71 research outputs found
Time-Shifted Prefetching and Edge-Caching of Video Content: Insights, Algorithms, and Solutions
Video traffic accounts for 82% of global Internet traffic and is growing at an unprecedented rate. As a result of this rapid growth and popularity of video content, the network is heavily burdened. To cope with this, service providers have to spend several millions of dollars for infrastructure upgrades; these upgrades are typically triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5 times as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collected YouTube and Netflix usage from over 1500 users spanning at least a one-year period consisting of approximately 8.5 million videos collectively watched. We use the datasets to analyze and present key insights about user-level usage behavior, and show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. Thereafter, equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching solutions, using machine-learning and deep-learning techniques (specifically supervised classifiers and LSTM networks), which anticipates the video content the user will consume based on their prior watching behavior, and prefetches it during off-peak periods. We find that our developed solutions can reduce nearly 35% of peak-time YouTube traffic and 70% of peak-time Netflix series traffic. We developed and evaluated a proof-of-concept system for prefetching video traffic. We also show how to integrate the two systems for prefetching YouTube and Netflix content. Furthermore, based on our findings from our developed algorithms, we develop a framework for prefetching video content regardless of the type of video and platform upon which it is hosted.Ph.D
Management of spatial data for visualization on mobile devices
Vector-based mapping is emerging as a preferred format in Location-based
Services(LBS), because it can deliver an up-to-date and interactive map visualization.
The Progressive Transmission(PT) technique has been developed to
enable the ecient transmission of vector data over the internet by delivering
various incremental levels of detail(LoD). However, it is still challenging to apply
this technique in a mobile context due to many inherent limitations of mobile
devices, such as small screen size, slow processors and limited memory. Taking
account of these limitations, PT has been extended by developing a framework of
ecient data management for the visualization of spatial data on mobile devices.
A data generalization framework is proposed and implemented in a software
application. This application can signicantly reduce the volume of data for
transmission and enable quick access to a simplied version of data while preserving
appropriate visualization quality. Using volunteered geographic information
as a case-study, the framework shows
exibility in delivering up-to-date spatial
information from dynamic data sources.
Three models of PT are designed and implemented to transmit the additional
LoD renements: a full scale PT as an inverse of generalisation, a viewdependent
PT, and a heuristic optimised view-dependent PT. These models are
evaluated with user trials and application examples. The heuristic optimised
view-dependent PT has shown a signicant enhancement over the traditional PT
in terms of bandwidth-saving and smoothness of transitions.
A parallel data management strategy associated with three corresponding
algorithms has been developed to handle LoD spatial data on mobile clients.
This strategy enables the map rendering to be performed in parallel with a process
which retrieves the data for the next map location the user will require. A viewdependent
approach has been integrated to monitor the volume of each LoD for
visible area. The demonstration of a
exible rendering style shows its potential
use in visualizing dynamic geoprocessed data. Future work may extend this
to integrate topological constraints and semantic constraints for enhancing the
vector map visualization
Modeling and Compostion of Environment-as-a-Service
Wireless-enabled electronic devices are becoming cheaper, more powerful and thus more popular. They include sensors, actuators, smartphones, tablets, wearable devices, and other complex devices. They can carry out complex tasks, cooperating with their ``neighbors''. However, it is difficult to develop mobile applications to exploit the full power of available resources because the computational capabilities on devices are not homogeneous, and their connectivity changes with physical movement. We propose a mobile environment model to describe the connected devices and study the structural and behavioral characteristics of the environments. Based on the model, we design the routing protocols and a language to support the composition of environments. We propose a framework to provide a unified, flexible and scalable service for task/process deployment and execution on top of the heterogeneous and dynamic mobile environments. We compare different architectures, and discuss the optimization of resources discovery and routing algorithm. A proof-of-concept framework is implemented and shows the feasibility of our Environment-as-a-Service approach. Finally, we explore the theoretical principles and practical techniques for performance optimization, including a data prefetching technique and a dynamic process/task allocation algorithm
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Collaborative Filtering-Based In-Network Content Placement and Caching for 5G Networks
As the number of wireless devices, the demand for high data rates, and the need for always-on connectivity are growing and becoming more stringent with the evolvement and emergence of 5G systems, network engineers and researchers are being faced with new unique challenges that need to be addressed. Among many challenges, tra�c congestion bottleneck at back-haul links arising from the massive connections emerges as one key challenge that 5G systems need to tackle. One solution approach that has been investigated as a key enabler for addressing such tra�c bottlenecks is in-network content caching, where frequently-accessed content is placed closer to end users at the network edges so that the amounts of tra�c that need to traverse core network and back-haul links are reduced. In this thesis, we propose a content placement and caching technique that leverages collaborative �ltering and k-means clustering to make e�cient content placement decisions, thereby reducing downloading time and back-haul tra�c. We simulate the proposed technique and compare it with two other existing caching techniques, and show that the proposed approach outperforms existing ones by achieving higher hit ratios, reducing backhaul tra�c, and decreasing download times. We therefore show that the proposed technique improves the users' quality of experience by minimizing network latency and the overall network performance by alleviating backhaul tra�c congestions
Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming
Video streaming is in high demand by mobile users, as recent studies
indicate. In cellular networks, however, the unreliable wireless channel leads
to two major problems. Poor channel states degrade video quality and interrupt
the playback when a user cannot sufficiently fill its local playout buffer:
buffer underruns occur. In contrast to that, good channel conditions cause
common greedy buffering schemes to pile up very long buffers. Such
over-buffering wastes expensive wireless channel capacity.
To keep buffering in balance, we employ a novel approach. Assuming that we
can predict data rates, we plan the quality and download time of the video
segments ahead. This anticipatory scheduling avoids buffer underruns by
downloading a large number of segments before a channel outage occurs, without
wasting wireless capacity by excessive buffering. We formalize this approach as
an optimization problem and derive practical heuristics for segmented video
streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed
measurements show that our solution essentially eliminates playback
interruptions without significantly decreasing video quality
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
Balancing Interactive Performance and Budgeted Resources in Mobile Computing.
In this dissertation, we explore the various limited resources involved in mobile applications --- battery energy, cellular data usage, and, critically, user attention --- and we devise principled methods for managing the tradeoffs involved in creating a good user experience. Building quality mobile applications requires developers to understand complex interactions between network usage, performance, and resource consumption. Because of this
difficulty, developers commonly choose simple but suboptimal approaches that strictly prioritize performance or resource conservation.
These extremes are symptoms of a lack of system-provided abstractions for managing the complexity inherent in managing performance/resource tradeoffs. By providing abstractions that help applications manage these tradeoffs, mobile systems can significantly improve user-visible performance without exhausting resource budgets. This dissertation explores three such abstractions in detail. We first present Intentional
Networking, a system that provides synchronization primitives and intelligent scheduling for multi-network traffic. Next, we present Informed Mobile Prefetching, a system that helps applications decide when to prefetch data and how aggressively to spend limited battery energy and cellular data resources toward that end. Finally, we present Meatballs, a library that helps applications consider the cloudy nature of predictions when making decisions, selectively employing redundancy to mitigate uncertainty and provide more
reliable performance. Overall, experiments show that these abstractions can significantly reduce interactive delay without overspending the available energy and data resources.PHDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108956/1/brettdh_1.pd
Collaborative Traffic Offloading for Mobile Systems
Due to the popularity of smartphones and mobile streaming services, the growth of traffic volume in mobile networks is phenomenal. This leads to huge investment pressure on mobile operators' wireless access and core infrastructure, while the profits do not necessarily grow at the same pace. As a result, it is urgent to find a cost-effective solution that can scale to the ever increasing traffic volume generated by mobile systems. Among many visions, mobile traffic offloading is regarded as a promising mechanism by using complementary wireless communication technologies, such as WiFi, to offload data traffic away from the overloaded mobile networks. The current trend to equip mobile devices with an additional WiFi interface also supports this vision.
This dissertation presents a novel collaborative architecture for mobile traffic offloading that can efficiently utilize the context and resources from networks and end systems. The main contributions include a network-assisted offloading framework, a collaborative system design for energy-aware offloading, and a software-defined networking (SDN) based offloading platform. Our work is the first in this domain to integrate energy and context awareness into mobile traffic offloading from an architectural perspective. We have conducted extensive measurements on mobile systems to identify hidden issues of traffic offloading in the operational networks. We implement the offloading protocol in the Linux kernel and develop our energy-aware offloading framework in C++ and Java on commodity machines and smartphones. Our prototype systems for mobile traffic offloading have been tested in a live environment. The experimental results suggest that our collaborative architecture is feasible and provides reasonable improvement in terms of energy saving and offloading efficiency. We further adopt the programmable paradigm of SDN to enhance the extensibility and deployability of our proposals. We release the SDN-based platform under open-source licenses to encourage future collaboration with research community and standards developing organizations. As one of the pioneering work, our research stresses the importance of collaboration in mobile traffic offloading. The lessons learned from our protocol design, system development, and network experiments shed light on future research and development in this domain.Yksi mobiiliverkkojen suurimmista haasteista liittyy liikennemäärien eksponentiaaliseen kasvuun. Tämä verkkoliikenteen kasvu johtuu pitkälti suosituista videopalveluista, kuten YouTube ja Netflix, jotka lähettävät liikkuvaa kuvaa verkon yli. Verkon lisääntynyt kuormitus vaatii investointeja verkon laajentamiseksi. On tärkeää löytää kustannustehokkaita tapoja välittää suuressa mittakaavassa sisältöä ilman mittavia infrastruktuuri-investointeja.
Erilaisia liikennekuormien ohjausmenetelmiä on ehdotettu ratkaisuksi sisällönvälityksen tehostamiseen mobiiliverkoissa. Näissä ratkaisuissa hyödynnetään toisiaan tukevia langattomia teknologioita tiedonvälityksen tehostamiseen, esimerkiksi LTE-verkosta voidaan delegoida tiedonvälitystä WiFi-verkoille. Useimmissa kannettavissa laitteissa on tuki useammalle langattomalle tekniikalle, joten on luonnollista hyödyntää näiden tarjoamia mahdollisuuksia tiedonvälityksen tehostamisessa.
Tässä väitöskirjassa tutkitaan liikennekuormien ohjauksen toimintaa ja mahdollisuuksia mobiiliverkoissa. Työssä esitetään uusi yhteistyöpohjainen liikennekuormien ohjausjärjestelmä, joka hyödyntää päätelaitteiden ja verkon tilannetietoa liikennekuormien optimoinnissa. Esitetty järjestelmä ja arkkitehtuuri on ensimmäinen, joka yhdistää energiankulutuksen ja kontekstitiedon liikennekuormien ohjaukseen.
Väitöskirjan keskeisiä tuloksia ovat verkon tukema liikennekuormien ohjauskehikko, yhteistyöpohjainen energiatietoinen optimointiratkaisu sekä avoimen lähdekoodin SoftOffload-ratkaisu, joka mahdollistaa ohjelmistopohjaisen liikennekuormien ohjauksen. Esitettyjä järjestelmiä arvioidaan kokeellisesti kaupunkiympäristöissä älypuhelimia käyttäen. Työn tulokset mahdollistavat entistä energiatehokkaammat liikennekuormien ohjausratkaisut ja tarjoavat ideoita ja lähtökohtia tulevaan 5G kehitystyöhön
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