Information-Centric Connectivity

Mobile devices are often presented with multiple connectivity options usually making a selection either randomly or based on load/wireless conditions metrics, as is the case of current offloading schemes. In this paper we claim that link-layer connectivity can be associated with information-availability and in this respect connectivity decisions should be information-aware. This constitutes a next step for the Information-Centric Networking paradigm, realizing the concept of Information-Centric Connectivity (ICCON). We elaborate on different types of information availability and connectivity decisions in the context of ICCON, present specific use cases and discuss emerging opportunities, challenges and technical approaches. We illustrate the potential benefits of ICCON through preliminary simulation and numerical results in an example use case

On the evaluation of exact-match and range queries over multidimensional data in distributed hash tables

2012 Fall.Includes bibliographical references.The quantity and precision of geospatial and time series observational data being collected has increased alongside the steady expansion of processing and storage capabilities in modern computing hardware. The storage requirements for this information are vastly greater than the capabilities of a single computer, and are primarily met in a distributed manner. However, distributed solutions often impose strict constraints on retrieval semantics. In this thesis, we investigate the factors that influence storage and retrieval operations on large datasets in a cloud setting, and propose a lightweight data partitioning and indexing scheme to facilitate these operations. Our solution provides expressive retrieval support through range-based and exact-match queries and can be applied over massive quantities of multidimensional data. We provide benchmarks to illustrate the relative advantage of using our solution over a general-purpose cloud storage engine in a distributed network of heterogeneous computing resources

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

A web services based framework for efficient monitoring and event reporting.

Network and Service Management (NSM) is a research discipline with significant research contributions the last 25 years. Despite the numerous standardised solutions that have been proposed for NSM, the quest for an "all encompassing technology" still continues. A new technology introduced lately to address NSM problems is Web Services (WS). Despite the research effort put into WS and their potential for addressing NSM objectives, there are efficiency, interoperability, etc issues that need to be solved before using WS for NSM. This thesis looks at two techniques to increase the efficiency of WS management applications so that the latter can be used for efficient monitoring and event reporting. The first is a query tool we built that can be used for efficient retrieval of management state data close to the devices where they are hosted. The second technique is policies used to delegate a number of tasks from a manager to an agent to make WS-based event reporting systems more efficient. We tested the performance of these mechanisms by incorporating them in a custom monitoring and event reporting framework and supporting systems we have built, against other similar mechanisms (XPath) that have been proposed for the same tasks, as well as previous technologies such as SNMP. Through these tests we have shown that these mechanisms are capable of allowing us to use WS efficiently in various monitoring and event reporting scenarios. Having shown the potential of our techniques we also present the design and implementation challenges for building a GUI tool to support and enhance the above systems with extra capabilities. In summary, we expect that other problems WS face will be solved in the near future, making WS a capable platform for it to be used for NSM

A named data networking flexible framework for management communications

The ongoing changes in the way we use the Internet are motivating the definition of new information-distributing designs for an interworking layer. Recently, information-centric networking (ICN) concepts have defined mechanisms focusing on which information to get rather than where it is located. However, the still unfledged architectures instantiating such concepts have disregarded vital operations, such as management aspects aiming to optimize content retrieval by the User Equipment (UE). In this article, a flexible management framework is proposed, which enhances existing Named Data Networking (NDN) architectures in allowing both the network and the UE to employ management mechanisms over the NDN fabric. We illustrate our management framework with an implementation over an open-source NDN software, considering the specific case study of interface management. We quantitatively access the performance gains achieved through the usage of this framework in such scenarios, when compared to un-managed NDN mechanisms.This article has been partially supported by the Portuguese Foundation for Science and Technology (FCT) grant agreement SFRH / BD / 61629 / 2009, and by the Madrid Community through the MEDIANET project (S-2009/TIC- 1468).Publicad

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

Adaptive Video Streaming with Network Coding enabled Named Data Networking

The fast and huge increase of Internet traffic motivates the development of new communication methods that can deal with the growing volume of data traffic. To this aim, named data networking (NDN) has been proposed as a future Internet architecture that enables ubiquitous in-network caching and naturally supports multipath data delivery. Particular attention has been given to using dynamic adaptive streaming over HTTP to enable video streaming in NDN as in both schemes data transmission is triggered and controlled by the clients. However, state-of-the-art works do not consider the multipath capabilities of NDN and the potential improvements that multipath communication brings, such as increased throughput and reliability, which are fundamental for video streaming systems. In this paper, we present a novel architecture for dynamic adaptive streaming over network coding enabled NDN. In comparison to previous works proposing dynamic adaptive streaming over NDN, our architecture exploits network coding to efficiently use the multiple paths connecting the clients to the sources. Moreover, our architecture enables efficient multisource video streaming and improves resiliency to Data packet losses. The experimental evaluation shows that our architecture leads to reduced data traffic load on the sources, increased cache-hit rate at the in-network caches and faster adaptation of the requested video quality by the clients. The performance gains are verified through simulations in a Netflix-like scenario