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The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems. To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals. To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP). Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems. Then, the end-to-end transmission latency is derived based on the proposed model. Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations. Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.

Ethereum is one of the most popular blockchain systems that support more than half a million transactions every day and foster miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this article, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytic platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract, and user-contract graphs, are constructed according to trading relationships and are segmented with an appropriate time window. We observe a strong correlation between the size of the user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights, and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions are validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, ``the rich is already very rich.''

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability.

Large software defined networks use a cluster of distributed controllers to process flow requests from a massive number of switches. To cope with traffic dynamics, this paper studies a new problem of how to improve the residual capacity available at the controllers to handle request bursts experienced at the switches. While the total residual capacity is a constant under a given total capacity of all controllers and a given total workload from all switches, this paper considers the residual capacity available to each individual switch, which depends on how the switches are mapped to the controllers for management. We focus on how to maximize the minimum residual capacity available to any switch . The prior work either provides poor residual capacity or incurs heavy synchronization overhead by simulation results. This paper proposes a new method called indirect multi-mapping that achieves both high residual capacity and low synchronization cost. We formally define a non-linear integer optimization problem for max-min residual capacity under indirect multi-mapping. We then approximate the problem as two sub-problems: switch-controller mapping selection and weight assignment for each switch-controller mapping. We solve these sub-problems and formally analyze their approximate factor. We implement the proposed solution on an SDN testbed for experimental studies and use simulations for large-scale investigation. Our evaluation shows that indirect multi-mapping improves the minimum residual capacity by 49.8% on average and reduces the synchronization cost by 41.9-60.3% on average when compared with the alternatives.

Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness of workloads, and wide range of execution times of serverless functions result in load imbalances and overloading of worker nodes of serverless platforms that impose high latency on invocations. This paper is concentrated on function scheduling within worker nodes of Apache OpenWhisk serverless platform and presents ETAS, a predictive scheduling scheme to reduce response times of invocations besides increasing workers throughputs and resource utilizations. ETAS schedules functions using their execution times, estimated by their previous execution’s history, arrival times, and containers’ status. We have implemented ETAS in Apache OpenWhisk and show that, compared to the OpenWhisk worker scheduler and several queue scheduling schemes, ETAS outperforms others by reducing the average waiting time by 30% and increasing the throughput by 40%.

The service process is the key phase in any queue system applied to business and industry operations. The service process in shops is defined as the payment process at the cash register. The service process consists of two elements or sub-processes: the waiting in the queue to the cash register as well as the payment processing (scanning the goods, giving receipts to customers, etc). Analysis of burstiness as the indicator of the service process has been well-established. Against this background on burstiness as the indicator of the service process, burstiness is also defined as a factor that influences the service process. However, burstiness as a factor in the service process has not attracted a lot of research attention. The aim of this paper is to analyse burstiness as a factor in the service process underpinning the elaboration of scenarios of the service process for the queue management purposes. The present work mostly employs theoretical methods: scientific literature analysis, synthesis, modelling, comparison, and systematization. The theoretical research results in the outline of the conceptual framework for exploring the impact of burstiness on the service process. The key concepts have been identified, namely binary customer behaviour, buyers’ burstiness, bottlenecks’ emergence at the server, and queue management. The logical chain of the development has been emphasized: binary customer behaviour → buyers’ burstiness → bottlenecks’ emergence at the check-out station or cash register (server) → queue management. The presented logical chain allows finding out that buyers’ burstiness leads to the queue appearance in the service process. In turn, queue appearance requires queue management measures. Hence, buyers’ burstiness influences on the decisions in regard to queue management within the service process. Further on, two functions of buyers’ burstiness are defined: the indicators of the service process, and the factor that influences the service process. This bi-modal role of buyers’ burstiness in the service process highlights the complex nature of the queue management. Five scenarios of the service process will allow using a combination of queue management measures in each scenario or even between scenarios. The findings of the comparative study propose the structure of the service process as the unity of the waiting in the queue to the cash register and the payment processing at the cash register, i.e. scanning of the goods and the payment. The present research has some limitations. Further research tends to validate the model of five scenarios of the service process for the queue management purposes. Comparative studies on buyers’ burstiness in the service process will be continued, too.

Attention-dependent reduction in the tendency for neurons to fire bursts (burstiness) is widely observed in the visual cortex. However, the underlying mechanism and the functional role of this phenomenon remain unclear. We recorded well-isolated single-unit activities in primary visual cortex (V1) from two primates (Macaca mulatta) while they performed a detection task engaging spatial attention with two levels of difficulty (hard/easy). We found that attention modulated burstiness of V1 neurons in a cell-type specific manner. For neurons whose net response enhanced with the increase of task difficulty (difficulty-enhanced neuron), representing their involvement in boosting the signal of the attended stimulus, attention led to a reduction in burstiness during hard task but not during easy task. In contrast, regardless of the level of task difficulty, attention-dependent reduction in burstiness was not observed in neurons that showed a net suppression in firing rate with the increase of task difficulty (difficulty-suppressed neuron), indicating their commitment in filtering out the interference of distractor. This differentiation in the effects of attentional modulation on burstiness among the cells with distinct functional roles in attention suggests that the reduction in burstiness by attention is linked to target enhancement and is not associated with distractor suppression.

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social content. Due to sensitive privacy, the traditional differential privacy (DP) methods are used to protect social contents. However, these DP methods rely on a fully trusted third party (TTP) without considering protecting the correlations of social keywords, and do not support the privacy-preserving online social content publication. In this article, we propose a novel local DP-based TTR (LDPTR) scheme to perform high-quality online TTR services while achieving local privacy-preserving social contents. Specifically, LDPTR first clusters the social keywords with high correlations into noisy graph classes based on a graph-based LDP (GLDP) algorithm for ensuring the keyword correlation privacy. Second, LDPTR adopts a novel mechanism called $\epsilon _{2}$ -compressive sensing indistinguishability (CSI) to generate noisy social topics, thereby preventing the user-linkage attack and breaking the curse of high-dimensional local differential privacy (LDP). Then, a dynamic graph-based CSI (DGCSI) algorithm is proposed to protect the online social content privacy while ensuring data usability. Furthermore, LDPTR calculates the trending topics of interest with high burstiness by using a topic distribution similarity model-based topic burstiness (TMTB) algorithm, which achieves effective TTR services. Our LDPTR scheme satisfies $\epsilon $ -LDP by detailed security analysis. Extensive experiments over two real-world data sets show that the proposed LDPTR gets high data utility while ensuring high-level privacy.

In order to answer how much bandwidth is available to an application from one end to another in a network, state-of-the-art estimation techniques, based on active probing, inject artificial traffic with a known structure into the network. At the receiving end, the available bandwidth is estimated by measuring the structural changes in the injected traffic, which are caused by the network path. However, bandwidth estimation becomes difficult when packet distributions are distorted by non-fluid bursty cross traffic and multiple links. This eventually leads to an estimation bias. One known approach to reduce the bias in bandwidth estimations is to probe a network with constant-rate packet trains and measure the average structural changes in them. However, one cannot increase the number of packet trains in a designated time period as much as needed because high probing intensity overloads the network and results in packet losses in probe and cross traffic, which distorts probe packet gaps and inflicts more bias. In this work, we propose a machine learning-based, particularly classification-based, method that provides reliable estimates utilizing fewer packet trains. Then, we implement supervised learning techniques. Furthermore, considering the correlated changes over time in traffic in a network, we apply filtering techniques on estimation results in order to track the changes in the available bandwidth. We set up an experimental testbed using the Emulab software and a dumbbell topology in order to create training and testing data for performance analysis. Our results reveal that our proposed method identifies the available bandwidth significantly well in single-link networks as well as networks with heavy cross traffic burstiness and multiple links. It is also able to estimate the available bandwidth in randomly generated networks where the network capacity and the cross traffic intensity vary substantially. We also compare our technique with the others that use direct probing and regression approaches, and show that ours has better performance in terms of standard deviation around the actual bandwidth values.

Buffer sizing for switching and routing devices is of significance for guaranteeing the Quality of Service (QoS) of critical services on the Internet of Things (IoT), continuously evolving scheduling mechanisms and complex traffic characteristics pose new challenges for the traditional method of static buffer sizing based on rule-of-thumb. In this paper, the scope of buffer sizing is extended from a basic scheduling system under homogeneous arrival traffic input to an integrated scheduling system under heterogeneous arrival traffic input which is more ubiquitous. In this context, Voices, videos and other heterogeneous data in the IoT are categorized into short-range-dependent (SRD) and long-range-dependent (LRD) traffic, and the integrated scheduling system is decomposed into single-server-single-queue (SSSQ) systems by not only decoupling the complex dependencies among heterogeneous traffic inputs but also taking the impact of SRD and LRD traffic burstiness on the buffer sizing into account. On this basis, expressions for the relation between the minimum buffer size and the maximum overflow probability are presented. The numerical analysis results and simulation analysis results reveal that the average arrival rate, traffic burst level and scheduling priority are positively correlated with the required buffer size, and once the overflow probability is set, the minimum buffer size can be determined correspondingly. The achievements of this paper will provide theoretical guidance for IoT manufacturers and technicians to set buffers more reasonably and use resources more efficiently.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

As network data keeps getting bigger, deep learning is coming to play a key role in network design and management. Meanwhile, accurate network traffic prediction is of critical importance for network management that is implemented to improve the quality of service (QoS) for users. However, the performance of existing network traffic prediction methods is still poor due to three challenges: complicated characteristics of network traffic, dynamics of traffic patterns caused by different network applications, and a complex set of variations like burstiness. In this article, we propose a long short-term memory (LSTM) based network traffic prediction (LNTP) model, which aims to forecast network traffic timely and accurately. The model can be divided into two parts, namely, wavelet transform and LSTM. The working process of LNTP falls into three stages, i.e., data acquisition, model training, and online learning and prediction. In addition, to avoid the negative incentives to models caused by the burstiness and adapt to the changing trend of the network traffic, a weight optimization algorithm of the neural network named sliding window gradient descent (SWGD), is also proposed. Extensive experiments based on two real-world network traffic datasets demonstrate that our model outperforms the state-of-the-art network traffic prediction models by more than 29 percent.

Digital single-flux quantum (SFQ) technology promises to meet the demands of ultralow-power and high-speed computing needed for future exascale supercomputing systems. However, high degrees of variability makes ultrahigh-speed low-skew clock distribution of large-scale SFQ circuits challenging. This article proposes to mitigate this problem by supporting multiple independent SFQ clock domains with the design of reliable clock domain crossing (CDC) circuits. We first show that previously proposed CDC first-in-first-out buffers (FIFOs) are vulnerable to synchronization errors, motivating the need for more robust CDC FIFOs. Inspired by complementary metal-oxide-semiconductor (CMOS) multi-flip-flop FIFO synchronizers, we then present a metastability-resilient SFQ FIFO synchronizer that, simulations show, delivers over a 1000 reduction in logical error rate at 30 GHz. We show that, for a 10-stage FIFO, the Josephson junction area of our proposed design is only 7.5% larger than the nonresilient counterpart. Finally, we propose detailed design guidelines that define the minimal depth of our proposed synchronizers subject to both throughput and burstiness constraints.

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.

In this paper, we propose a mixture model for high-dimensional count data clustering based on an exponential-family approximation of the Multinomial Beta-Liouville distribution, which we call EMBL. We deal simultaneously with the problems of fitting the model to observed data and selecting the number of components. The learning algorithm automatically selects the optimal number of components and avoids several drawbacks of the standard Expectation-Maximization algorithm, including the sensitivity to initialization and possible convergence to the boundary of the parameter space. We demonstrate the effectiveness and robustness of the proposed clustering approach through a set of extensive empirical experiments that involve challenging real-world applications. The results reveal that the novel proposed model strives to achieve higher accuracy compared to the state-of-the-art generative models for count data clustering. Furthermore, the superior performance of EMBL demonstrates its flexibility and ability to address the burstiness phenomenon successfully, as well as shows its computational efficiency, especially when dealing with sparse high-dimensional vectors.

The dramatic growth of mobile application usage has posed great pressure on application developers to better manage their backend capacity. Rule-based or schedule-based auto-scaling mechanisms have been proposed, but it is difficult or expensive to frequently adjust the backend capacity to track the burstiness of mobile traffic. In this paper, we explore a fundamentally different approach. Instead of scaling the backend in line with the mobile traffic, we smooth out traffic profiles to reduce the required backend capacity and increase its utilization. Our proposed solution, called Razor , is inspired by two key insights on mobile traffic. First, mobile traffic exhibits high short-term fluctuations but steady long-term trend, so that we may temporarily delay user requests and periodically adapt backend capacity based on the predicted traffic volume. Second, user requests have different priorities: while some requests are urgent (e.g., sending a message), some are delay-tolerant (e.g., changing the profile photo) and can be postponed without much influence on the user experience. Based on these observations, our design features a two-tier architecture: on a long timescale, Razor predicts future traffic using machine learning algorithms and plans the optimal backend capacity to minimize the budget with performance guarantee; on a short timescale, Razor schedules which requests to delay and by how much time to delay according to their delay tolerance. We implement a fully-functional prototype of Razor, and evaluate its performance with both real and synthetic traces. Extensive experimental results show that Razor can effectively help mobile application developers reduce their backend cost while guaranteeing the user experience.

PurposeThe purpose of this study is to propose a data-driven perspective to analyze enterprise information system (EIS) feature use for understanding what and how the system features are actually used in the organization.Design/methodology/approachAn empirical study was conducted by analyzing information system (IS) log data collected from a well-known shipbuilding manufacturer in China. The multiple analytical approach employed in this study includes social network analysis, association rules learning and human dynamics.FindingsThis study first classified IS users into 41 core users and 325 general users. Then 24 core modules and 54 general modules were identified by social network analysis, and the correlation between them was analyzed. Finally, we found that the IS use time intervals for different user groups followed a power-law distribution, and IS use displayed strong burstiness and weak memory.Originality/valueThis study proposes a data-driven perspective to investigate how the system features are actually used in the organization. This study contributes to the literature and opens a new avenue for future IS use research. Furthermore, this study informs managers how to diagnose, maintain and optimize the implemented IS in order to maximize IS benefits.

Different trees may have different seed production behaviors. Understanding the spatial and temporal patterns in seed production is important for sustainable forest management. A common feature in ...

Micro-burst traffic is not uncommon in data centers. It can cause packet dropping, which may result in serious performance degradation (e.g., Incast problem). However, current approaches to mitigate micro-burst is usually ad-hoc and not based on a principled understanding of the underlying behaviors. On the other hand, traditional studies focus on traffic burstiness in a single flow, while micro-burst traffic in the data centers could occur with highly fan-in communication pattern, and its dynamic behavior is still unclear. To this end, in this paper, we re-examine the micro-burst traffic in typical data center scenarios. We find that the evolution of micro-burst is determined by both TCP’s self-clocking mechanism and congestion control algorithm. Besides, dynamic behaviors of micro-burst under various scenarios can all be described by the time derivative of queue length evolution.Our observations also implicate that conventional solutions like absorbing and pacing are ineffective to mitigate micro-burst traffic.Instead, senders need to rapidly respond to some explicit signals of the queue buildup caused by the micro-burst traffic rather than independently and ineffectually pacing themselves in isolation. Inspired by the findings and insights from experimental observations, we propose Micro-burst-Aware Transport Control Protocol (MATCP), which leverages characteristic behaviors of micro-burst traffic derived from the time derivative of the queue occupancy. MATCP can suppress the sharp queue length increment by over $2\times $ and reduce the tail query completion time by up to 84.4%.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Additionally, a sender-based approach is proposed to estimate the flow concurrency. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations and testbed experiments, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average $7\times $ under severe congestion.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson’s Chi-Square test to extract the collided signals’ combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is $11.7\times $ higher than that of the state-of-the-art approach.

Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method.

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing , Lazy Rerouting , and Adaptive Rerouting . Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow’s performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatial-temporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method.

Service demand burstiness, or serial correlations in resource service demands, has previously been shown to have an adverse impact on system performance metrics such as response time. This article proposes AMIR , an analytic framework to characterize burstiness and identify strategies to reduce its impact on performance. AMIR considers an overtake-free system model consisting of multiple queues that service multiple classes of sessions, i.e., sequences of requests. Given the per-class service demand distributions and number of sessions belonging to each class, AMIR can identify an ordering of sessions, i.e., a schedule, that minimizes burstiness at the bottleneck. Hence, it is likely to improve system responsiveness metrics, including mean session wait time and total schedule processing time. To characterize burstiness, the technique uses an order O schedule burstiness metric β O representing the mean probability that O + 1 consecutive sessions in the schedule have resource demands at the bottleneck greater than the mean bottleneck demand of the schedule. For a given O , AMIR uses Integer Linear Programming (ILP) to identify schedules that progressively minimize β i ∀ i ∈ {1, … O }. We conduct an extensive simulation study to provide insights on the conditions under which such schedules can improve system responsiveness. These results show that schedules derived from AMIR can significantly outperform those derived from baseline policies such as Shortest Job First (SJF) and random scheduling when session classes are dissimilar from one another in terms of their service demand distributions. Furthermore, minimizing for higher orders of schedule burstiness is most critical when the bottleneck is heavily utilized and when the service demands of a workload are highly variable. For the system model that we consider, we are not aware of other techniques that are designed to analytically derive insights on the performance impact of high-order service demand burstiness.

With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

A Virtual Channel (VC) is a Time Division Multiplexed (TDM) slice of a physical channel/link. A crucial step of interconnect synthesis is to assign VCs to traffic that avoids deadlocks while meeting Power, Performance and Area (PPA) objectives. For a Network on Chip (NoC), VC assignment is tightly coupled with topology generation and routing. Inefficient VC assignment can lead to NoCs which may be an order of magnitude inferior in terms of PPA. However, both VC assignment and topology generation are combinatorially hard problems. Thus, combining VC assignment with topology generation makes it difficult to efficiently solve either.In this paper, we present a topology agnostic VC assignment approach for statically routed NoCs. This segregation enables us to solve the VC assignment problem first and then subsequently generate topology. By solving VC assignment first, we reduce the problem space of topology generation leading to NoCs with tighter PPA. We model the VC assignment problem as a Traffic Conflict Graph (TCG), capturing a global view of Quality of Service (QoS), Head-of-Line (HoL) conflicts, burstiness and external protocol level dependencies (e.g. PCIe root-complex). We apply combinatorial optimization techniques on TCG to arrive at an efficient VC assignment, that ensures deadlock free designs. The algorithm has been implemented in production NoC Synthesis tool. Results obtained on more than a dozen multi-million gate System-on-Chip (SoC) demonstrate an average improvement of 30% across metrics such as routers, resizers, power/clock domain converters, latencies etc. while meeting the performance vis-a-vis hand-tuned NoCs. The hand-tuned designs have been manually optimized over several man-months by expert designers, while the tool achieves better results in a few minutes.

High-resolution studies of data center traffic at the network core uncover short-term bursty traffic patterns, periods of high buffer utilization that last for tens of microseconds and lead to packet loss and longer flow completion time tails. While recent attention has been directed towards studying the bursty traffic at the network core, less heed has been given to the origins of bursty traffic, e.g., host machines. In this study, we try to perform high-resolution traffic measurements in the host networking stack to quantify the impact of system software components on traffic burstiness. We enforce per-packet timestamping on the datapath using various techniques like NIC timestamping, eBPF, and direct kernel source modification and measure the gaps between egress packets under various configurations. We provide preliminary findings on how process scheduling can affect traffic burstiness.

Empirical studies and theoretical models both highlight burstinessas a common temporal pattern in online behavior. A key driver for burstiness is the self-exciting nature of online interactions. For example, posts in online groups often incite posts in response. Such temporal dependencies are easily lost when interaction data is aggregated in snapshots which are subsequently analyzed independently. An alternative is to model individual interactions as a multi-dimensional self-exciting process, thus, enforcing both temporal and network dependencies. Point processes, however, are challenging to employ for large real-world datasets as fitting them incurs super-linear cost in the number of events. How can we efficiently detect online groups exhibiting bursty self-exciting temporal behavior in large real-world datasets? We propose a bursty group detection framework, called MYRON, which explicitly models self-exciting behavior within groups while also accounting for network-wide baseline activity. MYRON imposes bursty temporal structure within a scalable tensor factorization framework to decouple within-group interactions as interpretable factors. Our framework can incorporate different "shapes"of temporal burstiness via wavelet decomposition or kernels forself-exciting behavior. Our evaluation on both synthetic and real-world data demonstrates MYRON's utility in community detection.It is up to 40% more effective in detecting ground truth groups compared to state-of-the-art baselines. In addition, MYRON is able to uncover interpretable bursty patterns of behavior from user-photo interactions in Flickr.

The rising of online social platforms makes large volumes of data about social relationships and interactions available to the research community. In the varied ecosystem of techno-social platforms, blockchain-based online social networks - BOSNs - are gaining momentum since the underlying blockchain offers data validation, data storage, and data decentralization. As data sources, BOSNs provide high-resolution temporal data about the evolution of the social network and on the interactions of users with the platform services. In this study, we focus on a few temporal characteristics, by analyzing the dynamics of the link creation process and the claiming of rewards in the BOSN Steemit. We model blockchain data as a temporal directed network from which we extract the time series characterizing link creation and reward claims. Adopting a user-centric approach, we evaluate the heterogeneity of the time series through the inter-event time distribution, the burstiness, the bursty train size distribution, and the fitting of inter-event times by power law models. The outcomes of the analysis highlight that the above processes show bursty traits typical of human dynamics. However, the two aspects present a few differences concerning the types of models describing their behavior and the time scale of their bursty nature. To sum up, the creation of new relationships and the reward claim dynamics ask for specific models able to reproduce their general bursty traits but taking into account their specificities and relations with other services and mechanisms offered by BOSN platforms.

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.

The software switches and routers are rapidly developed in the NFV/SDN area, and are expected to be adopted in the emerging future mobile network (beyond-5G). Despite the high expectation of software routers, its latency characteristics have not been investigated extensively. To provide the reference latency value for software-based network function, we analyzed the latency of SRv6 Mobile User Plane on VPP software router. We show that, 1) for the beyond-5G mobile network application, the latency is in the feasible range (roughly 3–30 μs), 2) the throughput is sufficient with the range in 1–5 Mpps per CPU core, 3) the latency has the strong correlation with the input traffic load, 4) and the latency can be stable in a carefully crafted implementation. The packet interval analysis shows the burstiness of its output traffic that is enforced by the processing of a vector (bulk/batch) of packets. Our latency characteristic analysis can be of help for improving the performance of software router using the batch processing and designing software-based beyond-5G mobile communication systems.

This paper considers the problem of exactly calculating the stationary-state distribution of a system that statistically multiplexes the output of K sources. Each source is an N-state Markov chain and its Poisson rate of packet generation is determined by its state. Since Broadband ISDN (B-ISDN) will statistically multiplex many bursty sources and the burstiness can be modeled by Markov chains, the efficient analyses of large examples of the model considered here are an essential requirement for B-ISDN design. An algorithm is given for the spectral expansion of the state distribution whose complexity is o (K3(N-1)) for N fixed and K large. The efficiency of the algorithm is derived from an algebraic theory that gives the (exact) decomposition of the eigenvalue problem of the entire system into many small eigenvalue problems, and 224also gives the Kronecker product form to the eigenvectors; another important element is a recursive algorithm for (exact) aggregation.

Network traffic can reflect the operating status and resource bottleneck of the entire network. Accurate prediction of the future network is helpful in network maintenance, network optimization, routing policy design, load balancing, protocol design, and anomaly detection. However, the self-similarity, periodicity, chaos, multi-scale, and other characteristics of modern network traffic make it challenging to predict network behaviors. The available prediction models focus only on self-similarity and burstiness, lacking a more accurate and comprehensive description of the characteristics of network traffic. In this paper, we propose a prediction model based on Empirical Mode Decomposition (EMD) and the Gated Recurrent Unit (GRU) neural network with data reconstruction. First, the traffic data are reconstructed by complementing missing-points and eliminating outliers. Then, we decompose the reconstructed traffic data into several components through EMD and use each component to train the corresponding GRU neural network. Finally, the predicted values of all components are combined to get the final result. Numerical results show that the proposed prediction model offers higher accuracy and more stable performance than the state-of-the-art models.

Low-power wireless communication has been widely used in cyber-physical systems that require time-critical data delivery. Achieving this goal is challenging because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3.6 M packet transmissions per link, we propose both routing and scheduling algorithms that produce latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric B max that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst-case burst lengths over all links in the route. With extensive data-driven analysis, we show that our algorithms outperform existing solutions by achieving accurate latency bound with much less energy consumption. In addition, a testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high-quality links.

Studying research fronts enables researchers to understand how their academic fields emerged, how they are currently developing and their changes over time. While topic modelling tools help discover themes in documents, they employ a “bag-of-words” approach and require researchers to manually label categories, specify the number of topics a priori, and make assumptions about word distributions in documents. This paper proposes an alternative approach based on entity linking, which links word strings to entities from a knowledge base, to help solve issues associated with “bag-of-words” approaches by automatically identifying topics based on entity mentions. To study topic trends and popularity, we use four indicators—Mann–Kendall’s test, Sen’s slope analysis, z-score values and Kleinberg’s burst detection algorithm. The combination of these indicators helps us understand which topics are particularly active (“hot” topics), which are decreasing (“cold” topics or past “bursty” topics) and which are maturely developed. We apply the approach and indicators to the fields of Information Science and Accounting.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.

Virtual machine placement is one of the main sub-problems in the virtual machine consolidation process which faces different challenges. Burst-aware placement plays a key role in improving energy efficiency and reducing the SLA violations in cloud computing systems and hence requires special attention and investigation. Therefore, in this study, we will present burst-aware algorithms in order to decrease the resource wastage and reduce SLA violations. By presenting these algorithms, we aim to minimize the negative effects of workload bursts on the process of making decisions about the placement of virtual machines. We use random and real-world datasets and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding energy efficiency and performance, compared to the benchmark methods.

Given a temporal weighted graph that consists of a potentially endless stream of updates, we are interested in finding density bursting subgraphs (DBS for short), where a DBS is a subgraph that accumulates its density at the fastest speed. Online DBS detection enjoys many novel applications. At the same time, it is challenging since the time duration of a DBS can be arbitrarily long but a limited size storage can buffer only up to a certain number of updates. To tackle this problem, we observe the critical decomposability of DBSs and show that a DBS with a long time duration can be decomposed into a set of indecomposable DBSs with equal or larger burstiness. We further prove that the time duration of an indecomposable DBS is upper bounded and propose an efficient method TopkDBSOL to detect indecomposable DBSs in an online manner. Extensive experiments demonstrate the effectiveness, efficiency and scalability of TopkDBSOL in detecting significant DBSs from temporal graphs in real applications.

As multi-tenant cloud storage services are becoming increasingly popular in commercial clouds, there is a growing need for evaluating their resource usage effectiveness. Although tail latency and resource utilization are the most important and popular performance metrics, it is biased to use only one of them on evaluating the resource usage effectiveness. In this paper, we present a unified framework, Stochastic Model-based Effectiveness Analyzer (SMEA), to evaluate the resource usage effectiveness of multi-tenant cloud storage systems. Instead of evaluating tail latency and resource utilization separately, we propose a new metric called resource-productivity to combine both performance. Since evaluating tail latency or resource utilization solely is challenging due to contention, queueing and burstiness of workloads, a Markov-modulated Poisson process (MMPP) is adopted in SMEA to properly characterize the behavior of workloads. Moreover, the Stochastic Network Calculus (SNC)-based network latency analysis model is extended to evaluate the end-to-end tail latency over all system components, with the principle “treating the computer as a network”. After obtaining the analyzed tail latency and resource utilization, we derive the resource-productivity mathematically. We have implemented SMEA on a small-scale testbed. Extensive trace-driven experiments demonstrate that SMEA is efficient in evaluating the resource usage effectiveness of the multi-tenant cloud storage system with relative error less than 13%.

The dispersion that arises when packets traverse a network carries information that can reveal relevant network characteristics. Using a fluid-flow model of a bottleneck link with first-in first-out multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth, i.e., the residual capacity that is left over by other traffic. Difficulties arise, however, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple bottlenecks, clustering of packets due to interrupt coalescing, and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. This motivates us to explore the use of machine learning in bandwidth estimation. We train a neural network using vectors of the packet dispersion that is characteristic of the available bandwidth. Our testing results reveal that even a shallow neural network identifies the available bandwidth with high precision. We also apply the neural network under a variety of notoriously difficult conditions that have not been included in the training, such as randomly generated networks with the multiple bottleneck links and heavy cross traffic burstiness. Compared to two state-of-the-art model-based techniques as well as a recent machine learning-based technique (Yin et al., 2016), our neural network approach shows improved performance. Further, our neural network can effectively control the estimation procedure in an iterative implementation. We also evaluate our method with other supervised machine learning techniques.

Opinion dynamics on networks has wide applications to empirical and engineered systems and profound prospects in the general study of complex systems. Many efforts have been devoted to understanding how opinion dynamics is affected by network topology. However, human social interactions are best characterized as temporal networks in which ordering of interactions cannot be ignored. Temporal activity patterns including heterogeneous contact strength and interevent times, turnover edge/node dynamics and daily patterns could have significant effects that would not be captured by static aggregate network representations. In this paper, we study the effects of such temporal patterns on the speed of consensus formation in various models of continuous opinion dynamics using three empirical human face-to-face networks from different real-world settings. We find that static, aggregated networks consistently overestimate the speed of simulated consensus formation while weight heterogeneity associated with frequency of interactions has an inhibitory effect on consensus formation relative to the behavior on unweighted networks. Moreover, the speed of consensus formation is found to be highly sensitive to nodal lifetimes, suggesting that randomization protocols that dramatically alter the distribution of lifetimes cannot be relied upon as reference models. On the other hand, temporal patterns including burstiness of interevent times and the lifetime of edges are found to have insignificant effects on consensus formation., Comment: 15 pages, 8 figures

Task scheduling is a significant problem to be resolved for balancing the workload on a cloud server. One of the key problems that affect the scheduling performance is burstiness workloads. Few research studies have been introduced to schedule tasks and balancing loads in the cloud. However, scheduling performance of existing technique was not effective in burstiness workload’s conditions. Thus, there is a need for a novel task scheduling technique to handle bursty user demands and provide high-quality cloud services. Therefore, Threshold Based Multi-Objective Memetic Optimized Round Robin Scheduling (T-MMORRS) Technique is proposed in this research work. At first, user requests are sent to the cloud server. After that, T-MMORRS Technique employs burst detector to determine the workload condition as normal or that which is bursty. Based on burst detector result, then T-MMORRS Technique adapts the two different load balancing algorithms for efficiently scheduling the user tasks. The T-MMORRS Technique chooses Threshold Multi-Objective Memetic Optimization (TMMO) in normal workload situation and Weighted Multi-Objective Memetic Optimized Round Robin Scheduling (WMMORRS) in burstiness workload state. Finally, the selected load balancing algorithm in MMORRS Technique schedules the user request task to a resource-efficient virtual machine with higher efficiency and lower time consumption. As a result, T-MMORRS Technique enhances the task scheduling performance to balance the both bursty and non-bursty workloads of virtual machines in the cloud. The experimental evaluation of T-MMORRS Technique is conducted using factors such as scheduling efficiency, scheduling time and energy consumption with respect to the number of user requests. The experimental result shows that the T-MMORRS Technique can enhance the scheduling efficiency and also minimizes the energy usage in the cloud as compared to state-of-the-art works.

Temporal networks are composed of individuals with on-and-off interactions. In the study of human dynamics, a typical interaction is interpreted as a coincidental or forced concurrence of two events. Since human beings’ rationality is bounded, the interaction between sentient individuals is normally investigated under the framework of game theory in the past half a century. In this brief, a game model on social networks is introduced, in which individuals play a $2\times2$ divide-and-conquer game with their neighbors, which is a specific symmetric game based on the type of their interactions. The individuals in a network play with the game with a certain strategy. The duration of a continuous interaction is defined to be the number of the continuous rounds in which at least one of them receives a non-zero payoff. On the contrary, the inter-event time is measured by the number of the continuous rounds in which both players do not receive any payoff. A detailed analytical and numerical study of the model’s dynamical properties is presented, showing that it reproduces the burstiness of the human coordinating system. The findings reveal that the burstiness is inducible by rational interactions of humans. The new model and analysis shed some new lights on the origin of the burstiness of human activities.

In this paper, we consider a coordinated beamforming design for a multiple-input single-output bursty interference channel. In practical scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contribute to the bursty nature of network traffic. The potential gains provided by such burstiness can be exploited by feeding back the interference state information to the transmitters. Errors may occur during such feedback, thus causing uncertainties in the user traffic and resulting in complicated beamforming design problems. In this paper, we model such traffic uncertainties by using a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at the transmitters, our goal is to maximize the average system utility subjected to average power constraints under traffic uncertainties. We also extend the optimization problem to the imperfect CSI case. However, the resulting problem is highly nonconvex. Hence, we apply a series of convex approximation techniques. We further improve the approximation accuracy by using our proposed successive convex approximation (SCA) algorithms (HMM-SCA). In our simulation results, we observe that our proposed HMM-SCA provides a $46.3\%$ improvement in weighted sum rate over the SCA algorithm, in which the interference is assumed to be always present (NB-SCA). For the average weighted geometric mean rate, we observe that our proposed HMM-SCA also provides a $41.3\%$ improvement over the NB-SCA when user fairness is considered. Therefore, it is crucial to exploit bursty traffic for a practical wireless communication system when channel estimation errors are considered.

The grouping of data in bursts, also referred to as clusters, spikes or clumps, is a common phenomenon in stochastic sampling. There have been several reports that suggest that in NMR, the presence of such bursts is beneficial to spectral reconstruction where data are sampled nonuniformly. In this work, we seek to define a mode of sampling that produces bursts of randomly distributed data in a controlled manner. An algorithm is described for achieving this where the burst length and its uniformity is controlled - we refer to this type of sampling mode as clustered sampling. Measures are introduced for assessing the "burstiness" of nonuniformly sampled data in multiple dimensions and properties of the point-spread-function of these schedules are assessed. The clustered sampling method is applied to samples drawn from an exponentially weighted distribution either distributed randomly or pseudo-randomly by use of a jittering algorithm. The results reveal that bursts introduce characteristic sampling artifacts that are shifted to low frequencies (red shifted), with respect to the signal frequency, and that they produce artifact-reduced regions at frequencies related to the burst length. This observation is contrary to that observed for sampling methods that seek to evenly distribute NUS data, such as jittered or Poisson sampling. Extensive evaluation of simulated data with comparable inherent sensitivity, reveals that at high sampling coverage (25% in 1D), the distribution of the data has little impact on common spectral quality measures. Application of the introduced clustered sampling method to an experimental 3D NOESY experiment showed results consistent with that found for the simulated 1D data. However, in the extremes of very sparse sampling, the results suggest that there may be some advantages associated with incorporation of bursts in nonuniform sampling. The tools and theory presented will serve as a starting point to further explore this novel mode of sampling in NMR.

The construction industry is extremely high risk, and the unsafe behavior of workers is thought to be a critical factor in that risk level. Considering the limitation that existing studies merely analyze the relationship between unsafe behavior and the time factor, this paper uses a case study to explore two aspects of the time-statistical laws of workers’ unsafe behavior: (i) the characteristics of interevent time distributions and (ii) association rules for different worker types. First, workers’ unsafe acts at one metro construction site are collected and classified. Second, interevent time distributions of workers’ unsafe behavior from different types are analyzed via a human dynamics approach. Finally, a rule mining database is built, from which association rules concerning unsafe behavior, worker type and construction phase are determined using the Apriori algorithm. The results indicate that the interevent time distributions are fat tailed and show that workers’ unsafe behavior has the characteristics of burstiness and memory. Furthermore, the strong rules ‘construction phase → unsafe behavior’ exist for different worker types. The research presented in this paper can facilitate a better understanding of workers’ unsafe behavioral patterns and can accordingly be used to control frequent and widespread unsafe acts among different types of workers in different construction phases.

지속적으로 변화해가는 인간의 언어에는 필연적으로 새롭게 생겨나는 단어들이 존재하기 마련이다. 기존의 언어 행태를 기반으로 개발되는 자연어처리 기술에서 이런 신조어의 처리는 매우 어려운 문제이다. 본 연구는 뉴스 기사들에서 신조어의 사용 통계를 수집하여 신조어의 사용 특징을 분석한다. 정량적인 분석을 위하여 사용량, 사용 지속성, 사용 편중도 등의 척도를 제안한다. 신조어가 처음 사용되기 시작한 이래로 사용량이 어떻게 바뀌어 가는지, 일반 신조어와 전문 분야 신조어의 사용량 변화에 차이가 있는지, 신조어의 사용 지속성이나 사용 편중도가 사용량에 어떤 영향을 미치는지, 신조어에 대한 설명 표현은 얼마나 자주 신조어와 같이 사용되는지 등을 여러 실험을 통하여 살펴본다.

Simulation modelling is carried out, which allows adequate describing the traffic of multiservice networks with the commutation of packets with the characteristic of burstiness. One of the most effective methods for studying the traffic of telecommunications systems is computer simulation modelling. By using the theory of queuing systems (QS), computer simulation modelling of packet flows (traffic) in modern multi-service networks is performed as a random self-similar process. Distribution laws such as exponential, Poisson and normal-logarithmic distributions, Pareto and Weibull distributions have been considered. The distribution of time intervals between arrivals of packages and the service duration of service of packages at different system loads has been studied. The research results show that the distribution function of time intervals between packet arrivals and the service duration of packages is in good agreement with the Pareto and Weibull distributions, but in most cases the Pareto distribution prevails. The queuing systems with the queues M/Pa/1 and Pa/M/1 has been studied, and the fractality of the intervals of requests arriving have been compared by the properties of the estimates of the system load and the service duration. It has been found out that in the system Pa/M/1, with the parameter of the form a> 2, the fractality of the intervals of requests arriving does not affect the average waiting time and load factor. However, when 𝑎≤2, as in the M/Pa/1 system, both considered statistical estimates differ. The application of adequate mathematical models of traffic allows to correctly assess the characteristics of the quality of service (QoS) of the network.