Showing papers on "Service (systems architecture) published in 2018"

Blockchain Technologies for the Internet of Things: Research Issues and Challenges

Abstract: This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g, Internet of Vehicles, Internet of Energy, Internet of Cloud, Fog computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely, identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods towards secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

Live Service Migration in Mobile Edge Clouds

Abstract: Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.

Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]

Abstract: Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis.

Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore

Abstract: This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.

Service-Oriented Feature-Based Data Exchange for Cloud-Based Design and Manufacturing

Abstract: With the rapid development of service-oriented computing (SOC)/service-oriented architecture (SOA), cloud computing and web services, cloud-based design and manufacture (CBDM) is emerging as state-of-the-art technologies and methodologies to enable collaborative product development (CPD). CBDM-enabled CPD can provide cost-effective, flexible and scalable solutions to collaborative partners by sharing the resources in the applications of design and manufacturing. Feature-based data exchange (FBDE) has been one of the key issues in history of CPD and should be adapted in lasted CBDM-enabled CPD. Firstly this paper presents a service-oriented architecture for data exchange in CBDM. Within this architecture, FBDE was registered as service and FBDE users in the CBDM environment can acquire a set of FBDE services to replace the traditional FBDE functions among heterogeneous CAD systems. Secondly, in order to put the philosophy of FBDE-as-a-Service into practice for CBDM, this paper proposes a peer to peer (P2P) approach for service-oriented FBDE, which revolutionizes the traditional centralized and neutral-file based approach. Thirdly, technique issues of FBDE-as-a-Service in P2P architecture are discussed in details, including constituting of the P2P FBDE service, procedure of service-oriented P2P FBDE, pre-P2P FBDE service, topological entity matching between pre/post-P2P service and post-P2P FBDE service. Finally, a case study of data exchange is tested to demonstrate the proposed idea of service-oriented FBDE for CBDM.

Cost-effective service excellence

Abstract: This article integrates relevant literature to develop a conceptual model on the potential avenues to achieve service excellence at low unit costs, which we term cost-effective service excellence (CESE). To gain a deeper understanding of these strategies, their applicability and interrelatedness, we analyze how 10 organizations have achieved CESE. Our findings show that CESE can be achieved through three core strategies. First, a dual culture strategy provides a comprehensive set of high-quality services at low cost, largely driven by leadership ambidexterity and contextual ambidexterity. Second, an operations management approach reduces process variability and thereby allows the increased use of systems and technology to achieve CESE. Third, a focused service factory strategy can enable CESE through a highly specialized operation, typically delivering a single type of service to a highly focused customer segment. The use of the three approaches ranges from “pure” (e.g., mostly pursuing a dual culture strategy) to combinations of the latter two approaches with the dual culture strategy (e.g., a focused service factory strategy combined with dual culture). Our conceptual model provides an integrated view of the strategic options available to organizations that aim to pursue a strategy of CESE.

Edge Computing Architecture for Mobile Crowdsensing

Abstract: Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.

Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases

Abstract: Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.

NSML: Meet the MLaaS platform with a real-world case study

Abstract: The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively. However, existing machine learning frameworks are limited to sufficiently fulfill the collaboration and management for both data and models. We proposed NSML, a machine learning as a service (MLaaS) platform, to meet these demands. NSML helps machine learning work be easily launched on a NSML cluster and provides a collaborative environment which can afford development at enterprise scale. Finally, NSML users can deploy their own commercial services with NSML cluster. In addition, NSML furnishes convenient visualization tools which assist the users in analyzing their work. To verify the usefulness and accessibility of NSML, we performed some experiments with common examples. Furthermore, we examined the collaborative advantages of NSML through three competitions with real-world use cases.

An Introduction to Dew Computing: Definition, Concept and Implications

Abstract: Since the end of the 1990s, the world has witnessed a tremendous growth in the area of information and communication technology (ICT), starting with grid computing, cloud computing (CC), and fog computing to recently introduced edge computing. Although, these technologies are still in very good shape, they do heavily rely on connectivity, i.e., Internet. To address this challenge, this paper proposes a novel dew-cloud architecture that brings the power of CC together with the dew computing (DC). Originally, the dew-cloud architecture is an extension of the existing client-server architecture, where two servers are placed at both ends of the communication link. With the help of a dew server, a user has more control and flexibility to access his/her personal data in the absence of an Internet connection. Primarily, the data are stored at the dew server as a local copy upon which instantiation of the Internet is synchronized with the master copy at the cloud side. Users can browse, read, write, or append data on the local dew site, which is a local Web form of an actual website. With the incorporation of the dew domain naming system and dew domain name redirection, mapping between different local dew sites has become possible. Novel services, such as infrastructure-as-a-dew, software-as-a-dew service, and software-as-a-dew product, are, hereby, introduced along with the DC. This paper presents the following as key contributions: 1) a precise and concrete definition of DC; 2) detailed and comprehensive discussions of its concept and working principle; 3) application potentials; and 4) technical challenges. The motto of this paper is to conceptualize the fact of empowerment of the ICT-user base with almost an Internet-free surfing experience in coming days.

SERvICE: A Software Defined Framework for Integrated Space-Terrestrial Satellite Communication

Abstract: The existing satellite communication systems suffer from traditional design, such as slow configuration, inflexible traffic engineering, and coarse-grained Quality of Service (QoS) guarantee. To address these issues, in this paper, we propose SERvICE, a Software dEfined fRamework for Integrated spaCe-tErrestrial satellite Communication, based on Software Defined Network (SDN) and Network Function Virtualization (NFV). We first introduce the three planes of SERvICE, Management Plane, Control Plane, and Forwarding Plane. The framework is designed to achieve flexible satellite network traffic engineering and fine-grained QoS guarantee. We analyze the agility of the space component of SERvICE. Then, we give a description of the implementation of the prototype with the help of the Delay Tolerant Network (DTN) and OpenFlow. We conduct two experiments to validate the feasibility of SERvICE and the functionality of the prototype. In addition, we propose two heuristic algorithms, namely the QoS-oriented Satellite Routing (QSR) algorithm and the QoS-oriented Bandwidth Allocation (QBA) algorithm, to guarantee the QoS requirement of multiple users. The algorithms are also evaluated in the prototype. The experimental results show the efficiency of the proposed algorithms in terms of file transmission delay and transmission rate.

Mapping the sharing economy for sustainability research

Abstract: In order to guide sustainability research on the sharing economy, the purpose of this paper is to develop a comprehensive framework that captures the wide range of activities and business models that are considered to be part of the sharing economy,Based on a systematic literature review and a content analysis, existing typologies are identified and analyzed for their conceptual intersections Finally, categorizations from 43 documents are integrated into one framework,Four main dimensions are identified as being used in different contexts to characterize sharing systems and were combined to form one comprehensive typology: shared good or service, market structure, market orientation, and industry sector,The proposed typology is able to distinguish sharing activities based on their similarities and differences Social, economic, and communicational avenues for the term “sharing” are merged into a conceptual foundation of the sharing economy This enables researchers, practitioners, and policy makers to position their projects in the broad field of sharing By discussing inherent tensions with regard to sustainability of the sharing economy, the offered categorizations can help to guide future research and policy intervention Last but not least, professional managers should find valuable ideas for new business models

A Conceptual Framework for Industry 4.0

Abstract: Industrial Revolution emerged many improvements in manufacturing and service systems. Because of remarkable and rapid changes appeared in manufacturing and information technology, synergy aroused from the integration of the advancements in information technology, services and manufacturing were realized. These advancements conduced to the increasing productivity both in service systems and manufacturing environment. In recent years, manufacturing companies and service systems have been faced substantial challenges due to the necessity in the coordination and connection of disruptive concepts such as communication and networking (Industrial Internet), embedded systems (Cyber Physical Systems), adaptive robotics, cyber security, data analytics and artificial intelligence, and additive manufacturing. These advancements caused the extension of the developments in manufacturing and information technology, and these coordinated and communicative technologies are constituted to the term, Industry 4.0 which was first announced from German government as one of the key initiatives and highlights a new industrial revolution. As a result, Industry 4.0 indicates more productive systems; companies have been searching the right adaptation of this term. On the other hand, the achievement criteria and performance measurements of the transformation to Industry 4.0 are still uncertain. Additionally, a structured and systematic implementation roadmap is still not clear. Thus, in this study, the fundamental relevance between design principles and technologies is given and conceptual framework for Industry 4.0 is proposed concerning fundamentals of smart products and smart processes development.

Deploying Chains of Virtual Network Functions: On the Relation Between Link and Server Usage

Abstract: Recently, network function virtualization has been proposed to transform from network hardware appliances to software middleboxes. Normally, a demand needs to invoke several virtual network functions (VNFs) following the order determined by the service chain along a routing path. In this paper, we study the joint problem of the VNF placement and path selection to better utilize the network. We discover that the relation between the link and server usage plays a crucial role in the problem. Inspired by stress testing, we first propose a systematic way to elastically tune the link and server usage of each demand based on the network status and properties of demands. In particular, we compute a proper routing path length, and decide, for each VNF in the service chain, whether to use additional server resources or to reuse resources provided by existing servers. We then propose a chain deployment algorithm that follows the guidance of this link and server usage. Via simulations, we show that our design effectively adapts resource allocation to network dynamics and, hence, serves more demands than other heuristics.

Querying in Internet of Things with Privacy Preserving: Challenges, Solutions and Opportunities

Abstract: IoT is envisioned as the next stage of the information revolution, enabling various daily applications and providing better service by conducting a deep fusion with cloud and fog computing. As the key mission of most IoT applications, data management, especially the fundamental function-data query, has long been plagued by severe security and privacy problems. Most query service providers, including the big ones (e.g., Google, Facebook, Amazon, and so on) are suffering from intensive attacks launched by insiders or outsiders. As a consequence, processing various queries in IoT without compromising the data and query privacy is an urgent and challenging issue. In this article, we propose a thing-fog-cloud architecture for secure query processing based on well studied classical paradigms. Following with a description of crucial technical challenges in terms of functionality, privacy and efficiency assurance, we survey the latest milestone-like approaches, and provide an insight into the advantages and limitations of each scheme. Based on the recent advances, we also discuss future research opportunities to motivate efforts to develop practical private query protocols in IoT.

IoT-Based Strawberry Disease Prediction System for Smart Farming.

Abstract: Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.

Deep hybrid collaborative filtering for Web service recommendation

Abstract: With the rapid development of service-oriented computing and cloud computing, an increasing number of Web services have been published on the Internet, which makes it difficult to select relevant Web services manually to satisfy complex user requirements. Many machine learning methods, especially matrix factorization based collaborative filtering models, have been widely employed in Web service recommendation. However, as a linear model of latent factors, matrix factorization is challenging to capture complex interactions between Web applications (or mashups) and their component services within an extremely sparse interaction matrix, which will result in poor service recommendation performance. Towards this problem, in this paper, we propose a novel deep learning based hybrid approach for Web service recommendation by combining collaborative filtering and textual content. The invocation interactions between mashups and services as well as their functionalities are seamlessly integrated into a deep neural network, which can be used to characterize the complex relations between mashups and services. Experiments conducted on a real-world Web service dataset demonstrate that our approach can achieve better recommendation performance than several state-of-the-art methods, which indicates the effectiveness of our proposed approach in service recommendation.

How Should I Slice My Network?: A Multi-Service Empirical Evaluation of Resource Sharing Efficiency

Abstract: By providing especially tailored instances of a virtual network,network slicing allows for a strong specialization of the offered services on the same shared infrastructure. Network slicing has profound implications on resource management, as it entails an inherent trade-off between: (i) the need for fully dedicated resources to support service customization, and (ii) the dynamic resource sharing among services to increase resource efficiency and cost-effectiveness of the system. In this paper, we provide a first investigation of this trade-off via an empirical study of resource management efficiency in network slicing. Building on substantial measurement data collected in an operational mobile network (i) we quantify the efficiency gap introduced by non-reconfigurable allocation strategies of different kinds of resources, from radio access to the core of the network, and (ii) we quantify the advantages of their dynamic orchestration at different timescales. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms.

Knowledge-Centric Edge Computing Based on Virtualized D2D Communication Systems

Abstract: MEC has been gaining great interest in addressing the limited computational capability in 5G mobile networks. Although there are several existing works about MEC, for example, utilizing WNV and SDN, virtualized D2D communication together with learning-based D2D communication systems, such as social and smart D2D networking systems, have not been investigated by researchers, while knowledge of the edge is lacking. In this article, we introduce an innovative framework, called KCE, to dynamically detect network structure and manage communication resources, leveraging the insightful knowledge obtained from D2D communications among mobile users. Further, to support trustworthy network access and to meet different QoS requirements in the proposed architecture, specific SPs are selected from InPs by a KCE server. As such, the chosen SPs' resources can be dynamically allocated to meet various service requirements by virtualized D2D communication techniques. Simulation results demonstrate the effectiveness of the proposed framework under different scenarios and parameter settings.

Microscope: Pinpoint Performance Issues with Causal Graphs in Micro-service Environments

Abstract: Driven by the emerging business models (e.g., digital sales) and IT technologies (e.g., DevOps and Cloud computing), the architecture of software is shifting from monolithic to microservice rapidly. Benefit from microservice, software development, and delivery processes are accelerated significantly. However, along with many micro services running in the dynamic cloud environment with complex interactions, identifying and locating the abnormal services are extraordinarily difficult. This paper presents a novel system named “Microscope” to identify and locate the abnormal services with a ranked list of possible root causes in Micro-service environments. Without instrumenting the source code of micro services, Microscope can efficiently construct a service causal graph and infer the causes of performance problems in real time. Experimental evaluations in a micro-service benchmark environment show that Microscope achieves a good diagnosis result, i.e., 88% in precision and 80% in recall, which is higher than several state-of-the-art methods. Meanwhile, it has a good scalability to adapt to large-scale micro-service systems.

Achieving benefits with enterprise architecture

Abstract: There is strong anecdotal evidence that Enterprise Architecture (EA) brings benefits to organisations and that organisations are investing significantly in EA initiatives. However, demonstrating the business value of EA has proven elusive. Many of the benefits of EA are intangible and value is achieved indirectly within business change projects. Furthermore, it is not the EA itself that provides benefits, it is the ability to provide advisory services enabled by the EA that is important. In this paper we focus on EA service capability and develop and test a new research model that explains how EA service brings benefits to organisations. Our findings highlight the importance of EA service capability and dynamic capabilities in creating benefits from EA.

Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things

Abstract: After investigation on the existing advanced manufacturing systems (AMSs), it is found that supply–demand matching of manufacturing resource is one of the common issues to be addressed in all AMSs, and methods for addressing this issue have evolved from P2P (peer-to-peer)-based, to information centre-based, and to platform (or system)-based matching, and are moving towards socialisation and service-based solutions. In order to adapt to this trend, a new method for manufacturing resource supply–demand matching based on complex networks and Internet of Things (IoT) is proposed, and a four-layered architecture for implementing this method is designed. In this method, IoT technology is employed to realise the intelligent perception and accessing of various manufacturing resources and capabilities (MR&C), which enables logical aggregation of various distributed MR&C in the form of services. Then complex networks model and theory are used to realise the efficient manufacturing service management, optima...

A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

Abstract: Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City

Abstract: Smart city (SC) technologies can provide appropriate services according to citizens’ demands. One of the key enablers in a SC is the Internet of Things (IoT) technology, which enables a massive number of devices to connect with each other. However, these devices usually come from different manufacturers with different product standards, which confront interactive control problems. Moreover, these devices will produce large amounts of data, and efficiently analyzing these data for intelligent services. In this paper, we propose a novel artificial intelligence-based semantic IoT (AI-SIoT) hybrid service architecture to integrate heterogeneous IoT devices to support intelligent services. In particular, the proposed architecture is empowered by semantic and AI technologies, which enable flexible connections among heterogeneous devices. The AI technology can support very implement efficient data analysis and make accurate decisions on service provisions in various kinds. Furthermore, we also present several practical use cases of the proposed AI-SIoT architecture and the opportunities and challenges to implement the proposed AI-SIoT for future SCs are also discussed.