Multidimensional content modeling and caching in D2D edge networks

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Future Internet is going to be shaped by networked multimedia services with exploding video traffic becoming the dominant payload. That evolution requires a remedial shift from the connection-oriented architecture to a content-centric one. Another technique to address this capacity crunch is to improve spectral utilization through new networking paradigms at the wireless network edge. To this end, Device-to-Device (D2D) communications has the potential for boosting the capacity and energy efficiency for content-centric networking. To design and implement efficient content-centric D2D networks, rigorous content modeling and in-network caching mechanisms based on such models are crucial. In this work, we develop a multidimensional content model based on popularity, chunking and layering, and devise caching schemes through this model. Our main motivation is to improve the system performance via our caching strategies. The numerical analysis shows the interplay among different system parameters and performance metrics: while our schemes perform slightly poorer in terms of system goodput, they also decrease the system energy expenditure. Overall, this improvement dominates the loss in the goodput, leading to greater energy efficiency compared to the commonly-used caching technique Least Recently Used (LRU)

Enabling autoscaling for in-memory storage in cluster computing framework

2019 Spring.Includes bibliographical references.IoT enabled devices and observational instruments continuously generate voluminous data. A large portion of these datasets are delivered with the associated geospatial locations. The increased volumes of geospatial data, alongside the emerging geospatial services, pose computational challenges for large-scale geospatial analytics. We have designed and implemented STRETCH , an in-memory distributed geospatial storage that preserves spatial proximity and enables proactive autoscaling for frequently accessed data. STRETCH stores data with a delayed data dispersion scheme that incrementally adds data nodes to the storage system. We have devised an autoscaling feature that proactively repartitions data to alleviate computational hotspots before they occur. We compared the performance of S TRETCH with Apache Ignite and the results show that STRETCH provides up to 3 times the throughput when the system encounters hotspots. STRETCH is built on Apache Spark and Ignite and interacts with them at runtime

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

Streaming and User Behaviour in Omnidirectional Videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications and consequentially the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behaviour while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline while the latter one takes the benefit of understanding users’ behaviour and enable more personalised ODV streaming