Search

Organizations are increasingly relying on data to support decisions. When data contains private and sensitive information, the data owner often desires to publish a synthetic database instance that is similarly useful as the true data, while ensuring the privacy of individual data records. Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data -- the underlying correlations and dependencies among tuples and attributes (i.e., the structure of the data). This structure is often expressed as integrity and schema constraints, or with a probabilistic generative process. As a result, the synthesized data is not useful for any downstream tasks that require this structure to be preserved. This work presents Kamino, a data synthesis system to ensure differential privacy and to preserve the structure and correlations present in the original dataset. Kamino takes as input of a database instance, along with its schema (including integrity constraints), and produces a synthetic database instance with differential privacy and structure preservation guarantees. We empirically show that while preserving the structure of the data, Kamino achieves comparable and even better usefulness in applications of training classification models and answering marginal queries than the state-of-the-art methods of differentially private data synthesis., Update based on reviewers' comments

Data profiling is an important task to understand data semantics and is an essential pre-processing step in many tools. Due to privacy constraints, data is often partitioned into silos, with different access control. Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset. Simply applying general secure multi-party computation protocols incurs high computation and communication cost. This paper formulates the FD discovery problem in the secure multi-party scenario. We propose secure constructions for validating candidate FDs, and present efficient cryptographic protocols to discover FDs over distributed partitions. Experimental results show that solution is practically efficient over non-secure distributed FD discovery, and can significantly outperform general purpose multi-party computation frameworks. To the best of our knowledge, our work is the first one to tackle this problem.

In our previous study, we proposed a Restricted Color Number (RCN) algorithm based on graph coloring for interference coordination in cluster-wise distributed MU-MIMO in ultra-dense RAN. In this algorithm, we applied Delaunay Triangulation to help decide the interference relationship, which is different from the common-used threshold setting method. In this paper, we will discuss about the advantages of the Delaunay Triangulation method over the threshold method in graph coloring algorithm for interference coordination.

Object recognition based on LIDAR data is crucial in automotive driving and is the subject of extensive research. However, the lack of accuracy and stability in complex environments obstructs the practical application of real-time recognition algorithms. In this study, we proposed a new real-time network for multicategory object recognition. The manually extracted bird’s eye view (BEV) features were adopted to replace the resource-consuming 3D convolutional operation. Besides the subject network, we designed two auxiliary networks to help the network learn the pointwise features and boxwise features, aiming to improve the category and bounding boxes’ accuracy. The KITTI dataset was adopted to train and validate the proposed network. Experimental results showed that, for hard mode, the total average precision (AP) of the category reached 97.4%. For an intersection over a union threshold of 0.5 and 0.7, the total AP of regression reached 93.2% and 85.5%; especially, the AP of car’s regression reached 95.7% and 92.2%. The proposed network also showed consistent performance in the Apollo dataset with a processing duration of 37 ms. The proposed network exhibits stable and robust object recognition performance in complex environments (multiobject, unordered objects, and multicategory). And it shows sensitivity to occlusion of the LIDAR system and insensitivity to close large objects. The proposed multifunction method simultaneously achieves real-time operation, high accuracy, and stable performance, indicating its great potential value in practical application.

The ultra-dense radio access network with distributed MU-MIMO is considered as a promising approach to improve the coverage and the link capacity in 5G advanced systems. However, large-scale MU-MIMO requires prohibitively high computational complexity. Our previous work has proved that grouping neighborhood users/antennas into a number of clusters and performing cluster-wise MU-MIMO in parallel can alleviate the computational complexity problem, but the link capacity improvement is limited by the severe interference. In this paper, we propose a 2-step graph coloring algorithm that can eliminate both the inter-cell interferences and the inter-cluster interferences. The first step is to apply the graph coloring algorithm on the cell edge in order to reduce the inter-cell interferences. Once the color of the cell edge has been decided, the second step is to utilize the conditional graph coloring to the clusters within each BS cell. As a preliminary research, we focus on the second step and propose a Restricted Color Number (RCN) algorithm to mitigate the inter-cluster interferences. The computer simulation results show that our RCN algorithm can improve the link capacity compared with no coloring case.

The ultra-dense radio access network (RAN) consisting of a large number of distributed antennas and user terminals is considered to improve link capacity and coverage while saving the energy consumption for 5G advanced systems. Because of such a large-scale multi-user multiple-input multiple-output (MU-MIMO), the computational complexity of signal processing is difficult to realize. Aiming to reduce the computational complexity to an acceptable level, the clustering approach is promising to decompose the large-scale MU-MIMO into several smaller-scale ones. K-means clustering is introduced to form either user clusters or antenna clusters and then, cluster member assignment (CMA) is done to perform cluster-wise zero-forcing (ZF) based MU-MIMO. In this paper, we compare through computer simulation possible combinations of two clustering methods and two CMA methods in terms of achievable link capacity.

Data programming aims to reduce the cost of curating training data by encoding domain knowledge as labeling functions over source data. As such it not only requires domain expertise but also programming experience, a skill that many subject matter experts lack. Additionally, generating functions by enumerating rules is not only time consuming but also inherently difficult, even for people with programming experience. In this paper we introduce Ruler, an interactive system that synthesizes labeling rules using span-level interactive demonstrations over document examples. Ruler is a first-of-a-kind implementation of data programming by demonstration (DPBD). This new framework aims to relieve users from the burden of writing labeling functions, enabling them to focus on higher-level semantic analysis, such as identifying relevant signals for the labeling task. We compare Ruler with conventional data programming through a user study conducted with 10 data scientists who were asked to create labeling functions for sentiment and spam classification tasks. Results show Ruler is easier to learn and to use, and that it offers higher overall user-satisfaction while providing model performances comparable to those achieved by conventional data programming.

Objective: The lack of spiritual comfort for the empty nesters is a great harm to the elderly. There are few products on the market that combine the emotional detection and wearable devices for the elderly with empty nests. Product demand, emotional needs, to guide the design of empty nest elderly emotional detection products. Methods: Through the investigation and the emotional needs of the empty nest elderly, establish the functional requirements of the product; analyze the user’s functional demand indicators for the empty nest elderly emotional detection products, and obtain the user demand importance to guide the product design. Results: Complete the needs of the empty nest elderly for the emotional detection of wearable products, build the functional requirements of the product, reflect the demand for the product function of the empty nest elderly, and ensure that the product can better meet the specific needs of the empty nest elderly. Conclusion: It proves that the product design for the emotional detection products of the empty nest is broad market, in line with the future social trend, with practical feasibility, theoretical pertinence and development necessity.

There has been a recent flurry of interest in providing query capability on raw data in today's big data systems. These raw data must be parsed before processing or use in analytics. Thus, a fundamental challenge in distributed big data systems is that of efficient parallel parsing of raw data. The difficulties come from the inherent ambiguity while independently parsing chunks of raw data without knowing the context of these chunks. Specifically, it can be difficult to find the beginnings and ends of fields and records in these chunks of raw data. To parallelize parsing, this paper proposes a speculation-based approach for the CSV format, arguably the most commonly used raw data format. Due to the syntactic and statistical properties of the format, speculative parsing rarely fails and therefore parsing is efficiently parallelized in a distributed setting. Our speculative approach is also robust, meaning that it can reliably detect syntax errors in CSV data. We experimentally evaluate the speculative, distributed parsing approach in Apache Spark using more than 11,000 real-world datasets, and show that our parser produces significant performance benefits over existing methods.

Multimedia delivery over satellite network may be considered as a promising service in the emerging 5G cellular system. However, content delivery over the satellite link poses an enormous challenge to the service provider due to its long latency. The objective of this paper is to develop a video streaming framework over satellite and terrestrial integrated 5G content delivery infrastructure, which is investigated in the EU 5GPPP phase 2 SaT5G Project where satellite link is used as an additional channel as 5G backhaul. In this work, clients send SVC (Scalable Video Coding) layer download request to the Multi-access Edge Computing (MEC) server, which is able to dynamically select a backhaul link (Satellite or terrestrial) for each layer download request according to specific contexts. The purpose is to achieve optimised content load distribution between the two types of 5G backhaul links. Experiments conducted reveal that the proposed framework is able to achieve a good traffic offloading percentage without compromising with QoE of the end-user.

As over-the-top (OTT) applications such as videos dominate the global mobile traffic, conventional content delivery techniques such as caching no longer suffice to cope with mobile network users' requirements due to e.g., fluctuating radio conditions. In legacy 4G networks, mobile network operator (MNO) and OTT service providers (OSPs) are logically decoupled from each other, hence preventing them to share necessary context and enable in-network context-aware intelligence. The recently standardized 5G network architecture's softwarized and virtualized nature opens up new opportunities for flexible deployment of MNO- and OSP-operated network functions. In this work, we first extend the current 5G standard to enable third-party stakeholders to deploy their own user-plane functions (UPFs) within the MNO infrastructure. Based on this, we propose a service function chaining (SFC) framework within the 5G core network, which allows MNO to dynamically determine the optimal set of UPFs that each flow should traverse based on their real-time contexts. The proposed framework has been implemented in a testbed network. Through realistic experiments, we demonstrate that UPF deployment strategy plays a crucial rule in the resulting SFC performance, and our proposed scheme can achieve performance that is close to the benchmark. Furthermore, we establish recommendations on best practices of UPF deployment strategies in 5G network.

Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.

Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the high dependency on hyper-parameters. The algorithm results may fluctuate dramatically under the different configuration of hyper-parameters. Addressing the above issue, this paper presents an efficient Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning. We describe the OATM approach in five detailed steps and elaborate on it using two widely used deep neural network structures (Recurrent Neural Networks and Convolutional Neural Networks). The proposed method is compared to the state-of-the-art hyper-parameter tuning methods including manually (e.g., grid search and random search) and automatically (e.g., Bayesian Optimization) ones. The experiment results state that OATM can significantly save the tuning time compared to the state-of-the-art methods while preserving the satisfying performance.

Quorum sensing (QS) is a ubiquitous cell-cell communication mechanism in microbes that coordinates population-level cell behaviors, such as biofilm production, virulence, swarming motility, and bacterial persistence. Efforts to engineer QS systems to take part in metabolic network regulation represent a promising strategy for synthetic biology and pathway engineering. Recently, design, construction, and implementation of QS circuits for programmed control of bacterial phenotypes and metabolic pathways have gained much attention, but have not been reviewed recently. In this article, the architectural organizations and genetic contributions of the naturally occurring QS components to understand the mechanisms are summarized. Then, the most recent progress in application of QS toolkits to develop synthetic networks for novel cell behaviors creation and metabolic pathway engineering is highlighted. The current challenges in large-scale application of these QS circuits in synthetic biology and metabolic engineering fields are discussed and future perspectives for further engineering efforts are provided.

Satellite communications have recently been included as one of the enabling technologies for 5G backhauling, in particular for the delivery of bandwidth-demanding enhanced mobile broadband (eMBB) application data in 5G. In this paper we introduce a 5G-oriented network architecture empowered by satellite communications for supporting emerging mobile video delivery, which is investigated in the EU 5GPPP Phase 2 SaT5G Project. Two complementary use cases are introduced, including (1) the use of satellite links to support offline multicasting and caching of popular video content at 5G mobile edge, and (2) real-time prefetching of DASH (Dynamic Adaptive Streaming over HTTP) video segments by 5G mobile edge through satellite links. In both cases, the objective is to localize content objects close to consumers in order to achieve assured Quality of Experiences (QoE) in 5G content applications. In the latter case, in order to circumvent the large end-to-end propagation delay of satellite links, testbed based experiments have been carried out to identify specific prefetching policies to be enforced by the Multi-access computing server (MEC) for minimizing user perceived disruption during content consumption sessions.

Video applications using MPEG-DASH (Dynamic Adaptive Streaming over HTTP, such as YouTube and Netflix) have been dominating the Internet traffic in recent years. It is increasingly acknowledged that in order to provide video clients with better Quality-of-Experience (QoE), both content service providers and network operators need to be aware of clients' QoE in the first place. In this paper, we present a novel real-time QoE estimation system through edge computing, which has been implemented and deployed at a real LTE-A network edge. When equipped with such a system, any virtual network function (VNF) deployed in a mobile network will be able to infer all DASH clients' QoE under its coverage in real time, where no feedback from clients are needed. Furthermore, our scheme is able to work robustly in busy network environments involving air interface where packet errors frequently occur. The significance of such a scheme is the availability of accurate and real-time knowledge on user QoE through a very lightweight mechanism at the mobile edge, which can be instantaneously used for various content manipulation or resource adaptation operations in order to assure user QoE in dynamic conditions. Through experiments in a real LTE-A network, we demonstrate that our scheme is able to estimate DASH clients' QoE with very high accuracy with very low CPU and RAM footprint.

Internet video streaming applications have been demanding more bandwidth and higher video quality, especially with the advent of virtual reality and augmented reality appli-cations. While adaptive strea ming protocols like MPEG-DASH (dynamic adaptive streaming over HTTP) allows video quality to be flexibly adapted, e.g., degraded when mobile network condition deteriorates, this is not an option if the application itself requires guaranteed 4K quality at all time. On the other hand, conventional end-to-end transmission control protocol (TCP) has been struggling in supporting 4K video delivery across long-distance Internet paths containing both fixed and mobile network segments with heterogeneous characteristics. In this paper, we present a novel and practically feasible system architecture named MVP (mobile edge virtualization with adaptive prefetching), which enables content providers to embed their content intelligence as a virtual network function into the mobile network operator's infrastructure edge. Based on this architecture, we present a context-aware adaptive video prefetching scheme in order to achieve quality of experience (QoE)-assured 4K video on demand (VoD) delivery across the global Internet. Through experiments based on a real LTE-A network infrastructure, we demonstrate that our proposed scheme is able to achieve QoE-assured 4K VoD streaming, especially when the video source is located remotely in the public Internet, in which case none of the state-of-the-art solutions is able to support such an objective at global Internet scale.

With increased complexity of webpages nowadays, computation latency incurred by webpage processing during downloading operations has become a newly identified factor that may substantially affect user experiences in a mobile network. In order to tackle this issue, we propose a simple but effective transport-layer optimization technique which requires necessary context information dissemination from the mobile edge computing (MEC) server to user devices where such an algorithm is actually executed. The key novelty in this case is the mobile edge's knowledge about webpage content characteristics which is able to increase downloading throughput for user QoE enhancement. Our experiment results based on a real LTE-A test-bed show that, when the proportion of computation latency varies between 20% and 50% (which is typical for today's webpages), the downloading throughput can be improved up to 34.5%, with reduced downloading time by up to 25.1%

In a content delivery network (CDN), the energy cost is dominated by its geographically distributed data centers (DCs). Generally within a DC, the energy consumption is dominated by its server infrastructure and cooling system, with each contributing approximately half. However, existing research work has been addressing energy efficiency on these two sides separately. In this paper, we jointly optimize the energy consumption of both server infrastructures and cooling systems in a holistic manner. Such an objective is achieved through both strategies of: 1) putting idle servers to sleep within individual DCs; and 2) shutting down idle DCs entirely during off-peak hours. Based on these strategies, we develop a heuristic algorithm, which concentrates user request resolution to fewer DCs, so that some DCs may become completely idle and hence have the opportunity to be shut down to reduce their cooling energy consumption. Meanwhile, QoS constraints are respected in the algorithm to assure service availability and end-to-end delay. Through simulations under realistic scenarios, our algorithm is able to achieve an energy-saving gain of up to 62.1% over an existing CDN energy-saving scheme. This result is bound to be near-optimal by our theoretically-derived lower bound on energy-saving performance.

The design of a universal waveform generator derived from displaying driving applications using floating power and Miller integrator circuit is introduced first. Then, the feasibility of driving ultrasound transducers is verified by the test of the circuit and compared with other specific design of pulse generator for ultrasound driving purpose.

Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for answering queries with differential privacy place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget, and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quality they care about, namely accuracy of query answers. We present APEx, a novel system that allows data analysts to pose adaptively chosen sequences of queries along with required accuracy bounds. By translating queries and accuracy bounds into differentially private algorithms with the least privacy loss, APEx returns query answers to the data analyst that meet the accuracy bounds, and proves to the data owner that the entire data exploration process is differentially private. Our comprehensive experimental study on real datasets demonstrates that APEx can answer a variety of queries accurately with moderate to small privacy loss, and can support data exploration for entity resolution with high accuracy under reasonable privacy settings., Comment: Full version of the ACM SIGMOD 2019 paper

How to reduce power consumption within individual data centers has attracted major research efforts in the past decade, as their energy bills have contributed significantly to the overall operating costs. In recent years, increasing research efforts have also been devoted to the design of practical power-saving techniques in content delivery networks (CDNs), as they involve thousands of globally distributed data centers with content server clusters. In this paper, we present a comprehensive survey on existing research works aiming to save power in data centers and content delivery networks that share high degree of commonalities in different aspects. We firstly highlight the necessities of saving power in these two types of networks, followed by the identification of four major power-saving strategies that have been widely exploited in the literature. Furthermore, we present a high-level overview of the literature by categorizing existing approaches with respect to their scopes and research directions. These schemes are later analyzed with respect to their strategies, advantages and limitations. In the end, we summarize several key aspects that are considered to be crucial in effective power-saving schemes. We also highlight a number of our envisaged open research directions in the relevant areas that are of significance and hence require further elaborations.

In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching / replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes.

In this paper, we show how we use Nvidia GPUs and host CPU cores for faster query processing in a DB2 database using BLU Acceleration (DB2's column store technology). Moreover, we show the benefits and problems of using hardware accelerators (more specifically GPUs) in a real commercial Relational Database Management System(RDBMS).We investigate the effect of off-loading specific database operations to a GPU, and show how doing so results in a significant performance improvement. We then demonstrate that for some queries, using just CPU to perform the entire operation is more beneficial. While we use some of Nvidia's fast kernels for operations like sort, we have also developed our own high performance kernels for operations such as group by and aggregation. Finally, we show how we use a dynamic design that can make use of optimizer metadata to intelligently choose a GPU kernel to run. For the first time in the literature, we use benchmarks representative of customer environments to gauge the performance of our prototype, the results of which show that we can get a speed increase upwards of 2x, using a realistic set of queries.

With the rapid development of optical network technology, a thing worth noting is the generalized multi-protocol label switching (GMPLS) technology in optical network has been more used, mainly in auto-switching transmission network. GMPLS is an advanced protocol which is essential for managing a diverse and complex networks, GMPLS has caused technical challenges in its deployment in optical networks. In this paper, we analyze the evolution to optical network, and discuss the GMPLS technology and its applications. GMPLS will become the important protocol in future optical network.

In this paper, we analyse the optical burst switched (OBS) network, discuss the OBS node architecture. In OBS network, to construct a good data structure is very important. The OBS incoming data are assembled into basic units, referred to as data bursts, which are then transported over the optical core network. As to the OBS signaling, it is performed out of band by control packets that carry information such as the length, the edge-node-destination address. OBS is a promising solution for the all-optical WDM networks.

In this paper, a new routing design is presented. Interior gateway Protocols (IGPs), particularly Open Shortest Path First (OSPF), uses a simple single metric component, cost, which is based on the bandwidth of the link. Most large IP networks run Interior Gateway Protocols (IGPs) such as Open Shortest Path First (OSPF) or Intermediate System-Intermediate System (IS-IS) that select paths based on static link weights. Enhanced Interior Gateway Routing Protocol (EIGRP) is slightly more aware of the link characteristics in that it calculates a metric based on cumulative delay (delay is simply an arbitrary assigned value) and the minimum bandwidth value encountered between the source and destination. Routing technology can better the network operation efficiently.

In this study, the basic principle of the least squares support vector machine (LS- SVM) method was introduced systematically. The method was applied in a seawall engineering project to establish a prediction model of long-term seawall settlement based on the LS-SVM method, and the model parameters were optimized based on the genetic algorithm. Based on the monitoring data of seawall settlement during the construction of a soft foundation seawall project in Wenzhou, China, a sample was selected to train the model, and the predicted value of long-term settlement calculated by the model was compared with the measured sample value. The results show that, with highly accurate and reliable prediction results, the prediction model of long-term seawall settlement based on the LS-SVM method possesses great application value.

Following the adoption of basic temporal features in the SQL:2011 standard, there has been a tremendous interest within the database industry in supporting bi-temporal features, as a significant number of real-life workloads would greatly benefit from efficient temporal operations. However, current implementations of bi-temporal storage systems and operators are far from optimal. In this paper, we present the Bi-temporal Timeline Index, which supports a broad range of temporal operators and exploits the special properties of an in-memory column store database system. Comprehensive performance experiments with the TPC-BiH benchmark show that algorithms based on the Bi-temporal Timeline Index outperform significantly both existing commercial database systems and state-of-the-art data structures from research.

We have harnessed the salient features of information-centric networking (ICN) and implemented a communication infrastructure, called C-DAX, for supporting smart grid applications. We will demonstrate the operations of C-DAX both in a laboratory setup and a real field trial that involves the deployment of C-DAX in a live electricity grid in the Netherlands. This demo will showcase the capabilities of C-DAX, highlighting how ICN satisfies stringent smart grid application requirements.

Bi-temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., bi-temporal windows. We propose a two-dimensional index that supports one-time and continuous queries over fixed and sliding bi-temporal windows, covering static and streaming data. We demonstrate the advantages of the proposed index compared to the state-of-the-art in terms of query performance, index update overhead and space footprint.

We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an example, we show that our framework enables maintenance algorithms that outperform existing approaches in terms of space overhead, maintenance overhead, and dictionary lookup overhead during query execution.

A content delivery network (CDN) typically consists of geographically-distributed data centers, each containing servers that cache and deliver Web contents close to end-users for localized content access purpose. In recent years, optimization of CDN data center energy consumption has attracted increasing research efforts. The key technical challenge is the tradeoff between CDN energy consumption and the content service capability at both the server and the network sides. In this article, we introduce a data center management scheme that effectively reduces the energy consumption of cross-domain CDNs through dynamically provisioning servers and coordinating content delivery operations with respect to dynamic server and network load. The proposed scheme focuses on optimizing the energy-performance tradeoff in two aspects. On one hand, servers in CDN data centers are put to the sleep mode during off-peak hours to save energy. On the other hand, CDN Quality-of-Service (QoS) performance is assured through honoring constraints on servers and network link loads, especially through restricting inter-domain content traffic volume. As a result, the proposed scheme is able to reduce CDN data center energy consumption without compromising its end-to-end QoS performance. According to our experiments based on realistic CDN scenarios, the proposed scheme is able to reduce data center energy consumption by up to 45.9% while achieving desired QoS performance.

Optimizing server's power consumption in content distribution infrastructure has attracted increasing research efforts. The technical challenge is the tradeoff between server power consumption and the content service capability on both the server and the network side. This paper proposes and evaluates a novel approach that optimizes content servers' power consumptions in large-scale content distribution platforms across multiple ISP domains. Specifically, our approach strategically puts servers to sleep mode without violating load capacities of virtual content delivery links and active servers in the infrastructure. Such a problem can be formulated into a nonlinear programming model. The efficiency of our approach is evaluated in a content distribution topology covering two real interconnected domains. The simulation has shown that our approach is capable of reducing servers' power consumptions by up to 62.2%, while maintaining the actual service performance in an acceptable scope.

This paper firstly analyzes current Chinese word segmentation methods, and then bases on word dictionary segmentation and binary search tree (BST) proposing a way to organize the dictionary which focuses on reducing comparison times in order to increase the segmentation speed. After that, a practical demo is used to illustrate the feasibility and effectiveness of the proposed algorithm.

Online Analytical Processing (OLAP) was widely used to visualize complex data for efficient, interactive and meaningful analysis. Its power comes in visualizing huge operational data for interactive analysis. Data mining techniques (DM) are strong at detecting pattern sand mining knowledge from historical data. OLAP and DM are believed to be able to complement each other to analyze large data sets in decision support systems. Some recent researches have shown the benefits of combining OLAP with Data Mining. In this paper, we review the OLAM and OLAP and data mining, and identify their limitations. We propose a conceptual model that overcomes the existing limitations, and provide a way for combining OLAP with OLAM. Furthermore, the proposed model offers directions to improving cube's construction time and visualization over the data cube.

this paper introduces the Rational Unified Process and the eXtreme Programming, comparing and analyzing their advantages and disadvantages. Then it customizes these two process models and works out a preliminary integration framework plan.

Satellite communication has recently been included as one of the key enabling technologies for 5G backhauling, especially for the delivery of bandwidth-demanding enhanced mobile broadband (eMBB) applications in 5G. In this paper, we present a 5G-oriented network architecture that is based on satellite communications and multi-access edge computing to support eMBB applications, which is investigated in the EU 5GPPP phase-2 satellite and terrestrial network for 5G project. We specifically focus on using the proposed architecture to assure quality-of-experience (QoE) of HTTP-based live streaming users by leveraging satellite links, where the main strategy is to realize transient holding and localization of HTTP-based (e.g., MPEG-DASH or HTTP live streaming) video segments at 5G mobile edge while taking into account the characteristics of satellite backhaul link. For the very first time in the literature, we carried out experiments and systematically evaluated the performance of live 4K video streaming over a 5G core network supported by a live geostationary satellite backhaul, which validates its capability of assuring live streaming users’ QoE under challenging satellite network scenarios.