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Cache Management for TelcoCDNs
Daphné Tuncer Department of Electronic & Electrical Engineering
University College London (UK) [email protected]
22/12/2017
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Agenda
1. Internet traffic: trends and evolution 2. Content delivery models 3. Stakeholders: cooperation and challenges 4. ISP caches 5. Cache management strategies
2 D. Tuncer
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Internet traffic forecast (1/2)
• Based on Cisco VNI 2017 [1]
Consumer Internet video traffic to represent 82 percent of all consumer Internet traffic in 2021 (73 percent in 2016).
Internet video to TV doubled in 2016 and to 3.6-fold by 2021.
Consumer VoD traffic to double by 2021 (equivalent to 7.2 billion DVDs per month).
Live Internet video to account for 13 percent of Internet video traffic by 2021.
3 D. Tuncer
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Internet traffic forecast (2/2)
Emergence and rapid growth of advanced video services:
o Internet video surveillance (+76% in 2016)
o Virtual reality traffic (82% mean annual growth from 2016 to 2021)
Traffic from wireless and mobile devices will exceed traffic from wired devices by 2019 (49% in 2016 and 63% in 2021).
4 D. Tuncer
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Internet traffic in volume
• Traffic volume in petabytes (per month)
2016 2021 Compound annual
growth rate
Video 42 029 159 161 +31%
Web, email, data 9 059 19 538 +17%
File sharing 6 628 6 595 0%
Online gaming 915 10 147 +62%
Source: Cisco VNI 2017 [1]
D. Tuncer 5
Note: 1PB = 10^15 bytes
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Bandwidth requirements
Source: Cisco VNI: The Zettabyte Era - Trends and Analysis, July 2016 [2]
• Busy-hour compared with average Internet traffic growth
D. Tuncer 6
Busy-hour
Average
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Content delivery network
• Content distribution mainly relies on Content Delivery Networks (CDNs)
A CDN can be defined as “a large, geographically distributed network of specialized servers that accelerate the delivery of web content and rich media to internet-connected devices”, Akamai [3].
• Example of Akamai
More than 175,000 servers in more than 100 countries
• Content delivery network traffic will deliver more than three- fourths of all Internet video traffic by 2021 [1].
D. Tuncer 7
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Content distribution solutions
• Commercial CDNs ex: Akamai Technologies, Limelight Networks, Fastly, etc.
• ISP-operated CDNs ex: AT&T Inc., Level 3 Communications, Deutsche Telekom,
NTT, Telefonica, etc.
• Content provider-operated CDNs
ex: Netflix
• Peer-to-peer CDNs
ex: Coral Content Distribution Network
D. Tuncer 8
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Stakeholders
D. Tuncer 9
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Stakeholders
D. Tuncer 10
Content Provider
Content Producer
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Stakeholders
D. Tuncer 11
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 12
end user
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 13
end user I want to watch X
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 14
end user I want to watch X
Internet Service Provider
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 15
end user I want to watch X
Internet Service Provider Access the
Internet
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 16
end user I want to watch X
Content Delivery Network
Internet Service Provider Access the
Internet
Content Provider
Content Producer
Here is new content
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Stakeholders
D. Tuncer 17
end user I want to watch X
Content Delivery Network
Internet Service Provider Access the
Internet
Content Provider
Content Producer
Here is new content
Distribute the content
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Stakeholders
D. Tuncer 18
end user I want to watch X
Content Delivery Network
Request from your client
Internet Service Provider Access the
Internet
Content Provider
Content Producer
Here is new content
Distribute the content
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CDN management operations
• Content placement
Decide on the distribution of content items in the different server locations.
• Server selection
Decide how to serve client requests.
• Usually taken without or with only limited knowledge
of the underlying network conditions
Exert enormous strain of ISP networks
D. Tuncer 19
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Impact for the ISP
• External costs
Internet tie costs
Decreasing trend but still significant given volume of traffic carried by CDNs
• Internal costs
Internal network upgrades Upgrading a single router can amount in the order of tens
of thousand dollars
D. Tuncer 20
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Quality of Experience degradation
• Degradation of the Quality of Experience (QoE)
• Congestion and network failure lead to video playback issues (slow start, pixilation etc.) and buffering
• Severe effects on user experience
• The end user is more likely to contact his/her ISP than Netflix!
D. Tuncer 21
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User (in)tolerance and QoE expectation
• Effect of poor resolution and/or frequent interruption on user
Tolerance (in min) Percentage of abandonment
0 min 33%
1-4 min 43%
5-10 min 14%
11-30 min 5%
30+ min 3%
Source: Conviva 2015 [5]
D. Tuncer 22
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ISP network caches
• Two solutions [4]
Partner caching
Transparent caching
D. Tuncer 23
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Partner caches
• The Content Provider (CP) installs caches in the ISP’s network.
• Caches are owned and maintained by the CP.
• Reduction of traffic on interconnect links.
• Internal traffic reduction strongly depends on the number of partner caches.
• Example: Netflix via OpenConnect
D. Tuncer 24
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Transparent caches
• The ISP deploys its own caches used to locally cache most popular content items.
• Caching decision based on content popularity.
• Control messages between the client and the CP
Video statistics, ad views etc.
Essential for the CP’s business
• Example: Mediacom using Qwilt
• Legal implications associated with caching third party content.
D. Tuncer 25
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Partner caches vs. transparent caches (1/2)
D. Tuncer 26
Partner caches Transparent caches
Equipment cost Free for the ISP
Investment needed by the ISP
Content coverage
• Can only cache content of specific CP
• Good option only if one CP dominates
• Transparent to the
CPs • Best option if many
CPs of equal importance
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Partner caches vs. transparent caches (2/2)
D. Tuncer 27
Partner caches Transparent caches
Source of revenue
No additional source of
revenue for the ISP
New models involving
the ISP
External and internal costs
Address external cost only (transit cost)
Address both external and internal upgrade
costs but added complexity for the ISP
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Other solutions
• Collaborative models such as CDNI (Content Delivery Networks Interconnection)
• Cloud-based services
• Towards ISP-operated CDNs?
D. Tuncer 28
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New technological opportunities
• Decreasing cost of storage modules
Enable network devices (i.e. access point, set-top boxes etc.) to be equipped with storage modules
• Programming interfaces to network devices
• Virtualisation Not only compute and storage resources but also network
resources Offer flexibility in managing the resources
D. Tuncer 29
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Cache management strategies
D. Tuncer 30
2
3
1
6
4
5
7 8
10 9
x1
x2
Cache
CDN
ISP_2 Inter-domain
link
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Cache management strategies
D. Tuncer 31
2
3
1
6
4
5
Request for x1 7 8
10 9
x1
x2
Cache
CDN
ISP_2 Inter-domain
link
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Cache management strategies
D. Tuncer 32
2
3
1
6
4
5
Request for x1 7 8
10 9
x1
x2
Cache
Request for x1
served
locally
CDN
ISP_2 Inter-domain
link
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Cache management strategies
D. Tuncer 33
2
3
1
6
4
5
7 8
10 9
x1
x2
Request for x1
Cache
CDN
ISP_2 Inter-domain
link
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Cache management strategies
D. Tuncer 34
2
3
1
6
4
5
7 8
10 9
x1
x2
Request for x1
Cache
CDN
ISP_2 Inter-domain
link
Cache miss
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Cache management strategies
D. Tuncer 35
2
3
1
6
4
5
7 8
10 9
x1
x2
Request for x1
Cache
Request for x1
redirected
to node 4
CDN
ISP_2 Inter-domain
link
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Cache management strategies
D. Tuncer 36
2
3
1
6
4
5
7 8
10 9
x1
x2
Cache
CDN
ISP_2 Inter-domain
link
Request
for x3
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Cache management strategies
D. Tuncer 37
2
3
1
6
4
5
7 8
10 9
x1
x2
Cache
CDN
ISP_2 Inter-domain
link
Request
for x3
Request for x3
redirected
to origin server
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Management operations
D. Tuncer 38
• Content placement
• Server selection
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Content placement
• How to distribute the content items in the different cache locations?
Constrained by the available caching capacity Traffic cost equal zero if infinite capacity (unrealistic!!)
• Optimisation/Performance objective(s)
Reduce user perceived delay Optimise use of internal resources Reduce transit cost etc.
• Reactive vs. proactive strategies
D. Tuncer 39
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Reactive content placement (1/2)
• Each cache autonomously decides on the content items to (re)place.
• Two components:
Placement strategy
Replacement policy (ex: LFU, LRU)
• Dynamic system
Apply insertion and eviction decisions based on the content popularity evolution at each location
• Approach used by Facebook on its edge servers
D. Tuncer 40
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Reactive content placement (2/2)
• Advantages
Very low complexity Uncoordinated and local decisions Relatively good cache hit ratio (i.e. number of requests
server locally)
• Drawbacks
Can have an impact on network cost (i.e. link utilisation) Cannot avoid few cache misses when a content becomes
suddenly popular
D. Tuncer 41
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Proactive content placement (1/2)
• The operator periodically decides on the location of the content items in the available caching location.
• The placement decisions are taken based on the prediction of content popularity for the next configuration period.
• New configurations are applied at medium to long timescale (in the order of few hours)
Generally once a day at night time during period of low resource utilisation
• Solution used by Netflix
D. Tuncer 42
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Proactive content placement (2/2)
• Advantages
Fewer cache misses by provisioning the caches in anticipation to surge in popularity
The network cost can be taken as an optimisation parameter in the placement algorithm
• Drawbacks
The performance depends on the accuracy of prediction strategy
Higher management complexity Migration overhead when provisioning the caches
D. Tuncer 43
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Content popularity
• The popularity is defined both temporally and spatially
Number of requests per content item (long tail distributed)
Content items requested at each location
D. Tuncer 44
Pro
bab
ility
Rank
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Content popularity evolution
D. Tuncer 45
• The evolution of the popularity of an item over time strongly depends on the content type.
Source: A. Sharma et al. "Distributing Content Simplifies ISP Traffic Engineering, " SIGMETRICS’13 [6].
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Example of series
D. Tuncer 46
• To which extent do series viewers stick to a series?
• Behaviour of the viewers of series 1 (S1) when series 2 (S2) is released
Viewer behaviour Percentage
Watch S1 and 2 together 59%
Put S1 on hold 25%
S2 replaces S1 if S2 is great 11%
Abandon S1 4%
Source: Conviva 2015 [5]
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Predicting content popularity
D. Tuncer 47
• Example on a real VoD trace
• Limit of any prediction strategies
Some contents are inherently unpredictable
Source: M. Claeys et al. "Hybrid Multi-tenant Cache Management for Virtualized ISP Networks," JNCA 2016 [7]
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Proactive approaches (1/2)
• Problem formulation
Given a set of M caches and a set of X contents, determine the number of copies of each content item to store in the network the location of each copy
in order to optimise some objective.
• Family of facility location problems
D. Tuncer 48
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Proactive approaches (2/2)
• Different options to solve the problem
Integer Linear Programming (ILP)-based approaches
+ Optimal solution for the input parameters
- Does not scale well
Heuristics (e.g. greedy approaches)
+ Computationally more efficient than ILP approaches
- Sub-optimal solutions
• CDNs usually apply proprietary algorithms (e.g. Akamai, Netflix)
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Server selection (1/2)
• To decide on the best server location to serve client requests
For scalability decisions are taken at the group of clients level.
• Different redirection mechanisms can be implemented
DNS-based
HTTP-based
Use of smart intermediaries
• DNS-based mechanisms remain the preferred method of industry leader, e.g. Akamai.
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Server selection (2/2)
• Server selected based on different factors
Performance indicators, e.g. latency, packet loss, server load etc.
Business and regulatory restrictions
• Large scale monitoring systems required to build up-to-date map of the conditions.
• Decisions recomputed at the minute level.
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Performance metrics (1/2)
At the resource level
• Network metrics
Network load Link utilisation Retrieval latency
• Cache metrics
Cache hit ratio Cache occupancy ratio Content replication degree
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Performance metrics (2/2)
• Management costs
Signalling and monitoring overhead Migration overhead Algorithm complexity
• User metrics reflecting the QoE
Buffering ratio, start-up latency, average bitrate, frequency and duration of interruptions during playback etc.
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Management system
• How to implement cache management applications?
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Management system model
D. Tuncer 55
Management System
Network resources
Network monitoring
Decision enforcement
Reconfiguration applications
(i.e. content placement)
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Centralised vs. distributed management (1/2)
D. Tuncer 56
Central manager
Mgr1
Mgr2
Mgr3
Centralised system Distributed system
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Centralised vs. distributed management (2/2)
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Advantages Limitations
Centralised management
Easy to implement Optimal solution
Single point of failure Does not scale well Not appropriate for
dynamic system
Distributed management
Scale well Suitable for dynamic
system
Higher implementation complexity
Coordination
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Concluding remarks
• Scientific challenges
Development of advanced prediction strategies
Sensitivity of reconfiguration algorithms to content type
• Technological challenges
Monitoring support for real time services
Reducing access latency to memory
• Business challenges
Rethink existing models of collaboration between the different stakeholders.
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References
[1] Cisco Visual Networking Index: Forecast and Methodology, 2016-2021, June 2017, White Paper
[2] Cisco Visual Networking Index: The Zettabyte Era -Trends and Analysis, July 2016, White Paper
[3] Akamai Technologies, https://www.akamai.com/us/en/resources/content-distribution-network.jsp
[4] Colin Dixon, " Handling the explosion of online video: why caching is the key to containing costs, " October 2013, nScreenMedia
[5] Conviva.com, Binge Watching, The New Currency of Video Economics, 2015
[6] A. Sharma et al., "Distributing Content Simplifies ISP Traffic Engineering, " in proc. ACM SIGMETRICS ’13, 2013, pp. 229–242.
[7] M. Claeys et al., "Hybrid Multi-tenant Cache Management for Virtualized ISP Networks," Journal of Network and Computer Applications (JNCA), Volume 68, pp. 28-41, June 2016.
D. Tuncer 59