Distributed Architecture Research Articles

A technique of deviant behaviour detection in video surveillance systems based on complex behaviour analysis

This paper deals with deviant behavior detection using video surveillance systems. Deviant behaviour detection is a complex problem and includes not only the recognition of the abnormal patterns of individual’s behaviour, but also the problem of general methodology development, which consists of deviant behaviour markers and techniques to identify them. A new complex approach to the object behaviour analysis is presented in this paper as well as the integrated distributed classifier generic architecture is described. The novelty of this research is the consequent subject analysis based on a complex of potentially deviant and socially dangerous behaviour attributes.

Selective serotonin reuptake inhibitor use is associated with worse sleep-related breathing disturbances in individuals with depressive disorders and sleep complaints: a retrospective study.

The effects of serotonergic agents on respiration neuromodulation may vary according to differences in the serotonin system, such as those linked to depression. This study investigated how sleep-related respiratory disturbances relate to depression and the use of medications commonly prescribed for depression. Retrospective polysomnography was collated for all 363 individuals who met selection criteria out of 2,528 consecutive individuals referred to a specialized sleep clinic (Ottawa, Canada) between 2006 and 2016. The apnea-hypopnea index (AHI), oxygen saturation nadir, and oxygen desaturation index during REM and NREM sleep were analyzed using mixed analyses of covariance comparing 3 main groups: (1) medicated individuals with depressive disorders (antidepressant group; subdivided into the selective serotonin reuptake inhibitor and norepinephrine-dopamine reuptake inhibitor subgroups), (2) non-medicated individuals with depressive disorders (non-medicated group), and (3) mentally healthy control patients (control group). Individuals with depressive disorders (on antidepressants or not) had significantly higher AHIs compared to control patients (both P ≤ .007). The antidepressant group had a lower NREM sleep oxygen saturation nadir and a higher NREM sleep oxygen desaturation index than the control and non-medicated groups (all P ≤ .009). Within individuals with depressive disorders, independent of depression severity, the selective serotonin reuptake inhibitor group had a lower oxygen saturation nadir and a higher oxygen desaturation index during NREM sleep than the norepinephrine-dopamine reuptake inhibitor (both P ≤ .045) and non-medicated groups (both P < .001) and a higher NREM sleep AHI than the non-medicated group (P = .014). These findings suggest that the use of selective serotonin reuptake inhibitors may be associated with impaired breathing and worse nocturnal oxygen saturation in individuals with depressive disorders and sleep complaints, but this needs to be confirmed by prospective studies.

An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites.

The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that short-read aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden. This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a “clean-and-recover” step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals. The optimized workflow and analysis recommendations can be implemented in the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets.

5G network-oriented hierarchical distributed cloud computing system resource optimization scheduling and allocation

As the core technology of the next generation mobile communication system, the development of 5G key technologies needs to be able to efficiently and effectively support massive data services. Aiming at the impact of massive data traffic on mobile communication networks in 5G communication systems, this paper proposes a 5G-oriented hierarchical distributed cloud service mobile communication system architecture. The model consists of a cloud access layer, a distributed micro-cloud system, and a core cloud data center. The distributed micro cloud system consists of multiple micro clouds that are deployed to the edge of the network. The service content in the core cloud data center can be deployed and cached to the local micro cloud server in advance to reduce repeated redundant transmission of user requested content in the network. Aiming at the problem of how to determine the migration object when dynamically optimizing the resource structure, a heuristic function-based dynamic optimization algorithm for cloud resources is proposed. The experimental results show that the dynamic expansion algorithm of cloud resources based on dynamic programming ideas can better improve the performance of virtual resources, and the dynamic optimization algorithm of cloud resources based on heuristic functions can effectively and quickly optimize the resource structure, thereby improving the operating efficiency of user virtual machine groups. An efficient resource allocation scheme based on cooperative Q (Quality) learning is proposed. The environmental knowledge obtained by the base station learning and exchanging information is used for distributed resource block allocation. This resource allocation scheme can obtain the optimal resource allocation strategy in a short learning time, and can terminate the learning process at any time according to the delay requirements of different services. Compared with traditional resource allocation schemes, it can effectively improve system throughput.

A Computational Analysis of Decomposition Strategies for Model Predictive Control of Resource-Constrained Dynamic Systems

This paper presents two decomposition approaches, Bilevel Optimization and Benders Decomposition, to a model predictive control of resource-constrained dynamic systems. The proposed methods yield a distributed solution that converges to the same optimum that would be obtained by a centralized controller. In this context, it is shown that the decompositions enable the use of multi-core or distributed architectures. A level regularization method is applied to accelerate the convergence of the Benders decomposition. Computational analyses from experiments with synthetic problems and a problem regarding the charging of vehicle batteries are reported and discussed, which showed that the decomposition approaches are effective at solving the distributed problems, achieving a global optimum.

An Integrated Indexing and Search Service for Distributed File Systems

Data services such as search, discovery, and management in scalable distributed environments have traditionally been decoupled from the underlying file systems, and are often deployed using external databases and indexing services. However, modern data production rates, looming data movement costs, and the lack of metadata, entail revisiting the decoupled file system-data services design philosophy. In this article, we present TagIt, a scalable data management service framework aimed at scientific datasets, which can be integrated into prevalent distributed file system architectures. A key feature of TagIt is a scalable, distributed metadata indexing framework, which facilitates a flexible tagging capability to support data discovery. Furthermore, the tags can also be associated with an active operator, for pre-processing, filtering, or automatic metadata extraction, which we seamlessly offload to file servers in a load-aware fashion. We have integrated TagIt into two popular distributed file systems, i.e., GlusterFS and CephFS. Our evaluation demonstrates that TagIt can expedite data search operation by up to 10× over the extant decoupled approach.

Cognitive Popularity Based AI Service Sharing for Software-Defined Information-Centric Networks

As an important architecture of next-generation network, Software-Defined Information-Centric Networking (SD-ICN) enables flexible and fast content sharing in beyond the fifth-generation (B5G). The clear advantages of SD-ICN in fast and efficient content distribution and flexible control make it a perfect platform for solving the rapid sharing and cognitive caching of AI services, including data samples sharing and pre-trained models transferring. With the explosive growth of decentralized artificial intelligence (AI) services, the training and sharing efficiency of edge AI is affected. Various applications usually request the same AI samples and training models, but the efficient and cognitive sharing of AI services remain unsolved. To address these issues, we propose a cognitive popularity-based AI service distribution architecture based on SD-ICN. First, an SD-ICN enabled edge training scheme is proposed to generate accurate AI service models over decentralized big data samples. Second, Pure Birth Process (PBP) and error correction-based AI service caching and distribution schemes are proposed, which provides user request-oriented cognitive popularity model for caching and distribution optimization. Simulation results indicate the superiority of the proposed architecture, and the proposed cognitive SD-ICN scheme has 62.11% improved to the conventional methods.

Fiber Bragg grating sensing system with wavelength-swept-laser distribution and self-synchronization.

Fiber Bragg gratings (FBGs) with various interrogation schemes to estimate the FBG's spectrum shift have been widely used in fiber sensing systems. Wavelength swept laser (WSL) based interrogation architectures have been proposed to offer rapid and high-quality sensing performance. However, for getting higher sensing accuracy, the demands for high-performance WSL may push the system cost. Under these considerations, a WSL distribution architecture allowing multiple sensing processing units (SPUs) to share the WSL is studied in this Letter. A self-synchronization scheme is proposed to enable flexible SPU deployment with no concerns for the clock calibration. The proposed system is experimentally studied. Temperature estimation error of ∼2.5∘C and ∼0.5∘C with sensitivities of 0.13°C/ms and 0.14°C/ms, respectively, for the high and small temperature ranges are demonstrated.

Spotlight: Scalable Transport Layer Load Balancing for Data Center Networks

Load Balancing plays a vital role in cloud data centers to distribute traffic among instances of network functions or services. State-of-the-art load balancers dispatch traffic obliviously without considering the real-time utilization of service instances and therefore can lead to uneven load distribution and sub-optimal performance. In this article, we design and implement Spotlight, a scalable and distributed load balancing architecture that maintains connection-to-instance mapping consistency at the edge of data center networks. Spotlight uses a new stateful flow dispatcher which periodically polls instances’ load and dispatches incoming connections to instances in proportion to their available capacity. Our design utilizes a distributed control plane and in-band flow dispatching; thus, it scales horizontally in data center networks. Through extensive flow-level simulation and packet-level experiments on a testbed with HTTP traffic on unmodified Linux kernel, we demonstrate that compared to existing methods Spotlight distributes traffic more efficiently and has near-optimum performance in terms of overall service utilization. Compared to existing solutions, Spotlight improves aggregated throughput and average flow completion time by at least 20 percent with infrequent control plane updates. Moreover, we show that Spotlight scales horizontally as it updates the switches at O(100ms) and is resilient to lack of control plane convergence.

Design and Implementation of Elevator WeChat Public Supervision System based on SOA

A WeChat public supervision system for elevator maintenance was designed aimed at the problems of elevator maintenance status monitoring and elevator safety supervision. Combined with “Internet +” and big data supervision, aiming at the loose coupling characteristics of the system, using the unified development technology system (J2EE), introducing the concept of SOA layered and distributed system architecture, and finally the unified application service management platform was established. On this basis, the unified management of the elevator application management system was realized. The elevator information supervision system has the characteristics of public participation supervision, traceability of maintenance records, and benigh interaction between supervision and the public, which will contribute to the further development of intelligent elevator supervision.

A cost‐efficient QoS‐aware analytical model of future software content delivery networks

SummaryFreelance, part‐time, work‐at‐home, and other flexible jobs are changing the concept of workplace and bringing information and content exchange problems to companies. Geographically, spread corporations may use remote distribution of software and data to attend employees' demands, by exploiting emerging delivery technologies. In this context, cost‐efficient software distribution is crucial to allow business evolution and make IT infrastructures more agile. On the other hand, container‐based virtualization technology is shaping the new trends of software deployment and infrastructure design. We envision current and future enterprise IT management trends evolving towards container‐based software delivery over hybrid content delivery networks (CDNs). This paper presents a novel cost‐efficient QoS‐aware analytical model and a hybrid CDN–peer‐to‐peer (P2P) architecture for enterprise software distribution. The model would allow delivery cost minimization for a wide range of companies, from big multinationals to SMEs, using CDN–P2P distribution under various industrial hypothetical scenarios. Model constraints guarantee acceptable deployment times and keep interchanged content amounts below the bandwidth and storage network limits in our scenarios. Indeed, key model parameters account for network bandwidth, storage limits, and rental prices, which are empirically determined from their offered values by the commercial delivery networks KeyCDN, MaxCDN, CDN77, and BunnyCDN. This preliminary study indicates that MaxCDN offers the best cost‐QoS trade‐off. The model is implemented in the network simulation tool PeerSim and then applied to diverse testing scenarios by varying company types, number and profile (either, technical or administrative) of employees, and the number and size of content requests. Hybrid simulation results show overall economic savings between 5% and 20%, compared to just hiring resources from a commercial CDN, while guaranteeing satisfactory QoS levels in terms of deployment times and number of served requests.

Photoacoustic imaging for non-invasive examination of the healthy temporal artery - systematic evaluation of visual function in healthy subjects.

Photoacoustic (PA) imaging has the potential to become a non-invasive diagnostic tool for giant cell arteritis, as shown in pilot experiments on seven patients undergoing surgery. Here, we present a detailed evaluation of the safety regarding visual function and patient tolerability in healthy subjects, and define the spectral signature in the healthy temporal artery. Photoacoustic scanning of the temporal artery was performed in 12 healthy subjects using 59 wavelengths (from 680nm to 970nm). Visual function was tested before and after the examination. The subjects' experience of the examination was rated on a 0-100 VAS scale. Two- and three-dimensional PA images were generated from the spectra obtained from the artery. Photoacoustic imaging did not affect the best corrected visual acuity, colour vision (tested with Sahlgren's Saturation Test or the Ishihara colour vision test) or the visual field. The level of discomfort was low, and only little heat and light sensation were reported.The spectral signature of the artery wall could be clearly differentiated from those of the subcutaneous tissue and skin. Spectral unmixing provided visualization of the chromophore distribution and overall architecture of the artery. Photoacoustic imaging of the temporal artery is well tolerated and can be performed without any risk to visual function, including the function of the retina and the optic nerve. The spectral signature of the temporal artery is specific, which is promising for future method development.

A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller

The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum power point tracking controller is presented in order to address the drawbacks associated with respect to cost, complexity and efficiency of the existing photo-voltaic system architectures. In addition, a radial movement optimization based maximum power point tracking control algorithm is designed, developed, and validated using the proposed system architecture under five different partial shading conditions. The inferences obtained from the validation results of the proposed distributed system architecture indicated that cost was reduced by 75% when compared to the commonly used decentralised systems. The proposed distributed building integrated photo-voltaic system architecture is also more efficient, robust, reliable, and accurate.

Collaborative Accelerators for Streamlining MapReduce on Scale-up Machines With Incremental Data Aggregation

The MapReduce programming paradigm has been increasingly adopted to implement data-intensive applications processing both small and large scale datasets. As most jobs in data centers have a data footprint in the order of gigabytes, emerging high-end scale-up machines are capable of running most data center processing tasks, thus significantly improving power and server density. However, this approach provides limited performance and energy efficiency because of inefficient utilization of the memory subsystem and serial execution within the MapReduce programming model. Recent work has proposed a distributed hardware acceleration architecture, called CASM, which augments each core in a scale-up machine with a lightweight compute engine. The CASM's network of accelerators operates concurrently with the cores in executing MapReduce stages and reduces significantly traffic to/from storage. In this article, we study the benefits and applicability of CASM, by offering an extensive analysis of design parameters and of its scalable performance on a wide range of applications, and exploring its applicability to incremental data aggregation tasks. Our experimental evaluation indicates that CASM reduces off-chip traffic by four times on average over a chip multiprocessor solution, while scaling well with the number of cores in the system, and it is highly effective in providing incremental results that approximate final outcomes.

Change and Stability in Industry Architectures: Firm Actions and Performance Effects

This study analyzes performance effects of firm efforts to shape the structure of an industry. We view industry structure through the conceptual lens of industry architectures and posit that firms shape these architectures by carrying out a single or multiple of change- and stability-oriented actions over time. We hypothesize that both types of actions differently affect firm performance and that this relationship is moderated by the predictability, segment conformity, and simplicity of firms’ competitive behavior. To test our hypotheses, we created a unique dataset of airline actions that fostered either stability or change in the flight-ticket distribution architecture and assessed, as a proxy for firm performance, the change these actions cause in investors’ expectations of future firm performance once an action is announced. Our findings reveal that change-oriented actions negatively affect firm performance and that this effect is further exacerbated by simplicity in firms’ competitive behavior, but that stability-oriented actions have an inconclusive effect. Our findings challenge the literature’s overwhelming focus on firm-driven change and its associated benefits and makes an initial effort at identifying the factors that both facilitate firm efforts to shape architecture and influence firm performance.

A Hardware-in-the-Loop Platform for DC Protection

Real-time (RT) simulation of power and energy conversion systems allows engineers to interface both simulation- and hardware-based controls using controller hardware-in-the-loop (CHiL) simulation of networks of power electronic converters (PECs) to de-risk highly developmental systems, such as next generation electrified transportation systems and dc microgrids. CHiL exploration and performance verification moves a design from technology readiness level (TRL) 3 to TRL 4 without incurring significant cost investments in developmental hardware platforms, which otherwise discourages such endeavors. An RT CHiL simulation platform suitable for explorations of protective equipment, protection schemes, and networked PEC dc and mixed dc-ac power distribution architectures must be capable of simulating common-mode behavior, various grounding schemes, and fault transients at sufficiently high resolution. This article demonstrates this capability using a latency-based linear multistep compound (LB-LMC) simulation method implemented in a commercially sustainable, adaptable, and expandable FPGA-based test and instrumentation platform. The proposed CHiL platform achieves RT power system simulations, including detailed switching commutations of networked PECs, with 50-ns resolution, and faithfully produces resonant and transient behaviors associated with line-to-ground (LG) and line-to-line (LL) faults and fault recovery in ungrounded PEC-based dc systems. This resolution in RT cannot be achieved with today's commercial off-the-shelf CHiL platforms. This article demonstrates the need for high-resolution RT simulation of LG and LL faults within dc systems, and demonstrates a CHiL approach that enables dc protection design explorations and protective control hardware testing while taking into account the realistic aspects that affect fault characteristics in PEC-based dc systems, such as cable current rating and length, cable and PEC parasitic LG capacitance, and PEC internal response to fault scenarios.

Hybrid decentralized PBFT Blockchain Framework for OpenStack message queue

Cloud computing based on OpenStack is widely used as a distributed computing platform. OpenStack has progressed at a rapid pace, incorporating a variety of service modules; it is supported by many companies, has a community of active developers, and a diverse user base. OpenStack uses message queue to coordinate and exchange operation and status information between services. OpenStack supports various message queue services including RabbitMQ, Qpid, and ZeroMQ, whereas its distribution architecture uses RabbitMQ. As an OpenStack’s message queue service, RabbitMQ runs on a controller node as a centralized service. In case of the centralized service, increased usage may cause slowed response times and security vulnerability. This paper proposes a Hybrid decentralized Practical byzantine fault tolerance Blockchain Framework with two-step verification for OpenStack message queue service. When compared to existing OpenStack message queue service, OpenStack with the proposed framework demonstrates identical reliability a faster response time by approximately 46.75% with a two-step verification process and decentralization approach. Additionally, a reduction in the security vulnerability in the OpenStack message queue information with saving the message queue information into each node by blockchain-based decentralized data duplication approach.

Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO

Cell-free Massive MIMO (multiple-input multiple-output) is a promising distributed network architecture for 5G-and-beyond systems. It guarantees ubiquitous coverage at high spectral efficiency (SE) by leveraging signal co-processing at multiple access points (APs), aggressive spatial user multiplexing and extraordinary macro-diversity gain. In this study, we propose two distributed precoding schemes, referred to as \textit{local partial zero-forcing} (PZF) and \textit{local protective partial zero-forcing} (PPZF), that further improve the spectral efficiency by providing an adaptable trade-off between interference cancelation and boosting of the desired signal, with no additional front-hauling overhead, and implementable by APs with very few antennas. We derive closed-form expressions for the achievable SE under the assumption of independent Rayleigh fading channel, channel estimation error and pilot contamination. PZF and PPZF can substantially outperform maximum ratio transmission and zero-forcing, and their performance is comparable to that achieved by regularized zero-forcing (RZF), which is a benchmark in the downlink. Importantly, these closed-form expressions can be employed to devise optimal (long-term) power control strategies that are also suitable for RZF, whose closed-form expression for the SE is not available.

Scalable Decentralized Indexing and Querying of Multi-Streams in the Fog

NOA-AID (Network Overlays for Adaptive information Aggregation, Indexing and Discovery on the fog) is an approach for decentralized indexing, aggregation and discovery of data belonging to streams. It is organized on two network layers. The upper layer is in charge of delivering an information discovery approach by providing a distributed index structure. The lower layer is devoted to resource aggregation based on epidemic protocols designed for highly dynamic environment, well suited to stream-oriented scenarios. It defines a flexible approach to express queries targeting highly heterogeneous data, as well as a self-organizing dynamic system allowing the optimal resolution of queries on the most suitable stream producers. The paper also presents a theoretical study and discusses the costs related to information management operations; it also gives an empirical validation of findings. Finally, it reports an extended experimental evaluation that demonstrated the ability of NOA-AID to be effective and efficient for retrieving information extracted from streams in highly-dynamic and distributed processing architectures.

A Review of Solid-State Circuit Breakers

Although conventional electromechanical circuit breakers have a proven record as effective and reliable devices for circuit protection, emerging power distribution technologies and architectures, such as dc microgrids, require improved interruption performance characteristics (e.g., faster switching speed). The need for faster switching operation, in combination with the latest developments of advanced power semiconductor technologies, has spurred an increase in the research and development in the area of solid-state circuit breakers. This article provides a comprehensive review of various solid-state circuit breaker technologies that have been reported in the literature during recent years. First, we categorize solid-state circuit breakers based on key features and subsystems, including power semiconductor devices, main circuit topologies, voltage clamping methods, gate drivers, fault detection methods, and commutation methods for power semiconductor devices. Second, we discuss the various challenges associated with the design of solid-state circuit breakers from the perspective of generic applications and provide a comparison of several solid-state breaker technologies based on key metrics. Finally, we provide a useful framework and point of reference for future development of solid-state circuit breakers for many emerging power distribution applications.