Distributed Management Framework Research Articles

Hierarchical and distributed energy management framework for AC/DC hybrid distribution systems with massive dispatchable resources

Hierarchical and distributed energy management framework for AC/DC hybrid distribution systems with massive dispatchable resources

Distributed risk-constrained strategy management of cloud energy storage for residential and electricity market services provision

Energy storage systems can improve the performance of home energy management systems and also provide ancillary services in electricity markets. However, the economic and technical insufficiency of individual home storage units, makes employing them inefficient for domestic storage and market applications, respectively. Therefore, central storage units with capacity sharing capability to small consumers as Cloud Energy Storages (CESs) are becoming a promising solution, and are also potentially able to contribute to ancillary service provision in the market. In this context, this paper aims to develop a novel stochastic day-ahead distributed management framework for CES to simultaneously adopt the optimal strategy of storage allocation for photovoltaic-integrated homes and participation in energy and ancillary service markets. The developed distributed framework is formulated within the Benders decomposition approach in which CES strategy and home energy management systems are optimized through master and subproblems based on linear mathematical models, respectively. Furthermore, the uncertainty of market prices is handled by the Conditional Value at Risk (CVaR) method. The developed model is assessed through real-world case studies of ERCOT. The results highlight the effectiveness of the collaborative service framework for both CES and households. Energy arbitrage through local storage services compensates for CES's challenges such as degradation and low power rating in the provision of energy services. These improvements benefit CES owners with higher net revenues and households with reduced cost of electricity supply.

MapChain-D: A Distributed Blockchain for IIoT Data Storage and Communications

With the rapid growth of the Industrial Internet of Things (IIoT) devices, managing extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralised data centres suffer from high latency for large-scale IIoT data storage due to the increased communications and latency overheads, distributed storage frameworks such as blockchains have become promising solutions. In this paper, we design and analyse a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> , consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is designed for practical IIoT applications with storage, latency, and communications constraints. Detailed data exchange protocols are presented for the data insertion and retrieval operations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> . Based on these, theoretical analyses are provided on the space, time, and communications complexities of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> compared with conventional single-chain frameworks with local and distributed data storage. We implement our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> prototype using open-source LoRaWAN communications with multiple RPi and Arduino devices, Kademlia-based distributed hash table (DHT), and Ethereum-based blockchain with proof-of-authority (PoA) consensus. Experimental results from our prototype show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> is more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MapChain-D</i> with different numbers of edge nodes in the system.

An Efficient SDFRM Security System for Blockchain Based Internet of Things

Blockchain has recently sparked interest in both the technological and business firms. The Internet of Things's (IoT) core principle emerged due to the connectivity of several new technologies, including wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing (CC), Cyber-Physical System (CSP), Information and Communication Technologies (ICT) and Enterprise Model, and other technological innovations into the fourth industrial revolution referred to as Industry 4.0. Data management is defined as the process of accumulation in order to make better business decisions, and process, secure and store information about a company. In the incipient model, there are interconnected contrivances and Machine-to-Machine (M2M) interactions, and transaction data are stored on the Blockchain. Security is a challenging aspect that must be punctiliously considered during the design and development phases of a CSP. In this research article, we proposed a Secure and Distributed Framework for Resource Management (SDFRM) in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system, the dynamic and trust-based Distributed Management Framework (DMF) of shared resource access. Such issues are focused by taking into account of the traditional characteristics of IoT/Industrial Internet of Things’ (IIoT)-predicated environments, an SDFRM in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system. Also, to ensure strong privacy over the procedures associated with Access Control (AC), a privacy-preserving method is proposed and integrated into the DMF. The proposed DMF, based on blockchain technology and peer-to-peer networks, allows dynamic access management and system governance without using third parties who could be attacked. We worked hard to design and implement the proposal to demonstrate its viability and evaluate its performance. Our proposal outperforms the Multichain Blockchain in terms of successful storage transactions with an achieved average throughput of 98.15%.

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.

SparkDQ: Efficient generic big data quality management on distributed data-parallel computation

In the big data era, large amounts of data are under generation and accumulation in various industries. However, users usually feel hindered by the data quality issues when extracting values from the big data. Thus, data quality issues are gaining more and more attention from data quality management analysts. Cutting-edge solutions like data ETL, data cleaning, and data quality monitoring systems have many deficiencies in capability and efficiency, making it difficult to cope with complicated situations on big data. These problems inspire us to build SparkDQ, a generic distributed data quality management model and framework that provides a series of data quality detection and repair interfaces. Users can quickly build custom tasks of data quality computing for various needs by utilizing these interfaces. In addition, SparkDQ implements a set of algorithms that in a parallel manner with optimizations. These algorithms aim at various data quality goals. We also propose several system-level optimizations, including the job-level optimization with multi-task execution scheduling and the data-level optimization with data state caching. The experimental evaluation shows that the proposed distributed algorithms in SparkDQ run up to 12 times faster compared to the corresponding stand-alone serial and multi-thread algorithms. Compared with the cutting-edge distributed data quality solution Apache Griffin, SparkDQ has more features, and its execution time is only around half of Apache Griffin on average. SparkDQ achieves near-linear data and node scalability.

Decentralized transactive energy management of multi-microgrid distribution systems based on ADMM

Presence of microgrids (MGs) and renewable energy sources (RESs) in distribution power systems could result in dramatic changes in operation and planning of these systems. In this regard, operational procedures employed by utilities should take into account the intermittent nature of RESs, while coping with the independent operation of MGs. In this perspective, this paper develops a distributed power management framework based on alternating direction method of multipliers (ADMM) for distribution networks with multi-MG (MMG) structures. The proposed approach develops a transactive signal in the context of ADMM to coordinate operation of MGs in a distributed manner. In this context, the configuration of MMG distribution systems is devised in a way that facilitates modern distributed control of the system by eliminating the role of a central coordinator in traditional distribution systems. Moreover, robust optimization technique is deployed in the MG operational scheduling to handle the uncertainty associated with RESs. Finally, through a numerical case study, the efficiency of the proposed framework to schedule local resources for MMG distribution systems is analyzed.

A Distributed Stochastic Energy Management Framework Based-Fuzzy-PDMM for Smart Grids Considering Wind Park and Energy Storage Systems

Distributed optimization methods have been vastly investigated and approved by the researchers due to their major advantages including high accuracy, secured performance and low time-consuming structure compared to the centralized frameworks. This paper aims to provide a novel method based on fuzzy primal-dual method of multipliers (PDMM) to manage the optimal energy scheduling problem in the smart grids. The proposed method illustrates some unrivaled points of interest which are more preferable than the conventional alternating direction method of multipliers (ADMM) in terms of preciseness and convergence speed. The proposed smart grid is constructed of different components such as generators, wind park and storage devices as two of the most profitable and applicable energy sources in the power grids. In order to model the uncertainty effects, a stochastic method based on fuzzy cloud theory is developed to capture the high-dimension uncertainty in a more realistic way. The units are scheduled to exchange energy in the smart grid in a fully distributed manner when meeting the active/reactive generation and demand balance. Such an energy exchanging process continues until a proper solution would be found through which all the agents in the system are satiated. The simulation results on the IEEE 24-bus test system indicate that the proposed stochastic distributed energy management framework yields an error of less than 0.018% compared to the centralized approach.

Towards distributed based energy transaction in a clean smart island

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication.

Distributed Group Key Management for Event Notification Confidentiality among Sensors

There is an increasing involvement of the Internet of Things (IoT) in many of our daily activities, with the aim of improving their efficiency and effectiveness. We are witnessing the advent of smart cities, in which IoT is exploited to improve the management of a city's assets, as well as smart factories, where IoT is paving the way for the forth industrial revolution. These applications and many other ones imply several non-functional requirements to be satisfied by the adopted IoT solution, where security assumes paramount importance. Secure communications among the IoT nodes are strongly needed due to the use of wireless technologies that are easy to eavesdrop, in order to steal valuable information. Accordingly, confidentiality is a fundamental prerequisite, but the existing solutions based on transport-level encryption are ineffective, while the ones with application-level encryption may be too expensive in terms of energy consumption. In this work, we propose a series of solutions and methods to achieve confidentiality with end-to-end guarantees, by using group-based keys within the context of a clustered and distributed key management framework. We have implemented such solutions on top of TinyOS, and assessed their achievable quality by means of the TOSSIM simulator.

A comprehensive simulation study of a network-based distributed mobility management framework

New mobility solutions are required for mobile environments characterised by an ever-increasing number of mobile devices and volume of internet data traffic. A single mobility anchor in the current Centralised Mobility Management CMM performs all mobility and routing functionalities. Distributed Mobility Management DMM paradigm is introduced recently to overcome scalability, reliability and non-optimal routing issues of the CMM. The DMM approach is proposed to flatten the network architecture by moving the mobility anchors closer to the users and separating the control and data planes at the network edge. In this paper, we develop and implement a network-based DMM model using NS2 simulation tool. Extensive simulations are performed to evaluate the performance of the DMM against that of the CMM. The simulation results indicate that the DMM approach outperforms the CMM approach under various mobility environments. Thus, this study verifies previous analytical studies and provides more accurate findings in DMM.

ARC Control Tower: A flexible generic distributed job management framework

While current grid middleware implementations are quite advanced in terms of connecting jobs to resources, their client tools are generally quite minimal and features for managing large sets of jobs are left to the user to implement. The ARC Control Tower (aCT) is a very flexible job management framework that can be run on anything from a single users laptop to a multi-server distributed setup. aCT was originally designed to enable ATLAS jobs to be submitted to the ARC CE. However, with the recent redesign of aCT where the ATLAS specific elements are clearly separated from the ARC job management parts, the control tower can now easily be reused as a flexible generic distributed job manager for other communities. This paper will give a detailed explanation how aCT works as a job management framework and go through the steps needed to create a simple job manager using aCT and show that it can easily manage thousands of jobs.

Modeling Social Influences in a Knowledge Management Network

The issue of knowledge management in a distributed network is receiving increasing attention from both scientific and industrial organizations. Research efforts in this field are motivated by the awareness that knowledge is more and more perceived as a primary economic resource and that, in the context of organization of organizations, the augmented management complexity of its whole life cycle requires new reference models. In this paper, we build on recent research work to propose a distributed knowledge management framework that can be used in several application domains. We characterize the dimension of social influences in terms of identity, negotiation and trust modeling them within a framework that can augment learning and cooperation capabilities through knowledge sharing and effective communication. A particular instance of the presented framework, to handle the problem of risk management in enterprise alliance partnership, is discussed as a case study that shows the practical applicability of our approach.

Optimising context data dissemination and storage in distributed pervasive computing systems

Context management systems are expected to administrate large volumes of spatial and non-spatial information in geographical disperse domains. In particular, when these systems cover wide areas such as cities, countries or even the entire planet, the design of scalable storage, retrieval and propagation mechanisms is paramount. This paper elaborates on mechanisms that address advanced requirements, including support for distributed context databases management; efficient query handling; innovative management of mobile physical objects and optimization strategies for distributed context data dissemination. These mechanisms establish a robust spatially-enhanced distributed context management framework that has already been designed and carefully implemented and thoroughly evaluated.

Distributed agents model for intrusion detection based on AIS

Artificial immune systems (AIS) is a complicated system with the ability of self-adapting, self-learning, self-organizing, parallel processing and distributed coordinating, and it also has the basic function to distinguish self and non-self and clean non-self. One significant feature of the theory immunology is the ability to adapt to changing environments and dynamically learning continuously. Inspired by the theory of artificial immune systems, a novel model of Agents of Network Danger Evaluation is presented. The concepts and formal definitions of immune cells are given, and dynamically evaluative equations for self, antigen, immune tolerance, mature-lymphocyte lifecycle and immune memory are presented, and the hierarchical and distributed management framework of the proposed model are built. Furthermore, the idea of dynamic immunological surveillance period is applied for enhancing the self-learning ability to adapt continuously variety environments. The experimental results show that the proposed model has the features of real-time processing that provide a good solution for network surveillance.

Computer Forensics System Based on Artificial Immune Systems

The current computer forensics approaches mainly focus on the network ac- tions capture and analysis the evidences after attacks, which always result in the static methods. Inspired by the theory of artificial immune systems (AIS), a novel model of Computer Forensics System is presented. The concepts and formal definitions of im- mune cells are given, and dynamically evaluative equations for self, antigen, immune tolerance, mature-lymphocyte lifecycle and immune memory are presented, and the hierarchical and distributed management framework of the proposed model are built. Furthermore, the idea of biology immunity is applied for enhancing the self-adapting and self-learning ability to adapt continuously variety environments. The experimental results show that the proposed model has the features of real-time processing, self- adaptively, thus providing a promising solution for computer forensics.

SEMANTICS‐ASSISTED PROBLEM SOLVING ON THE SEMANTIC GRID

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e‐Science. The framework uses ontology as the conceptual backbone and adopts the service‐oriented computing paradigm for information‐ and knowledge‐level computation. We further present a semantics‐based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e‐Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology‐enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

Towards a cooperative distributed manufacturing management framework

Advanced manufacturing systems need to be developed for enterprises to cooperate with each other in order to survive in the increasingly competitive global market. In this paper, manufacturing management issues are addressed at three levels: virtual enterprise (inter-enterprise), enterprise (intra-enterprise) and shop floor levels. An Internet enabled framework based on Web Services and agents for cooperative manufacturing management is proposed. An integration environment consisting of an agent based scheduling system, a Web based remote monitoring and control system, and corresponding services is designed and developed for the management, monitoring and control of the shop floor. Implementation details of the shop floor management environment are presented and some future work is prospected.

Distributed engineering change management for allied concurrent engineering

Effectively managing engineering change is a critical task in engineering management. Meanwhile, in allied concurrent engineering, the importance and difficulty of engineering change management is increased. This study presents a distributed engineering change management approach for the practice of allied concurrent engineering (ACE). An ACE-based engineering change management methodology was developed under the concepts of enterprise integration to manage the processes, systems and information of engineering change in an integrated fashion both within an individual enterprise and the enterprise alliance. The methodology includes a life cycle model for engineering change management, a hierarchical and distributed management framework, and a reference model for engineering change management. Based on this methodology, an ACE-based engineering change management system is developed using Unified Modelling Language (UML) modelling techniques. In additon to the methodology and the system for engineering change management, this work also demonstrates a systematic approach for enterprise system development. Results in this investigation will enable the practice of allied concurrent engineering and subsequently increase product development capability and quality, reduce development cycle time and cost and ultimately increase product marketability.

A framework for distributed management with mobile components

The increasing importance of networks and the growing numbers of devices and services that run on them necessitate effective network and systems management. The traditional centralized management paradigm alone is no longer sufficient for effective management solutions, primarily as it does not scale well. Distribution of management tasks is a promising approach. The distributed management framework (DMF) presented in this paper provides an environment that allows a broad range of management tasks to move and run anywhere within the managed system. In our approach, management tasks are lightweight applications that can be configured and downloaded dynamically as required, reducing the load on managed resources and simplifying the problem of management software updates. We present an object-oriented, Java-based implementation of the DMF and describe applications developed on this platform.