Information Resilience through User-Assisted Caching in Disruptive - - PowerPoint PPT Presentation

Information Resilience through User-Assisted Caching in Disruptive Content-Centric Networks

Vasilis Sourlas, Leandros Tassiulas, Ioannis Psaras, George Pavlou IFIP Networking 2015 Ioannis Psaras

EPSRC Fellow University College London i.psaras@ucl.ac.uk

Best Paper Award

Problem Attacked

When the network gets fragmented, and given we have a number of (in-network) caches, for how long can we keep the content “alive” in caches and end-user devices?

– How do we find “alive” content (i.e., content still in caches)?

Goals

• Find ways to:

– Exploit all possible sources to retrieve content when the main path is “down” – Exploit in-network caching to prolong information lifetime in case of disasters – Natively support P2P-like content distribution at the network layer

Starting Points

• Information-Centric Networking

– Very promising future networking environment

• Information retrieval is more important than location

– Explicitly named content chunks/packets. – Request-response at the chunk/packet level. – Flexible to adaptation through its native support to caching, mobility and multicast.

• In-network opportunistic caching

– Salient characteristic of ICN. – Packets are opportunistically cached in passing by nodes. – Plenty of research on the optimization in-network caching system performance.

• Disaster scenarios (earthquake, tsunami, etc.)

– Usage of ICN functional parts, even when these are disconnected from the rest of the network (IETF ICNRG working group). – Difficult in today’s networks that mandate connectivity to central entities for communication.

ICN World

A B C E F D some/weird/name some/weird/name ICN Routing Engine some/weird/name

ICN: Application-layer name à Network-layer name (the network routes to the content itself by name)

Information Resilience through SIT

A B C E F D R: some/weird/name Server for content: some/weird/name Route based on FIB C: some/weird/name R: some/weird/name

✗ ✗

Route based on SIT C: some/weird/name Some sh!t happened!!

Key Design challenges & Contributions

• How to augment the original NDN content router to

increase information resilience under fragmentation?

– How to forward Interests when network fragmented?

• What changes are required to the main ICN

packets format and their processing in order to enable P2P-like content distribution?

• Can we measure information resilience?

– We build Markov processes for the hit probability and the time to absorption of an item and find lower bounds

Router Design

• Content Store (CS)
• Pending Interest Table (PIT)
• Forwarding Information Base (FIB)

Same to NDN original model

Satisfied Interest Table (SIT)

– Keeps track of data packet next hop. – “Breadcrumbs” for user-assisted caching. – Allows a list of outgoing faces. – Similar to Persistent Interests (PI) in C. Tsilopoulos and G. Xylomenos, “Supporting Diverse Traffic Types in ICN” ACM SIGCOMM ICN 2011.

Index

CS

Ptr Type

PIT FIB SIT /a/b . . . . .

Content Store (CS)

Name Data

/c/d 3,1 . . /a/b 1

Satisfied Interest Table (SIT)

Name Face List

/c 0,1 . . /a 2

Forwarding Info Base (FIB)

Prefix Face List

/c/d 2 . . /a/b 0,3

Name

• Req. Faces

Face 0 Face 1 Face 2 Face 3 Pending Interest Table (PIT)

Packet Processing

• Interest Packet format

– Destination flag (DF) bit to distinguish whether the Interest is headed towards content origin (DF=0), or towards neighbouring users (DF=1).

• Interest Packet processing

– Normal operation (i.e., no fragmentation): Same as in NDN – Fragmentation Detected: If the Interest cannot find a match in CS, PIT and FIB then DF is set to 1 and follows entries in SIT. – An Interest with DF=1 can be replied both by routers and by users with matching cached content.

• Data packet processing

– Exactly the same as in NDN; follow the chain of PIT entries. – A passing by Data packet installs SIT entries. – Optionally cached in CS of each passing by router (under investigation).

Performance Bounds

System model

Absorbing State Probability

Mean Time to Absorption

• Result: When the death rate of the users interested in a content item is

larger than the corresponding birth rate, the item will finally get absorbed when the content origin is not reachable.

– The formula above gives us the “time to absorption”

[1] H. M. Taylor and S. Karlin, “An Introduction to Stochastic Modeling, 3rd edition”, Academic Press, 1998.

Performance Evaluation

Strategies/Policies (after the network fragmentation)

• Interest forwarding policies

– SIT based forwarding policy (STB) – Flooding forwarding policy (FLD)

• Caching policies

– No caching policy (NCP) – Edge caching policy (EDG) – En-route caching policy (NRT/LCE)

• Placement/Replacement policies

– Least Recently Used policy (LRU)

Evaluation setup

• Tool: Icarus
• Network topology: 50 nodes - Internet topology Zoo
• Traffic demand: 1req/sec at each node
• Request distribution: Zipf and localised, i.e., different across

different regions

• Connection rate: 1 new user per sec
• “Initialization period” of 1 hour. “Observation period” of 3 hours.

Network fragmentation and origin servers of all items are not reachable.

Metrics

• Satisfaction (% of issued interests).
• Absorbed Items (% of content items).
• Mean Absorption Time (sec).
• User Responses (% of satisfied interests)
• Minimum Hop Distance (hops)
• Traffic overhead (hops)

Experiments

• Model validation
• Impact of cache size
• Impact of users’ disconnection rate.

Model Validation

Perfect match between model and simulation!

200 400 600 800 1000 10 20 30 40 50 60 70 10000 20000 30000

Theoretical Experimental

V=50, =1, =0.1, =0

Absorption Time (sec) Information Item

Impact of the cache size

Popular messages can stay in the network for hours even with modest amounts of cache.

5 10 15 20 25 30 35 40 10 20 30 40 50 60 70 80 90 100 110

V=50, =1, =0.1

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Satisfaction (% of issued interests) C/M (%) 5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40

V=50, =1, =0.1

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Traffic Overhead (hops) C/M (%) 5 10 15 20 25 30 35 40 1,0 1,5 2,0 2,5 3,0 3,5 4,0

V=50, =1, =0.1

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Minimum Hop Distance C/M (%) 5 10 15 20 25 30 35 40 1000 2000 3000 4000 5000 6000 7000

V=50, =1, =0.1

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Mean Absorption Time (sec) C/M (%) 5 10 15 20 25 30 35 40 10 20 30 40 50 60 70 80 90 100

V=50, =1, =0.1

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Absorbed Items (% of items) C/M (%) 5 10 15 20 25 30 35 40 10 20 30 40 50 96 98 100

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

V=50, =1, =0.1

User Resps. (% of responded interests) C/M (%)

Impact of users’ disconnection rate

• When disconnection rate is larger than 0.2, less than 5% of the

satisfied interests are served from users.

• The STB enabled mechanisms discard less popular items fast and

maintain the rest items for a longer period.

0,0 0,2 0,4 0,6 0,8 1,0 1,5 2,0 18 24 30 36 80 85

STB-NCP FLD-NCP STB-EDG-LRU FLD-EDG-LRU STB-NRT-LRU FLD-NRT-LRU

V=50, =1, C/M=5%

Satisfaction (% of issued interests)

• 0,0

0,2 0,4 0,6 0,8 1,0 1,5 2,0 3 6 9 12 15 18 21 24 27

V=50, =1, C/M=5%

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Traffic Overhead (hops)

• 0,0

0,2 0,4 0,6 0,8 1,0 1,5 2,0 1,0 1,5 2,0 2,5 3,0 3,5 4,0

V=50, =1, C/M=5%

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Minimum Hop Distance

• 0,0

0,2 0,4 0,6 0,8 1,0 1,5 2,0 10 20 30 400 800 1200 1600 2000

V=50, =1, C/M=5%

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Mean Absorption Time (sec)

• 0,0

0,2 0,4 0,6 0,8 1,0 1,5 2,0 20 30 40 50 60 70 80 90 100

V=50, =1, C/M=5%

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

Absorbed Items (% of items)

• 0,0

0,2 0,4 0,6 0,8 1,0 1,5 2,0 10 20 30 40 50

STB-NCP STB-EDG-LRU STB-NRT-LRU FLD-NCP FLD-EDG-LRU FLD-NRT-LRU

V=50, =1, C/M=5%

User Resps. (% of responded interests)

q It is very easy to make the network resilient to fragmentation (at least in case of disasters). q The Satisfied Interest Table (SIT) is not memory-intensive – acts like a cache. q Some (popular) content can stay in the network for hours. q Scoped flooding can improve performance significantly (results on the way). q P2P can be supported natively in an ICN world and is very very helpful in case of disasters/fragmentation q We’re working to incorporate the Satisfied Interest Table (SIT) in the NDN normal operation.

Conclusions

Some 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.

Thanks! Questions?

Dr Ioannis Psaras i.psaras@ucl.ac.uk http://www.ee.ucl.ac.uk/~uceeips/