Autoscaling is a technology that automatically scales resources for applications without human intervention to ensure runtime Quality of Service (QoS) while reducing costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variability and contain bursts, posing challenges to autoscaling in maintaining QoS within Service-Level Objectives (SLOs). Conservative strategies risk over-provisioning, while aggressive ones may cause SLO violations, making it more challenging to design effective autoscaling. This paper introduces BASE, a burst-adaptive autoscaling framework that leverages a stacked ensemble of machine learning models to mitigate SLO violations and reduce costs for containerized services and applications operating under time-varying workloads. BASE incorporates a novel prediction-based burst detection mechanism that distinguishes between predictable workload spikes and actual uncertain bursts. When bursts are detected, BASE appropriately overestimates them and allocates resources accordingly to address the rapid growth in resource demand. On the other hand, BASE employs reinforcement learning to rectify potential inaccuracies in resource estimation, enabling more precise resource allocation during non-burst periods. Experiments across ten real-world workloads demonstrate BASE's effectiveness, achieving a significant reduction in SLO violations with lower resource costs compared to other prominent methods.

Edge-cloud environments constitute a heterogeneous computing paradigm that integrates resource-constrained edge servers with high-performance cloud servers. While microservices have revolutionized large-scale applications development by enhancing scalability and flexibility, achieving efficient microservice autoscaling—the dynamic adjustment of instances to maintain quality of service (QoS) and meet service-level agreements (SLA) targets—remains challenging in such environments. Most existing autoscaling techniques rely on metric forecasting and centralized or per-node control, causing them to overlook temporal evolution and server relationships and resulting in unstable and weakly coordinated scaling. To address these challenges, we present SynScale, a distributed collaborative autoscaling framework that strengthens both temporal sensitivity and structural coordination. SynScale introduces a spatiotemporal representation module that couples a temporal attention network with a multi graph convolutional model. The temporal component distills behavioral trends from recent observations to ensure coherent responses to time-varying dynamics, while the spatial component performs relational reasoning over explicitly modeled inter server correlations to yield structure-aware representations that support effective cross-node coordination. These spatiotemporal embeddings are then used within a multi-agent reinforcement learning paradigm, enabling distributed agents to generate context-aware scaling decisions that align local adaptability with system-wide efficiency. Experimental evaluations against state of-the-art autoscaling techniques show that SynScale reduces average response time by 59.32%, SLA violations by 85.71%, and P95 latency by 73.2%, while also lowering resource cost. It further improves scaling stability—reflected by lower instance time and longer instance lifetimes—and maintains lightweight, scale-stable runtime overhead, ensuring practical deployability in heterogeneous edge-cloud environments.

Serverless computing, an emerging paradigm, bolsters operational efficiency and cost savings by enabling the dynamic execution of fine-grained functions through a Function as a Service (FaaS). It incorporates autoscaling, thereby eliminating the need for infrastructure management. However, the conventional autoscaling strategies employed by cloud service providers frequently result in cold starts and inefficiencies. The complexity of variable workloads and the intricate interdependencies between functions amplify this challenge. For the above, this paper introduces FuncScaler, a deep learning- enhanced, cold-start-aware, holistic autoscaling approach for FaaS. FuncScaler employs a Gated Recurrent GCN to capture spatiotemporal relationships among functions, aggregating neighbour information and controlling information flow. Additionally, the framework integrates a cold-start-aware Jackson Queuing Network (JQN), which utilizes predicted workloads to initiate pre-warming and holistically autoscales. This empowers FuncScaler to reveal deeper function relationships, precisely forecast workloads, and concurrently reconfigure interconnected resources, effectively mitigating cold starts and hotspots. Our study shows FuncScaler excels beyond five comparative methods in ensuring Quality of Service (QoS) and enhancing resource utilization, additionally reducing cold start latency by 42.3% compared to the second-best approach.

The improvement of consensus algorithms has greatly enhanced the performance of consortium blockchain, making it possible to be applied in large-scale network scenarios such as finance, healthcare and supply chain management. However, better performance often results in a heavier storage burden on the nodes. A storage strategy based on erasure code is a good solution, capable of reducing the storage consumption of each block to O(1). However, in large-scale consortium blockchain, this storage strategy encounters issues like poor performance and limited system dynamicity. To address the issues associated with the application of coding-based storage strategies in large-scale consortium blockchain, this paper proposes a storage strategy based on reputation-layered coding, named Level-Store. Level-Store first partitions the nodes into different storage units based on their reputations, then allocates the storage tasks of different block segments to different storage units for encoded storage. By calculating reasonable storage schemas for each storage unit, the data availability is ensured. Experiment shows that compared to existing coding-based storage strategies, Level-Store significantly improves system performance and dynamicity. It demonstrates notable advancements in key metrics such as space overhead, time consumption for storage and retrieval, the number of nodes and chunks affected by re-encoding, and the time consumption for re-encoding. Additionally, the system’s security and fairness have also been enhanced.

Summary Currently, inter‐organizational process collaboration (IOPC) has been widely used in the design and development of distributed systems that support business process execution. Blockchain‐based IOPC can establish trusted data sharing among participants, attracting more and more attention. The core of such study is to translate the graphical model (e.g., BPMN) into program code called smart contract that can be executed in the blockchain environment. In this context, a proper smart contract plays a vital role in the correct implementation of block‐chain‐based IOPC. In fact, the quality of graphical model affects the smart contract generation. Problematic models (e.g., deadlock) will result in incorrect contracts (causing unexpected behaviors). To avoid this undesired implementation, this article explores to generate smart contracts by using the verified formal model as input instead of graphical model. Specifically, we introduce a prototype framework that supports the automatic generation of smart contracts, providing an end‐to‐end solution from modeling, verification, translation to implementation. One of the cores of this framework is to provide a CSP#‐based formalization for the BPMN collaboration model from the perspective of message interaction. This formalization provides precise execution semantics and model verification for graphical models, and a verified formal model for smart contract generation. Another novelty is that it introduces a syntax tree‐based translation algorithm to directly map the formal model into a smart contract. The required formalism, verification, and translation techniques are transparent to users without imposing additional burdens. Finally, a set of experiments shows the effectiveness of the framework.

The emerging edge-cloud computing paradigm, comprising cloud centers and multiple distributed edge servers, extends the computing capability from the cloud center to a range of servers. Although the microservice autoscaling problem has been intensively studied in the context of cloud computing, existing algorithms in most cases cannot be effectively migrated to the edge-cloud environment because servers are geographically distributed and heterogeneous, and information is not synchronized between servers. Existing works, however, mainly focus on centralized strategies with time-consuming synchronization methods, i.e. strategies shared by all servers, without comprehensively considering the heterogeneity and distribution of the environment. Soft information synchronization, autonomy and collaboration is proposed to tackle the aforementioned issues, and refer to it as SAC paradigm. According to the SAC paradigm, each server with inferred information of other servers can collaborate with others by a dedicated autoscaling strategy, that is, server collaboration. The microservice autoscaling problem is then transformed into the Graph-based Jointly Microservice Autoscaling (GJMA) problem based on spectral graph theory. GJMA problem aims to minimize average waiting time of microservice-based application while reducing service-level agreement(SLA) violation rate and fluctuations in the autoscaling process, taking into account resource heterogeneity. Graph-based Multi-agent Algorithm(GMA), an implementation of SAC paradigm based on graph convolutional networks and multi-agent reinforcement learning, is implemented to solve GJMA problem. Experimental results show that the proposed algorithm for the edge-cloud environment is always efficient to find a better autoscaling strategy compared to the implemented comparison algorithms.

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.

Social data produced from widely emerged social media activities are expected to promote information dissemination and engagement, or even make business intelligence more powerful. However, the recent increase in social media incidents of illegal surveillance and data breaches raises questions about the current data ownership model, in which centralized applications collect and control large amounts of user data. In this paper, we present SocialChain, which is a decentralized social data storage and sharing system based on blockchain that decouples user data and social applications to return data ownership to the user. We adopt Personal Data Store to extend off-chain storage for the social data, set up an identity establishment mechanism that can support WebID-based authentication functions using a unique identity assignment (i.e., WebID) as well as certificateless cryptography, and design a general framework that leverages smart contracts to help securely store and share social data in an automated manner. We develop a software prototype based on Ethereum and conduct case studies to test the effects of the adopted techniques on the performance. Experimental results show that SocialChain can provide easy-to-use interfaces while introducing relatively low latency, cost, and overhead and that it can support real-world social media applications.

The elastic cloud applies autoscaling technology to allow users to automatically provision or deprovision resources on demands, attracting many application providers to migrate their applications to the cloud. However, autoscaling multi-service applications are still challenging due to the complex correlations among services. This paper presents HRA, an intelligent, holistic resource autoscaling framework for multi-service applications, utilizing model-based deep reinforcement learning (DRL), mitigating service-level agreements (SLA) violations while saving costs. HRA (i) leverages historical telemetry data and machine learning methods to build a simulated environment adaptively, modeling relations between resources, workloads and performance, (ii) exploits the environment model to drive up training efficiency of DRL agent, and (iii) uses the agent to automatically take actions to scale resources online based on simple low-level features from a monitor instead of elaborate high-level features that are representing the complex correlations and needing much sophisticated prior knowledge. Experiments (i) evaluated the fidelity of the proposed (simulated) environment modeling method, (ii) evaluated the reliability of the resource allocation policy from the simulation to reality, and (iii) compared related autoscaling methods. The evaluation results demonstrate that HRA realizes a more effective resource allocation policy under the limited number of time-consuming interactions and significantly decreases the 32-92% in SLA violation rate at a lower cost compared to other main methods.

AI-based process model analysis has attracted more and more interest. Model quality is crucial for such research. At present, inter-organizational business process (IOBP) has been widely used in the model design and development of the distributed system. Before implementing the intelligent analysis of the IOBP model, conformance as a foundation for model quality checking plays a key role because it ensures in advance that the participants can successfully interact without violating the global communication constraints imposed by the choreography. In fact, the multi-instance participant is a common requirement in IOBP. This paper provides a formal approach and framework supporting the conformance between BPMN choreography and collaboration while considering multi-instance participants and message communication modes. As a core, the formalization proposed is based on BNF syntax and structured CSP# processes. It can well support multi-instance features and multiple communication modes. Combined with CSP#, the formal definitions of communication modes and verification properties are given. On this basis, an integrated framework is provided to support automated formal verification referring to multiple communication modes. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposal.

Container-supported microservice technology is widely used in cloud applications. For elastic cloud, it's vital to maintain application response time within service-level agreements (SLA) by auto-scaling technology. For applications composed of multiple services (i.e. multi-service applications), due to complex topologies, there are many factors that reduce auto-scaling algorithm performance, such as correlations among services, untimely decision, oversupply, etc. To resolve this, we propose a holistic auto-scaling algorithm (HAB) based on balanced Jackson queuing network (JQN) to reduce SLA violations rapidly with less resource cost. With the holistic auto-scaling strategy, HAB scales all services quickly and accurately. Keeping the balanced state among services, HAB saves resource cost, reduces auto-scaling decision space and simplifies algorithm parameters. The experimental results demonstrate that HAB has an average decrease of 42.31% in SLA violation rate, an average decrease of 17.88% in resource cost and an average increase of 19.39% in stability, compared with other main methods.

Due to the lightweight and flexible characteristics, containers have gradually been used for the application deployment and the basic unit for resource allocation in a cloud platform recently. Reinforcement learning (RL), as a classic algorithm, is widely used in virtual machine scheduling scenarios due to its advantages of adaptability and robustness. However, most RL methods have problems in container scheduling, such as untimely scheduling, lack of accuracy in decision-making and poor dynamics that will lead to a higher SLA violation rate. In order to solve the above problems, a predictive RL algorithm A-SARSA is proposed, which combines the ARIMA model and the neural network model. This algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads. Through a large number of experiments, the timeliness and effectiveness of the A-SARSA algorithm for container scheduling are verified, which can reduce the SLA violation rate dramatically while keeping the resource utilization rate at a good level.

High performing teams may benefit the execution efficiency of business processes. In executing a process, the performance of the team is concerned with the individual expertise of team members and the handover relations between executors of adjacent tasks of the process. Team formation problem in the presence of business process can be defined as finding a group of individuals to execute all tasks of the process. Considering individual expertise and handover relations, a method called Bayesian Network based Team Formation (BN-TF) is proposed to address the team formation problem in business process context. Using BN-TF, a team formation problem is first modeled as a Most Probable Explanation (MPE) problem in Bayesian network based on the structural information of the related business process. For solving the transformed MPE problem, we design an improved genetic algorithm called Forward-Backward Greedy Genetic Algorithm (FBG-GA). Experimental results on simulation data verify that BN-TF indeed produces teams that satisfy the requirements while improve the execution efficiency of the business process. Compared with existing methods, RarestFirst and CoverSteiner, which focus on minimizing team communication cost, BN-TF shows improvement in terms of individual expertise and handover relations.

ABSTRACT Virtual organization (VO) is a main organizational paradigm for enterprises to collaborate in the rapidly changing environment. However, finding partners for VO creation has great challenges on Internet, because a VO initiator is blinded without enough partners' information. The problem of VO creation in cloud environment is discussed. A VO creating algorithm (called Group‐Choose) based on reputation system is presented to help initiator minimize the operating risk on Internet. In the algorithm, a VO initiator aggregates partners' trust evaluations of candidates to select new partners for VO, instead of evaluating candidate's trust only by itself. The third‐party assessment and Time Slide Window are used in peer‐to‐peer trust evaluation to make the model more adaptive in the dynamic environment. The proposed method focuses on the reputation data, without taking other factors into consideration. Compared with PathTrust and PeerTrust, the Group‐Choose algorithm for VO creation has better performance in resisting conspiracy attack and periodic attack. Copyright © 2013 John Wiley & Sons, Ltd.

Most of the existing trust models don't perform well in preventing malicious nodes from profiteering by conspiracy attack or periodic attack, and pay little attention to incentive mechanisms in the environment of Virtual Organization (VO). This paper proposes a new distributed trust mechanism in Single-core VO, which chooses the new node by Group-Choose mechanism that aggregates direct trust (Direct Trust) and 3rd-party assessment from every group member, in order to solve the problem of selecting a specific set of services. The Direct Trust iteratively calculates trust value with a dynamic adaptive parameter. The 3rd-party assessment method is based on credibility of evaluation. Group-Choose mechanism aggregates trust evaluations of the central node and existing members in the group to select the next member. Compare with Path Trust and Peer Trust, Group-Choose mechanism has better performance in resisting conspiracy attack and periodic attack and increasing the group success rate.

Eliciting and describing complex business process consistently and unambiguously is important and critical for migrating complex business processes to cloud platform. Mspoa(Subject Predicate Object Adverbial complex business process description Meta-model) is proposed in this paper, which can represent the static relationship in business process description problem space. Based on Mspoa, clewed and cored by form business process, CBPM (Complex Business Process Model) is presented to describe dynamic behavior relationship. By the means of the migration steps described in this paper, incoherence and bounce in the course of business processes analysis can be overcome, assuring the correctness of the artifacts.

P2P reasoning system can answer queries from not only each peer's local theory but also some other peers related with sharing part of its vocabulary. This paper concentrates on P2P reasoning technology based on description logic, trying to provide an intelligent solution for searching network resources. A P2P reasoning algorithm suitable for description logic called DL-DeCA is proposed in this paper, as well as its communication protocol and system implementation of DL-P2PRS (description logic P2P reasoning system). Experiment results show that DL-P2PRS can work correctly and effectively.

Abstract Resource allocation of workflow has a direct impact on the average execution time of process instances and the cost of an enterprise. Through simulation optimization, resource allocation can be optimized to increase throughput. In the current research, mathematical analysis‐based optimization methods are used to solve the resource allocation problem. These methods are restricted in their application scope. Based on an analysis of the relationship between resource allocation and process instance response time, heuristics for workflow resource simulation optimization through tabu search is proposed in this paper. Experiment shows that it can remarkably improve the efficiency and result of the optimization. Copyright © 2011 John Wiley & Sons, Ltd.

Based on an analysis of related work about time exception in workflow, an algorithm for time exception handling of temporal workflow is presented in the paper. The algorithm is to meet the overall deadline of the case via an approach of cutting down the slack time of remaining activities, when there is a time exception. Meanwhile, details about the adjusting strategy for various kinds of routing constructions are also discussed. Finally, contrast experiment is used to show the effectiveness of the algorithm.