Distributed Environment Research Articles

A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery

Distributed heterogeneous factory environments have become the mainstream in real-world manufacturing enterprises. Scheduling the assembly no-idle flowshops in such distributed heterogeneous environments is significant for practitioners. This problem takes into account the heterogeneity between different factories and the batch delivery process. To minimize inventory and tardiness costs of this new problem, a novel Q-learning-based multi-population multi-objective evolutionary algorithm is proposed. The algorithm incorporates five problem-specific properties to design the solution representation and calculate objectives. To obtain high-quality initial dual populations, a collaborative initialization technique combining four NEH heuristics and a completely random approach is proposed. To enhance algorithm’s search efficiency, a Q-learning-based selection method is developed to coordinate four job-related and four product-related operators. The dual-cooperation strategy is introduced to improve the global search capability. Experimental results demonstrate the effectiveness of all the designed components, indicating that the proposed algorithm outperforms five existing algorithms. It is well-suited for addressing real-world distributed heterogeneous scheduling scenarios by the proposed algorithm.

Construction of supply chain management information system based on networked web service composition technology

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods.

MallobSat: Scalable SAT Solving by Clause Sharing

SAT solving in large distributed environments has previously led to some famous results and to impressive speedups for selected inputs. However, in terms of general-purpose SAT solving, prior approaches still cannot make efficient use of a large number of processors. We aim to address this issue with a complete and systematic overhaul of the distributed solver HordeSat with a focus on its algorithmic building blocks. In particular, we present a communication-efficient approach to clause sharing, careful buffering and filtering of produced clauses, and effective orchestration of state-of-the-art solver backends. In extensive evaluations, our approach named MallobSat significantly outperforms an updated HordeSat, doubling its mean speedup. Our clause sharing results in effective parallelization even if all threads execute identical solver programs that only differ based on which clauses they import at which times. We thus argue that MallobSat is not a portfolio solver with the added bonus of clause sharing but rather a clause-sharing solver where adding some explicit diversification is useful but not essential. We also discuss the last four iterations of the International SAT Competition (2020–2023), where our system ranked very favorably, and identify several previously unsolved competition problems that MallobSat solved successfully. Last but not least, our approach is malleable, i.e., supports running on a fluctuating set of resources, which allows us to combine parallel job processing and parallel SAT solving in a flexible manner for best resource efficiency

WIDESim: A Toolkit for Simulating Resource Management Techniques Of Scientific Workflows in Distributed Environments with Graph Topology

WIDESim: A Toolkit for Simulating Resource Management Techniques Of Scientific Workflows in Distributed Environments with Graph Topology

BSSN-SDNs: A Blockchain-Based Security Service Negotiation for the SDN Interdomain

The security requirements for SDN (Software-Defined Network) cross-domain communication are diverse and dynamically changing; thus, a security service negotiation function is required for the SDN interdomain. However, the SDN interdomain distributed communication environment leads to a lack of trustworthiness and security. Therefore, this paper proposes a blockchain-based SDN interdomain security service negotiation mechanism, BSSN-SDNs, to provide automatic, secure, and trustworthy SDN interdomain security service negotiation. BSSN-SDNs proposes a three-layer reference architecture that enables joint on-chain and off-chain work by extending the security service negotiation module and blockchain client on the controller and deploying security service negotiation smart contracts on the blockchain. It especially adopts non-interactive key exchange and the message authentication code to ensure the confidentiality of the secure service negotiated on-chain. Finally, the timeliness as well as security and trustworthiness of BSSN-SDNs are analyzed, and the FISCO BCOS-based experiment results show that the delay of BSSN-SDNs is acceptable and is positively correlated with the number of policies and the number of SDN domains involved in negotiation.

Adaptive context-aware access control for IoT environments leveraging fog computing

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.

The Diagnosis-Effective Sampling of Application Traces

Distributed tracing is cutting-edge technology used for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and the direct storage and utilization of every trace is impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as the simplification of application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine-learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Software maintenance practices using agile methods towards cloud environment: A systematic mapping

AbstractAgile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on‐the‐premise) or global (distributed) environment. In a local environment, the software maintenance team is co‐located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well‐known digital databases and examined the articles that map to the pre‐defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP‐ and Scrum‐based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges.

Federated continual representation learning for evolutionary distributed intrusion detection in Industrial Internet of Things

As a promising paradigm, Federated Learning (FL)-based distributed intrusion detection offers potent protection for the network security of Industrial Internet of Things (IIoT) systems. Nonetheless, the practical deployment of IIoT systems occurs in a highly-complex and dynamic distributed environment. The ever-growing and dynamically-evolving cyber attacks will render the FL-based intrusion detection model inefficient, since FL cannot gracefully learn from the dynamic traffic data streams to identify new attacks. To address this issue, we propose the evolutionary distributed intrusion detection system based on federated continual representation learning, designed to continually capture effective feature representations of emerging attacks from dynamic traffic data streams. Specifically, we develop the supervised contrastive loss and the global information-aware regularization loss to alleviate the catastrophic forgetting on the previously observed attacks and mitigate the data heterogeneity across clients. Besides, we propose the prototype variance-based memory update strategy to ensure the effective memory replay data. Extensive experimental results demonstrate our proposed method outperforms the state-of-the-art methods by 13.3%–31.5% in terms of average accuracy on a real energy intrusion detection dataset.

Privacy Preserving Authentication of IoMT in Cloud Computing

INTRODUCTION: The Internet of Medical Things (IoMT) blends the healthcare industry with the IoT ecosystem and enables the creation, collection, transmission, and analysis of medical data through IoT networking. IoT networks consist of various healthcare IT systems, healthcare sensors, and healthcare management software. OBJECTIVES: The IoMT breathes new life into the healthcare system by building a network that is intelligent, accessible, integrated, and effective. Privacy-preserving authentication in IoMT is difficult due to the distributed communication environment of heterogeneous IoMT devices. Although there has been numerous research on potential IoMT device authentication methods, there is still more to be done in terms of user authentication to deliver long-term IoMT solutions. However, password handling is one of the big challenges of IoMT. METHODS: In this paper, we present an IoMT-related online password-less authentication technique that is quick, effective, and safe. In order to offer cross-platform functionality, the article includes a simulation of FIDO2/WebAuthn, one of the most recent standards for a password-less authentication mechanism. RESULTS: This makes it easier to secure user credentials and improve them while preserving anonymity. The IoMT device authentication process and registration process delays are also assessed. CONCLUSION: Results and simulations show that the efficacy of the proposed mechanism with quick authentication on cloud servers may be accomplished with the fewest registration and authentication procedures, regardless of device setup.

Data Structure Profiler Using MERN Technology

Profiler introduces a cutting-edge approach to efficiently manage and track Data Structures and Algorithms (DSA) in software development projects. In the realm of modern software engineering, where complexity is ubiquitous and scalability is critical, effective management of DSAs is essential for ensuring the robustness, maintainability, and performance of software systems. DSA Profiler seamlessly integrates into the development lifecycle, offering developers a centralized platform to meticulously document, organize, and monitor the implementation of various data structures and algorithms within their codebase. Its intuitive interfaces and powerful functionalities facilitate the process of incorporating, modifying, and optimizing DSAs. By providing detailed insights and analytics, DSA Profiler empowers developers to make informed decisions about the most appropriate data structures and algorithms for their specific needs, ultimately streamlining the development process and significantly enhancing code quality. One of the standout features of DSA Profiler is its ability to benchmark data structures and algorithms, allowing developers to evaluate their performance under different conditions. This benchmarking capability ensures that developers can identify and address performance bottlenecks early in the development cycle. Furthermore, This collaborative environment helps maintain consistency and adherence to standards across projects, which is particularly beneficial in large-scale and distributed development environments. Keywords - data structure benchmarking, data structure optimization, algorithm performance tracking, software scalability, development lifecycle integration, codebase management, performance analytics, collaborative development tools, real-time optimization recommendations.

GA Based Task Allocation In Agile Software Development In Distributed Environment

GA Based Task Allocation In Agile Software Development In Distributed Environment

Dynamic round robin with adaptive time quanta (DRRATQ): A proposed algorithm

The CPU scheduling technique influences the performance and efficiency of operating systems. Round-robin scheduling algorithm is ideal for time-shared systems, but it is not optimal for real-time operating systems since it yields more context switching, longer waiting time, and high turnaround time. The performance of the algorithm is predominantly influenced by the designated time quantum; however, determining a suitable time quantum is extremely challenging. This paper presents a CPU scheduling algorithm that provides a better tradeoff between waiting time, turnaround time, response time, and number of context switch by using hypothesis-based quanta generation approach. It combines the CPU burst requirements of actual processes with some noisy data and plots them against the presumed CPU quanta to get quanta densities so that a polynomial regression model can fit the data points with the highest adjusted R-squared. Then applying some complex inferential statistic, the required quanta is obtained. The scheduling is dynamic in nature because it generates the next CPU quanta in reference to the quanta that have been used in the previous cycle with remaining CPU burst requirements of the process, and it is also adaptive in nature because, at each cycle, it uses ‘d’ (5, 5, 4, 3, 2) degree of freedom to calculate the Jarque-Bera Statistics to accept/reject the hypothesis. The algorithm is implemented in ‘R’ and the performance has been evaluated on a sample size of five processes with some noisy data which outperforms the conventional RR and significantly reduces the performance parameters mentioned above. Implementing this algorithm to a time-sharing or distributed environment will undoubtedly improve system performance and will help to avoid issues like thrashing, incorporate aging, CPU affinity, and starvation. Since the proposed algorithm is work-conservative, therefore can be implemented in network packet switching, statistical multiplexing, and real-time systems.

Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contribution is the development of an innovative grouping algorithm for edge clients. This algorithm employs a distinctive ID distribution function, enabling efficient and secure grouping of both normal and potentially malicious clients. Our proposed grouping scheme accurately determines the numerical difference between normal and malicious groups under various network scenarios. Our method addresses the challenge of model poisoning attacks, ensuring the accuracy of outcomes in a collaborative federated learning framework. Our numerical experiments demonstrate that our grouping scheme effectively limits the number of malicious groups. Additionally, our collaborative FL framework has shown resilience against various levels of poisoning attack abilities and maintained high prediction accuracy across a range of scenarios, showcasing its robustness against poisoning attacks.

Discovery of Actionable Patterns Through Scalable Vertical Data Split Method with Meta Actions and Information Granules

Action Rules are rule based systems that extract actionable patterns which are hidden in big volumes of data. Users need recommendations on actions they can undertake to increase their profit or accomplish their goals, this recommendations are provided by Actionable patterns. In the technological world of big data, massive amounts of data are collected by organizations, including in major domains like financial, medical, social media and Internet of Things(IoT). To analyze and store such a massive amount of data, distributed computing frameworks like Hadoop and Spark are introduced to store the big data in a distributed fashion which manage and analyze them in parallel. The traditional Action Rules extraction models, which analyze the data in a nondistributed fashion, do not perform well when dealing larger datasets. Serious complications of discovering Action Rules with such distributed environments are - data distribution among computing nodes and calculation of major parameters including : support, confidence, utility, and coverage, that represent the whole data. Information granules form basic entities in the world of Granular Computing(GrC), which represents meaningful smaller units derived from a larger complex information system. In this research, we focus on the data distribution phase of the distributed Actionable Pattern Mining problem. To handle the data distribution task by splitting the big data in both horizontal and vertical fashions - we propose partition threshold rho. In this work, we concentrate on using information granules to implement a vertical data splitting strategy with Meta Actions. Hence our results discover valuable Actionable Knowledge with application in Business and Education domains.

ChainAgile: A framework for the improvement of Scrum Agile distributed software development based on blockchain.

Software Development based on Scrum Agile in a distributed development environment plays a pivotal role in the contemporary software industry by facilitating software development across geographic boundaries. However, in the past different frameworks utilized to address the challenges like communication and collaboration in scrum agile distributed software development (SADSD) were notably inadequate in transparency, security, traceability, geographically dispersed location work agreements, geographically dispersed teamwork effectiveness, and trust. These deficiencies frequently resulted in delays in software development and deployment, customer dissatisfaction, canceled agreements, project failures, and disputes over payments between customers and development teams. To address these challenges of SADSD, this paper proposes a new framework called ChainAgile, which leverages blockchain technology. ChainAgile employs a private Ethereum blockchain to facilitate the execution of smart contracts. These smart contracts cover a range of functions, including acceptance testing, secure payments, requirement verification, task prioritization, sprint backlog, user story design and development and payments with the automated distribution of payments via digital wallets to development teams. Moreover, in the ChainAgile framework, smart contracts also play a pivotal role in automatically imposing penalties on customers for making late payments or for no payments and penalties on developers for completing the tasks that exceed their deadlines. Furthermore, ChainAgile effectively addresses the scalability limitations intrinsic in blockchain technology by incorporating the Interplanetary File System (IPFS) is used for storage solutions as an off-chain mechanism. The experimental results conclusively show that this innovative approach substantially improves transparency, traceability, coordination, communication, security, and trust for both customers and developers engaged in scrum agile distributed software development (SADSD).

Towards a Decentralized Blockchain-Based Resource Monitoring Solution For Distributed Environments

The increasing number of connected users and devices to Cloud, Fog, and Edge environments encouraged the creation of many applications and services in the most varied areas and domains. Such services are highly distributed on top of heterogeneous infrastructures that require real-time monitoring. The monitoring process may be considered a complex task since it requires experienced users and robust cloud-based solutions to support the most varied needs in such scenarios. The main problem relies on the centralization of the monitoring approaches for cloud-centric solutions that represent a central point of failure in end-to-end communication, compromising the application's security and performance in case of high latency or downtime. In this context, blockchain networks enable exciting features such as decentralization, immutability, and traceability with higher security levels. This work is towards a blockchain-based and decentralized resource monitoring solution for distributed environments. The proposed solution integrates blockchain technology to continuously monitor, store, and safely broadcast Operating System performance counters in a highly decentralized fashion. The results demonstrated that a blockchain-based monitoring tool based on Smart Contract is feasible and that it may serve as an entry point for varied solutions for monitoring, security, scheduling, and so on.

Discrete control for state of charge balance in DC microgrids considering the disturbance of photovoltaics

This study examines State of Charge (SoC) balancing control in DC microgrids subject to photovoltaic (PV) fluctuations, aiming to optimize power distribution in energy storage systems influenced by PV disturbances. The proposed approach enhances both the lifespan of storage systems and microgrid stability. To mitigate voltage variation due to PV perturbation, the paper introduces an adjustment in droop control offset. Additionally, it presents a novel discrete Sliding Mode Controller (SMC) characterized by reduced parameter sensitivity, thus enhancing control responsiveness. A SoC balancing control strategy employing sliding mode control is developed to equalize SoC levels across Battery Energy Storage Systems during both charge and discharge cycles. The stability of this strategy is substantiated through the construction of a Lyapunov function. Simulations conducted in a distributed DC microgrid environment using Simulink/SimPower Systems demonstrate the efficacy of the discrete SMC and the SoC balancing algorithm, achieving uniform SoC in energy storage nodes during operation, with improved robustness against PV perturbations.

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.