behavior patterns considered based on Petri nets. We propose a formal model called E-commerce Business Process Net to model a normal online shopping business process that represent intended functions, and malicious behavior patterns representing a potential attack that violates the security goals at the requirement analysis phase. Then, we synthesize the normal business process and malicious behavior patterns by an incremental modeling method. According to the synthetic model, we analyze whether an online shopping business process is resistant to the known malicious behavior patterns. As a result, our approach can make the software design provably secured from the malicious attacks at process design time and, thus, reduces the difficulty and cost of modification for imperfect systems at the release phase. We demonstrate our approach through a case study.

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.

By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.

The number of web services has grown drastically. Then how to manage them efficiently in a service repository is an important issue to address. Given a special field, there often exists an efficient data structure for a class of objects, e.g., the Google' Bigtable is very suitable for webpages' storage and management. Based on the theory of the equivalence relations and quotient sets, this work proposes a multilevel index model for large-scale service repositories, which can be used to reduce the execution time of service discovery and composition. Its novel use of keys as inspired by the key in relational database can effectively remove the redundancy of the commonly-used inverted index. Its four function-based operations are for the first time proposed to manage and maintain services in a repository. The experiments validate that the proposed model is more efficient than the existing structures, i.e., sequential and inverted index ones.

Due to the openness and dynamic characteristics of the network environment, the failure of component services in a composite Web service (CWS) can occur. CWS is a conglomeration of existing Web services (WSs) interacting together to offer a new value-added service. Hence, it is necessary to perform some transactions to ensure its correct and reliable execution. However, their introduction complicates its quality of service (QoS) analysis. How to analyze and evaluate its QoS values based on them becomes an important research issue. To address the problem, this paper introduces transactional properties of a single WS and CWS and conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop ones. Then, it develops an algorithm to compute the execution time of CWS and shows an example to illustrate the effectiveness of the proposed algorithm.

An exploratory search may be driven by a user's curiosity or desire for specific information. When users investigate unfamiliar fields, they may want to learn more about a particular subject area to increase their knowledge rather than solve a specific problem. This work proposes a topic-oriented exploratory search method that provides browse guidance to users. It allows them to discover new associations and knowledge, and helps them find their interested information and knowledge. Since an exploratory search needs to judge the ability to discover new knowledge, the existing commonly used metrics fail to capture it. This paper thus defines a new set of criteria containing clarity, relevance, novelty, and diversity to analyze the effectiveness of an exploratory search. Experiments are designed to compare results from the proposed method and Google's “search related to ....” The results show that the proposed one is more suitable for learning new associations and discovering new knowledge with highly likely relevance to a query. This work concludes that it is more suitable than Google for an exploratory search.

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.

As there are various risks of failure in its execution, a composite web service (CWS) requires a transactional mechanism to guarantee its reliable execution. Though the existing service selection methods have considered that its transactional properties may affect its quality of service (QoS) such as its execution time, some of these methods can just give the locally optimal transactional CWS while others can give globally optimal CWS only under a given fixed transactional workflow. This paper addresses the issue of selecting and composing web services via a genetic algorithm (GA) and gives a transaction and QoS-aware selection approach. First, it introduces transactional properties of a single web service and CWS and the transactional rules used to compose them. Next, it conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop patterns and develops an algorithm to compute the execution time of a complex CWS. Then, it presents a GA-based approach, which takes into account the execution time, price, transactional property, stability, and penalty-factor, to achieve globally optimal service selection. Finally, this paper reports experimental results that compare the proposed approach with the exhaustive search algorithm, transactional-QoS-driven selection algorithm, and transactional service selection algorithm. The experimental results show that the proposed algorithm is efficient and effective and can give a globally optimal transactional CWS.

Internet data are heterogeneous, redundant, disordered, and exponentially growing. Finding the right information from them becomes an ever-challenging issue. Existing technologies such as inverted index and keyword matching can list user webpage matching with given search keywords. They cannot recognize potential relations among webpages to meet some rising user needs, e.g., exploratory search and personalized search. We propose an indexing network model that organizes information in webpages at three levels: words, webpages, and categories, thereby leading to a semantic association graph. Words are used as the description of webpages and categories. Webpage classification is used to gather similar webpages together. Hyperlinks imply the wisdom of the webpage creator, which can help us generate semantic relations among categories. With a clear organizational structure, an indexing network can provide support for many important applications including intelligent information retrieval, recommendation and decision support. In order to provide access to interfaces for the proposed indexing network, an indexing network algebra is defined. Finally, to validate the proposed model, an indexing network is generated based on 30 million webpages and its structure is analyzed. We also give methods to achieve “browsing navigation” and “personalized search” based on the generated network. Results reveal that the use of an indexing network can greatly facilitate exploratory information retrieval and personalized search.

A task' s end-to-end delay in its execution is a key requirement to real-time systems. This paper presents a configurable state class method based on time Petri nets for their quantitative analysis. The proposed method has a flexible state class structure. A firing domain is separated into a kernel domain that supports the basic evolution of state classes, and a configurable domain that is used to evaluate end-to-end delays. Since both domains adopt a uniform representation for time constraints, end-to-end delays can be computed synchronously with the evolution of state classes via the same firing rules. Firing rules are decomposed into basic timing operations. This treatment not only makes the calculation of end-to-end delays more flexible, but also provides a scalable way to add new timing operations into a time Petri net model. The proposed method computes arbitrary end-to-end delays along a trace with time O(ml2) and space O(l2), where m is the number of firing transitions along the trace and l is the maximum number of transitions in configurable and kernel domains. Compared with the existing state class methods, it has better performance and flexibility in on-the-fly computation of end-to-end delays.

The creation of value-added services by automatic composition of existing ones is gaining significant momentum as the potential silver bullet in service-oriented computing. A large number of composition methods have been proposed, and most of them are based on the matching of input and output parameters of services only. However, most services in the real world are not universally applicable, and some applicable conditions or restrictions are imposed on them by their providers. Such constraints have a great impact on service composition, but have been largely ignored by the existing methods. In this paper, they are discussed and defined, and a simple formal expression is adopted to describe them. Two novel concepts, called service intension and service extension, are presented, which allow one to divide the basic elements of a web service definition into two parts. Consequently, their use allows us to propose a constraint-aware service composition method in which service constraints are well taken care. The proposed solution includes a graph search-based algorithm and two novel preprocessing methods. A publicly available test set from ICEBE05 is used to evaluate and analyze the proposed methodology.

E-commerce and online shopping with a third-party payment platform have rapidly developed recently, and encountered many fault tolerance and security problems concerned by users. The causes of these problems include malicious behavior and imperfect business processes. The latter lead to the emergence of security vulnerabilities and loss of user funds which become more and more serious these years. We focus on the business process of e-commerce, and propose a formal model for constructing an e-commerce business process called an E-commerce Business Process Net. It integrates both data and control flows based on Petri nets. Rationality and transaction consistency are defined and validated to guarantee the transaction properties of an e-commerce business process. This paper offers a complete methodology for modeling and validating an e-commerce system with a third-party payment platform from the view point of a business process. Its use enables a designer to identify errors early in the design process and correct them before the deployment phase. In order to demonstrate the applicability and feasibility of the methodology, we have modeled and validated a real-world e-commerce business process and discovered the problems that cause the violation of transaction properties.

Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete event systems. Some extensions to PNs enhance their modeling capability. This work uses Time Petri nets (TPNs) and Synchronized Petri nets (SPNs) to design a traffic control system (ITCS) for intersections dealing with accidents such that emergency response is provided and additional accidents are prevented. It includes a camera surveillance subsystem (CSS) and a traffic light control subsystem (TLCS) simulated by using SPNs and TPNs, respectively: a multi-camera surveillance mechanism is established to sense and detect the accident, synchronously, and then according to the information of the accident, corresponding traffic light control policies are carried out to prevent additional accidents from happening. A reach ability tree method is adopted to demonstrate how the models are used to enforce the phase of traffic transitions, and verify their important properties. To our knowledge, this is the first work that employs PNs to model and design the real-time traffic control system for intersections facing accidents, and to perform the cooperation of cameras and traffic lights. This helps enhance the state of the art in real-time traffic accident detection and traffic safety at an intersection.

Owing to their rapid development, services are increasing rapidly in quantity. The consequence is that there are so many services that share the same or similar functions. Therefore, it is important to select a credible and optimal service. Reputation as one of the important parameters of services plays a significant role in the decision support for service selection. This paper proposes a novel two-phase method to calculate service reputation. The first phase uses a dynamic weight formula to calculate reputation such that it can reflect the latest tendency of a service. The second one uses an olfactory response formula to mitigate the negative effect of unfair ratings. Some experiments are conducted and the results validate the effectiveness of the proposed method.

A type of refinement operations of time Petri nets is presented for design, analysis and verification of complex real-time systems. First, the behavior preservation is studied under time constraints in a refinement operation, and a sufficient condition for behavior preservation is obtained. Then, the property preservation is considered, and the results indicate that if the refinement operation of time Petri nets satisfies behavior preservation, it can also preserve properties such as boundedness and liveness. Finally, based on the behavior preservation, a reachability decidability algorithm of a refined time Petri net is designed using the reachability trees of its original net and subnet. The research results are illustrated by an example of designing, analyzing and verifying a real-time manufacturing system.

The use of information technology and management systems for the betterment of health care is more and more important and popular. However, existing efforts mainly focus on informatization of hospitals or medical institutions within the organizations, and few are directly oriented to the patients, their families, and other ordinary people. The strong demand for various medical and public health care services from customers calls for the creation of powerful individual-oriented personalized health care service systems. Service computing and related technologies can greatly help one in fulfilling this task. In this paper, we present PHISP: a Public-oriented Health care Information Service Platform, which is based on such technologies. It can support numerous health care tasks, provide individuals with many intelligent and personalized services, and support basic remote health care and guardianship. In order to realize the personalized customization and active recommendation of intelligent services for individuals, several key techniques for service composition are integrated, which can support branch and parallel control structures in the process models of composite services and are highlighted in this paper.

With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to improve the efficiency of service discovery and composition is important. This paper proposes three categorization rules to classify services for a large scale repository to form a relational taxonomy. The service retrieve scope can be drastically narrowed by this taxonomy. Therefore, the efficiency of service discovery and service composition can be greatly improved. We evaluate and compare the performance of the proposed method and other related ones via a publicly available test set, ICEBE05. The experimental results validate the effectiveness and high efficiency of the proposed one.

The information technology has been recognized as one of the most important means to improve health care and curb its ever-increasing cost. However, existing efforts mainly focus on informatization of hospitals or medical institutions within organizations, and few are directly oriented to individuals. The strong demand for various health services from customers calls for the creation of powerful individual-oriented personalized health care service systems. Web service composition (WSC) and related technologies can greatly help one build such systems. This paper aims to present a newly developed platform called a Public oriented Health care Information Service Platform (PHISP) and several novel WSC techniques that are used to build it. Among them include WSC techniques that can well support branch and parallel structures.

A deadlock-checking approach for one-place unbounded Petri nets is presented based on modified reachability trees (MRTs). An MRT can provide some useful information that is lost in a finite reachability tree, owing to MRT's use of the expression a + bn(i) rather than symbol omega to represent the value of the components of a marking. The information is helpful to property analysis of unbounded Petri nets. For the deadlock-checking purpose, this correspondence paper classifies full conditional nodes in MRT into two types: true and fake ones. Then, an algorithm is proposed to determine whether a full conditional node is true or not. Finally, a necessary and sufficient condition of deadlocks is presented. Examples are given to illustrate the method.

The current stepwise refinement operation of Petri nets mainly concentrates on property preservation, which is an effective way to analyze and verify complex systems. Further steps into this field are needed from the perspective of system synthesis and language preservation. First, the refinement of Petri nets is introduced based on a$k$-well-behaved Petri net, in which$k$tokens can be processed. Then, according to the different compositions of subsystems, well-, under- and overmatched refined Petri nets are proposed. In addition, the language and property relationships among sub-, original, and refined nets are studied to demonstrate behavior characteristics and property preservation in a system synthesis process. A manufacturing system is given as an example to illustrate the effectiveness of the proposed approach in synthesizing and analyzing the Petri nets of complex systems.

This paper presents an AI planning method for Web service automatic composition using Predicate/Transition (Pr/T) net model. First, based on Web service description of inputs, outputs, preconditions and effects in OWL-S specification, a Pr/T net model is constructed for a service composition plan. Then, this plan is effectively solved by a reachability algorithm of the Pr/T net and a corresponding reachability graph is obtained. Moreover, a regular language of Pr/T net is generated for representing all plan paths of a service composition. Finally, a process model of composite service is extracted after normalization of the plan paths. This method provides a complete solution from modeling AI plan to constructing process model for service composition, so it is helpful for realizing automatic composition and dynamical integration of Web service.

This paper introduces an automatic Web service composition method based on logical inference of Horn clauses in Petri net models. The available services and user request described in SA WSDL are translated into a set of Horn clauses and the composability rules of the services' input/output parameters are established using ontology reasoning. We choose Petri net as the model of this set of Horn clauses. T-invariant method of Petri nets is used to determine the existence of composite Web services that can fulfill user's requirement.

In this paper a genetic algorithm-based optimization procedure for ontology matching problem is presented as a feature-matching process. First, from a global view, we model the problem of ontology matching as an optimization problem of a mapping between two compared ontologies, and every ontology has its associated feature sets. Second, as a powerful heuristic search strategy, genetic algorithm is employed for the ontology matching problem. Given a certain mapping as optimizing object for GA, fitness function is defined as a global similarity measure function between two ontologies based on feature sets. Finally, a set of experiments are conducted to analysis and evaluate the performance of GA in solving ontology matching problem

Refinement operation of workflow nets is provided in this paper for modeling and analyzing integrated workflow. Structure, dynamic properties and behavior expression of refined workflow net are also discussed. These works proves that refinement operation with step-by-step refinement of transitions could realize hierarchical modeling of workflow as well as composite modeling of workflow integration Furthermore, the refinement operation can reduce complexity of model analysis. In fact the reliable refined nets satisfy soundness, and dynamic behavior of refined Petri nets was consistent with of original nets and subnets. Therefore the properties analysis and verification of refined workflow nets can be realized by properties of subnets using refinement operation. Moreover, the research results are successfully applied to designing, modeling and verification of layered workflows and their integration in Shanghai Traffic Information Service Application Grid (STISAG).