On the Feasibility of a User-Operated Mobile Content Distribution - - PowerPoint PPT Presentation

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On the Feasibility of a User-Operated Mobile Content Distribution - - PowerPoint PPT Presentation

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On the Feasibility of a User-Operated Mobile Content Distribution Network Ioannis Psaras , Vasilis Sourlas, Denis Shtefan, Sergi Re and George Pavlou University College London, UK Mayutan Arumaithurai University of Goettingen, Germany Dirk

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On the Feasibility of a User-Operated Mobile Content Distribution Network

Ioannis Psaras, Vasilis Sourlas, Denis Shtefan, Sergi Reñé and George Pavlou

University College London, UK

Mayutan Arumaithurai

University of Goettingen, Germany

Dirk Kutscher

Huawei German Research Center

iCore & CommNet2 workshop on

Content Caching and Distributed Storage for Future Communication Networks

June 20, 2017, Imperial College, London

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Content Distribution Network (aka CDN) User-Operated Mobile

Data caps cannot keep up with demand for mobile video delivery

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Facts I: CDNs focus on the fixed domain

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Facts II: Mobile Video will Skyrocket

*Ericsson Mobility Report, 2016

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Mobile Data in terms of Video

One hour of streaming per day (e.g., during commuting) consumes a 2GB data plan in less than 10 days!

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Mobile micro-datacentres

All modern smartphones have at least 16GBs of memory 16 GBs of memory translates to nearly 1,000 minutes of YouTube

  • r 100 10-min YouTube videos

Modern smartphone devices are always-on, always- connected, mobile data-centres for short audio/video-clips

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Working Example

  • Assume:

ü BBC application installed in 10M end-user devices – that’s roughly 1 in 6 devices you see around (in the UK) ü End-users split in: 1) source, 2) destination, and 3) relay nodes

  • Picture this:

① Content Providers (CPs), say BBC, publish one new video-clip every 1 hour ② CPs push the video to a limited number of source nodes – source nodes have prior agreement with CPs ③ Source nodes exploit mobility to update destination nodes ④ Once updated, destination nodes can act as relay nodes for a limited amount of time.

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Working Example

  • Assume:

ü BBC application installed in 10M end-user devices – that’s roughly 1 in 6 devices you see around (in the UK) ü End-users split in: 1) source, 2) destination, and 3) relay nodes

  • Picture this:

① Content Providers (CPs), say BBC, publish one new video-clip every 1 hour ② CPs push the video to a limited number of source nodes – source nodes have prior agreement with CPs ③ Source nodes exploit mobility to update destination nodes ④ Once updated, destination nodes can act as relay nodes for a limited amount of time.

Result: Huge amounts of content is proactively put in users’ devices in an application-centric manner. Challenge: Can we have every video-clip pre-loaded to the users’ devices before new content comes out (i.e., within 1h)?

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ubiCDN a distributed and ubiquitous content distribution network for data delivery at the mobile domain. ubiCDN exploits user mobility in urban environments to proactively distribute non-real time content Content spreads through smart, Information-Centric Connectivity

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ubiCDN Components

  • Node Groups

– Source nodes: get new content pushed to their devices – Destination nodes: passively wait to receive updates – Relay nodes: act as source nodes for limited time

  • D2D Information-Aware and Application-Centric Connectivity

– WiFi Direct Generic Advertisement Protocol (GAS) – Devices advertise services/applications, e.g., BBC-Sports-11am

  • Incentives

– Source and Relay nodes are compensated – Compensation proportional to content distributed

  • Data Integrity/Content authentication

– Digital certificates from CPs – Digital Signatures based on Public Key Infrastructure (PKI) – Source and Relay nodes: Storage Delegates *K.V. Katsaros et. al. “Information-Centric Connectivity”, IEEE Communications Magazine, August 2016.

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ubiCDN

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ubiCDN

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ubiCDN

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ubiCDN

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Information-Aware and Application-Centric Connectivity

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Target of this study

Feasibility of a user-operated CDN

  • define “Feasibility”
  • Metrics:

– Satisfaction rate: percentage of nodes updated within update interval – Overhead: duplicates, messages of no interest or incomplete transfers – Relayed content: percentage of messages delivered by relay nodes – Energy consumption: what percentage of battery is consumed for ubiCDN

* We define this as “update interval” and set it to 1 hour.

What percentage of population is updated within reasonable time-frames*? F1: How many source nodes are needed? F2: What’s the impact of relaying? F3: What’s the impact on battery?

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Evaluation: Setup and Assumptions

  • ubiCDN implemented on the ONE simulator.
  • Set of 10 applications, Pareto-distributed by popularity and

randomly distributed among users (at least one application per user).

  • We compare it with Floating Content.

*Joerg Ott et al. www.floating-content.net

Floating Content

  • Messages stay within some

area

  • Messages live for some specific

amount of time

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Evaluation: Setup and Assumptions

Helsinki simulation area

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Evaluation: Setup and Assumptions

  • Urban movement: 8.3km x 7.3km area
  • Multiple movement patterns map-based defined:

– Source Nodes (50):

  • 18 Buses on predefined routes.
  • 32 working day movement model with 50% evening activity

– Destination Nodes (1000):

  • Tourists (20% of destination nodes): Random travel destinations

including “points of interest” to which they travel following the shortest path, wait randomly between 2-15 minutes and then move again.

  • Workers (80% of destination nodes): Working day movement model:

Home to work (for 7 hours) + 50% probability of evening activity, before travelling back home

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Evaluation: Setup and Assumptions

Parameter Value Number of Applications 10 Number of Source Nodes 50 Number of Destination Nodes 1000 Size of each message 5 MBs

  • App. update period

1 hour D2D Link Capacity 31.25Mbps Radio Range 60 m

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Feasibility 1: Number of source nodes

Exponential increase Flooding is more efficient, but… 5% of nodes reach out to 60% of population

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Feasibility 1: Number of source nodes

Less than 10%

  • verhead –

mainly due to mobility Significant

  • verhead –

up to 50%

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Feasibility 2: Impact of Relaying

Substantial gain (up to 40%) after 5-15mins ubiCDN gains from up to 30mins of relaying

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Feasibility 2: Impact of Relaying Up to 90%

  • verhead

using fltCDN Bounded to 20% for ubiCDN Space for Optimisation: Least popular applications cause little

  • verhead
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Feasibility 2: Impact of Relaying

More than 40% (ubiCDN) / 80% (fltCDN) of distribution comes from relaying

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Feasibility 2: Impact of Relaying

Most nodes get updated within the first 20-25 mins

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Feasibility 3: Energy – the price to pay

10 20 30 40 5 MB 50 MB 100 MB % Battery Content update size

Energy Consumption Source nodes

ubiCDN fltCDN 5 10 5 MB 50 MB 100 MB %Battery Content update size

Energy Consumption Relay nodes

ubiCDN fltCDN

15x less consumption ~ 1% ~ 1,5% ~ 2% ~ 15% ~ 25% ~ 30%

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Conclusions

Data Caps cannot follow demand for mobile vide

  • Expected to be about 8GBs in 2020

CDNs cannot reach the mobile domain

  • Can’t put a server after the BS

Pressing need for a solution to distribute heavy content in the mobile domain. User devices as micro-data centres: Opportunity not to be missed At least 50% of users updated within 30mins Energy consumption is as low as 1% of battery capacity per hour. Information-Centric Connectivity is necessary in this case

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Key Publications

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  • I. Psaras, L. Saino, M. Arumaithurai, K.K.

Ramakrishnan, G. Pavlou, “Name-Based Replication Priorities in Disaster Cases” IEEE INFOCOM NOM Workshop 2014

  • I. Psaras, S. Rene, K.V. Katsaros, V. Sourlas, N.

Bezirgiannidis, S. Diamantopoulos, I. Komnios, V. Tsaoussidis, G. Pavlou “KEBAPP: Keyword-Based Mobile Application Sharing” ACM MobiArch 2016 Best Paper Award

Hierarchical Part z }| { /a/b/c/ | {z } App Market App Developer ⊕ Hash Tags z }| { #tag1, #tag2 | {z } App Developer

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ICN Information-Resilience

“Information Resilience Through User-Assisted Caching in Disruptive Content-Centric Networks”

  • V. Sourlas, L. Tassiulas, I. Psaras, G. Pavlou

IFIP NETWORKING 2015 Best Paper Award “Opportunistic Off-Path Content Discovery in Information-Centric Networks”

  • O. Ascigil, V. Sourlas, I. Psaras, G. Pavlou

IEEE LANMAN 2016 Best Paper Award

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INRPP: In-Network Resource Pooling

A B C A B C Ti Ti+1

  • I. Psaras, L. Saino, G. Pavlou

“Revisiting Resource Pooling: the Case for In-Network Resource Sharing” ACM HotNets 2014

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Modelling In-Network Caching

  • I. Psaras, R. G. Clegg, R. Landa, W. K. Chai, G. Pavlou, "Modelling and Evalua/on
  • f CCN-Caching Trees", Proceedings of the 10th IFIP Networking, Valencia,

Spain, 9-13 May 2011

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Centrality-Based In-Network Caching

  • W. K. Chai, D. He, I. Psaras, G. Pavlou, "Cache "Less for More" in Informa/on-centric

Networks", Proceedings of the 11th IFIP Networking, Prague, Czech Republic, 21-25 May 2012

  • W. K. Chai, D. He, I. Psaras, G. Pavlou, "Cache "Less for More" in Informa/on-centric

Networks", Elsevier Computer Communica9ons Special Issue on ICN 2013 Best Paper Award One of top cited COMCOM papers since 2013!!

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Probabilistic In-Network Caching

  • I. Psaras, W. K. Chai, G. Pavlou, "Probabilis/c In-Network Caching for

Informa/on-Centric Networks", Proc. of the 2nd ACM SIGCOMM Workshop on ICN 2012, Helsinki, Finland, August 2012

  • I. Psaras, W. K. Chai, G. Pavlou, ”In-Network Cache Management and Resource

Alloca/on for Informa/on-Centric Networks", IEEE TPDS

ProbCache: Probabilistic In-Network Caching

Caching Capability of a Path Weight-based Caching

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Cache-aware-/Hash-routing for ICN

  • L. Saino, I. Psaras, G. Pavlou, ”Hash-rou/ng schemes for Informa/on-Centric

Networks", Proc. of the 3rd ACM SIGCOMM Workshop on ICN 2013, Hong Kong, August 2013

  • L. Saino, I. Psaras, G. Pavlou, ”Icarus: a Caching Simulator for Informa/on-

Centric Networking", Proc. of the 7th ICST SIMUTOOLS, Lisbon, Portugal, March 2014

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Further Paper Highlights

  • Kaito Ohsugi, Junji Takemasa, Yuki Koizumi, Toru Hasegawa, Ioannis Psaras, “Power

Consumption Model of NDN-based Multicore Software Router based on Detailed Protocol Analysis”, IEEE JSAC, Series on Green Communications and Networking, 2016.

  • Ioannis Psaras, Wei Koong Chai, George Pavlou, “In-Network Cache Management

and Resource Allocation for Information-Centric Networks”, IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), vol. 25, issue 11, pp. 2920-2931, 2014.

  • L. Saino, I. Psaras, G. Pavlou, “Icarus: a Caching Simulator for Information-Centric

Networking”, Proc. of the 7th ICST SIMUTOOLS 2014, Lisbon, Portugal, March 2014

  • Lorenzo Saino, Ioannis Psaras, George Pavlou, “Understanding Sharded Caching

Systems”, IEEE INFOCOM 2016, to appear.

  • Ioannis Psaras, Lorenzo Saino, George Pavlou, “Revisiting Resource Pooling: The

Case for In-Network Resource Sharing”, in Proc. of ACM HotNets 2014, Los Angeles, California, Oct 2014.