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The systems used to process data streams and provide for the needs of stream-based applications are Data Stream Management Systems (DSMSs). This book presents a new paradigm to meet the needs of these applications, including a detailed discussion of the techniques proposed. Ii includes important aspects of a QoS-driven DSMS (Data Stream Management System) and introduces applications where a DSMS can be used and discusses needs beyond the stream processing model. It also discusses in detail the design and implementation of MavStream. This volume is primarily intended as a reference book for researchers and advanced-level students in computer science. It is also appropriate for practitioners in industry who are interested in developing applications.
Data stream processing in the industrial as well as in the academic field has gained more and more importance during the last years. Consider the monitoring of industrial processes as an example. There, sensors are mounted to gather lots of data within a short time range. Storing and post-processing these data may occasionally be useless or even impossible. On the one hand, only a small part of the monitored data is relevant. To efficiently use the storage capacity, only a preselection of the data should be considered. On the other hand, it may occur that the volume of incoming data is generally too high to be stored in time or-in other words-the technical efforts for storing the data in time would be out of scale. Processing data streams in the context of this thesis means to apply database operations to the stream in an on-the-fly manner (without explicitly storing the data). The challenges for this task lie in the limited amount of resources while data streams are potentially infinite. Furthermore, data stream processing must be fast and the results have to be disseminated as soon as possible. This thesis focuses on the latter issue. The goal is to provide a so-called Quality-of-Service (QoS) for the data stream processing task. Therefore, adequate QoS metrics like maximum output delay or minimum result data rate are defined. Thereafter, a cost model for obtaining the required processing resources from the specified QoS is presented. On that basis, the stream processing operations are scheduled. Depending on the required QoS and on the available resources, the weight can be shifted among the individual resources and QoS metrics, respectively. Calculating and scheduling resources requires a lot of expert knowledge regarding the characteristics of the stream operations and regarding the incoming data streams. Often, this knowledge is based on experience and thus, a revision of the resource calculation and reservation becomes necessary from time to time. This leads.
A large part of this big data is most valuable when analysed quickly, as it is generated. Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, and Internet of Things (IoT), continuous data streams must be processed under very short delays. In multiple domains, there is a need for processing data streams to detect patterns, identify failures, and gain insights. Data is often gathered and analysed by Data Stream Processing Engines (DSPEs).A DSPE commonly structures an application as a directed graph or dataflow. A dataflow has one or multiple sources (i.e., gateways or actuators); operators that perform transformations on the data (e.g., filtering); and sinks (i.e., queries that consume or store the data). Most complex operator transformations store information about previously received data as new data is streamed in. Also, a dataflow has stateless operators that consider only the current data. Traditionally, Data Stream Processing (DSP) applications were conceived to run in clusters of homogeneous resources or on the cloud. In a cloud deployment, the whole application is placed on a single cloud provider to benefit from virtually unlimited resources. This approach allows for elastic DSP applications with the ability to allocate additional resources or release idle capacity on demand during runtime to match the application requirements.We introduce a set of strategies to place operators onto cloud and edge while considering characteristics of resources and meeting the requirements of applications. In particular, we first decompose the application graph by identifying behaviours such as forks and joins, and then dynamically split the dataflow graph across edge and cloud. Comprehensive simulations and a real testbed considering multiple application settings demonstrate that our approach can improve the end-to-end latency in over 50% and even other QoS metrics. The solution search space for operator reassignment can be enormous depending on the number of operators, streams, resources and network links. Moreover, it is important to minimise the cost of migration while improving latency. Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) have been used to tackle problems with large search spaces and states, performing at human-level or better in games such as Go. We model the application reconfiguration problem as a Markov Decision Process (MDP) and investigate the use of RL and MCTS algorithms to devise reconfiguring plans that improve QoS metrics.
This volume is the first part of a four-volume set (CCIS 190, CCIS 191, CCIS 192, CCIS 193), which constitutes the refereed proceedings of the First International Conference on Computing and Communications, ACC 2011, held in Kochi, India, in July 2011. The 68 revised full papers presented in this volume were carefully reviewed and selected from a large number of submissions. The papers are organized in topical sections on ad hoc networks; advanced micro architecture techniques; autonomic and context-aware computing; bioinformatics and bio-computing; cloud, cluster, grid and P2P computing; cognitive radio and cognitive networks; cyber forensics; database and information systems.
Wireless Sensor Network Technologies for Information Explosion Era The amount and value of information available due to rapid spread of information technology is exploding. Typically, large enterprises have approximately a petabyte of operational data stored in hundreds of data repositories supporting thousands of applications. Data storage volumes grow in excess of 50% annually. This growth is expected to continue due to vast proliferation of existing infor- tion systems and the introduction of new data sources. Wireless Sensor Networks (WSNs) represent one of the most notable examples of such new data sources. In recent few years, various types of WSNs have been deployed and the amount of information generated by wireless sensors increases rapidly. The information - plosion requires establishing novel data processing and communication techniques for WSNs. This volume aims to cover both theoretical and practical aspects - lated to this challenge, and it explores directions for future research to enable ef- cient utilization of WSNs in the information-explosion era. The book is organized in three main parts that consider (1) technical issues of WSNs, (2) the integration of multiple WSNs, and (3) the development of WSNs systems and testbeds for conducting practical experiments. Each part consists of three chapters.
This book constitutes the proceedings of the 18th IFIP International Conference on Distributed Applications and Interoperable Systems, DAIS 2018, held in Madrid, Spain, in June 2018. The 10 papers presented together with 2 short papers in this volume were carefully reviewed and selected from 33 submissions. The papers are organized in topical sections on application domains, including stream processing, video dissemination, storage, privacy protection, and large-scale orchestration.
This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems. It establishes the theoretical foundations and principles of real-time linked dataspaces as a data platform for intelligent systems. The book introduces a set of specialized best-effort techniques and models to enable loose administrative proximity and semantic integration for managing and processing events and streams. The book is divided into five major parts: Part I “Fundamentals and Concepts” details the motivation behind and core concepts of real-time linked dataspaces, and establishes the need to evolve data management techniques in order to meet the challenges of enabling data ecosystems for intelligent systems within smart environments. Further, it explains the fundamental concepts of dataspaces and the need for specialization in the processing of dynamic real-time data. Part II “Data Support Services” explores the design and evaluation of critical services, including catalog, entity management, query and search, data service discovery, and human-in-the-loop. In turn, Part III “Stream and Event Processing Services” addresses the design and evaluation of the specialized techniques created for real-time support services including complex event processing, event service composition, stream dissemination, stream matching, and approximate semantic matching. Part IV “Intelligent Systems and Applications” explores the use of real-time linked dataspaces within real-world smart environments. In closing, Part V “Future Directions” outlines future research challenges for dataspaces, data ecosystems, and intelligent systems. Readers will gain a detailed understanding of how the dataspace paradigm is now being used to enable data ecosystems for intelligent systems within smart environments. The book covers the fundamental theory, the creation of new techniques needed for support services, and lessons learned from real-world intelligent systems and applications focused on sustainability. Accordingly, it will benefit not only researchers and graduate students in the fields of data management, big data, and IoT, but also professionals who need to create advanced data management platforms for intelligent systems, smart environments, and data ecosystems.
"This book highlights and discusses the underlying QoS issues that arise in the delivery of real-time multimedia services over wireless networks"--Provided by publisher.
Research Paper (postgraduate) from the year 2007 in the subject Computer Science - Internet, New Technologies, grade: PG, VIT University (Net Research Labs), 26 entries in the bibliography, language: English, abstract: One of the major challenges faced by MANET researchers is the deployment of end-to-end quality-of-service support mechanisms for streaming media services over a group of MANET users. Group-oriented services over large, dynamically changing MANET networks has a big impact on the needs of streaming services communication in terms of mobility, quality of service (QoS) support and multicasting. In MANET networks, where such features are not embedded with its architecture, it is necessary to develop QoS multicasting strategies. The research work focuses on the basic building blocks of an mobile ad hoc group communication scheme, which achieves multicasting optimal QoS efficiency OptiQ by tracking resource availability in a node's neighborhood based on resource reservations, which announces the required QoS before each session initiation. The primary quality of service (QoS) issues such as required bandwidth, message delay, traffic type and hop count per route improves the efficiency of streaming services over ad-hoc network. Streaming services support voice, data and video traffic by assessing and adjusting for various levels of QoS. The performance analysis is performed on functional prototype of OptiQ in mobile / wireless ad-hoc network with emphasis on service satisfaction for multiple group conference sessions. The performance of OptiQ scheme is well compared with QoS-aware versions of AODV and TORA, well-known ad-hoc routing and limited QoS protocols. Using the SPRUCE bandwidth traffic gathering tool, with a set of C++ modules an extensive set of performance experiments were conducted for these protocols with OptiQ on a wide variety of mobility patterns and reservation strategies. The results shows the performance analysis of OptiQ is better than