Future of networking is the future of Big Data, The

2019 Summer.Includes bibliographical references.Scientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science

Integrating personal media and digital TV with QoS guarantees using virtualized set-top boxes: architecture and performance measurements

Nowadays, users consume a lot of functionality in their home coming from a service provider located in the Internet. While the home network is typically shielded off as much as possible from the `outside world', the supplied services could be greatly extended if it was possible to use local information. In this article, an extended service is presented that integrates the user's multimedia content, scattered over multiple devices in the home network, into the Electronic Program Guide (EPG) of the Digital TV. We propose to virtualize the set-top box, by migrating all functionality except user interfacing to the service provider infrastructure. The media in the home network is discovered through standard Universal Plug and Play (UPnP), of which the QoS functionality is exploited to ensure high quality playback over the home network, that basically is out of the control of the service provider. The performance of the subsystems are analysed

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Vehicular Ad Hoc Networks: Growth and Survey for Three Layers

A vehicular ad hoc network (VANET) is a mobile ad hoc network that allows wireless communication between vehicles, as well as between vehicles and roadside equipment. Communication between vehicles promotes safety and reliability, and can be a source of entertainment. We investigated the historical development, characteristics, and application fields of VANET and briefly introduced them in this study. Advantages and disadvantages were discussed based on our analysis and comparison of various classes of MAC and routing protocols applied to VANET. Ideas and breakthrough directions for inter-vehicle communication designs were proposed based on the characteristics of VANET. This article also illustrates physical, MAC, and network layer in details which represent the three layers of VANET. The main works of the active research institute on VANET were introduced to help researchers track related advanced research achievements on the subject

HIL: designing an exokernel for the data center

We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or