G. Carofiglio (Bell Labs) joint work with M. Gallo (Orange labs), L.Muscariello (Orange labs), D.Perino (Bell labs). Modeling data transfer in Content-Centric Networks. Towards the definition of transport control mechanisms for CCN.
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joint work with M. Gallo (Orange labs), L.Muscariello (Orange labs), D.Perino (Bell labs)Modeling data transfer in Content-Centric Networks
Towards the definition of transport control mechanisms for CCN
Named-Data Networking (NDN) NFS project 2010-on
Content-based Networking by Carzaniga et al. Univ. of Colorado (2001-2004)
Data Oriented Network Architecture (DONA) by Shenker et al. UC Berkley, 2007
Publish/Subscribe Internet Routing Paradigm 2009-on (PSIRP-PURSUIT EU project)
Network of information (NetInf) 4WARD-SAIL EU projects 2008-on
Content-Oriented Networking: a New Experience for Content Transfer (CONNECT), French project (2010-on)
Content-centric networking proposals
Content items are identified by a unique name and are split into self-identified chunks (packets)
CCN names are hierarchically structured to allow aggregation of routing and forwarding table entries.
CCN packets are of two types:
Routing can be done in a similar fashion to today’s IP routing:
Component-wise longest prefix match forwarding based on FIB information
Lookup Content Store
Lookup+Update(Add/Change entry) Pending Interest Table (PIT)
Lookup Forwarding Information Base (FIB)
Lookup+Update(Remove entry) Pending Interest Table (PIT)
Store in Content Store
Additional operations may be required to verify packets
Receiver controls Interest rate for a content retrieval based on Data rate (window-based controller), performs loss recovery and congestion control.
Interest may be forwarded along different routes according to different forwarding strategies.
Chunks of the same content may be retrieved from different locations.
coupled with in-path caching
The sender endpoint does not exist.
On a path between user and a permanent copy of the content every intermediate node can send the data in response to an Interest.
Performance evaluation of the CCN transport paradigm
analytically characterize average throughput/delivery time
account for the interplay between receiver-driven transport layer and in-network caching
Explicit data transfer rate formula
To develop an analytical tool for the design of a CCN window-based flow control
network dimensioning tool
M different content items organized in K popularity classes,
m=M/K in each class
Content popularity distribution: Zipf
Average content size [chunks]
Content store size x [chunks]
Shortest path routing
(a) linear topology
(b) binary tree
No bandwidth limitations
Miss probability for class k at node i
Round trip delay at node i
VRTT represents the average time that elapses between the expression of an interest and data delivery
Similarly to RTT for TCP, VRTT is the average distance in time between the user and the requested data
Two level request process modeled through a Markov Modulated Rate Process (MMRP)
VRTT is assumed to have converged to a stationary value.
single LRU cache dynamics (King 71, Flajolet 92)
miss probability exact formulae (computational expensive)
combinatorial approaches (Coffman 99, Starobinski-Tse 01)
probabilistic models: Jelenkovic (92-08)
asymptotics of miss probability for large caches
closed-form characterization under Poisson content request arrival process
approximation of miss process
Kurose-Towsley 09 extends single cache model in Dan-Towsley 90 to the network case
All previous models are at content level, most ignore request correlation.
Stationary miss probability
The output process is a renewal process that we approximate with a
MMRP (as in Jelenkovic 08) with intensity μk at content level.
We developed an ad hoc C++ event-driven simulator at chunk level
Parameters: M= 20000, K=400, m=50, =2, chunk size= 10KB, =690 chunks (6.9MB), x=100k÷400k chunks (1GB÷4GB), =40 content/s, W=1, no filtering.
We assume a timeout of Δ sec. for PIT entries.
In practice, requests are ‘filtered’ on a time interval equal to min(Δ, RVRTTk(i)), where RVRTT denotes the residual VRTT at node i for class k.
Note that if a chunk request is filtered, every following request for chunks of the same content is equally filtered
The filtering probability at the first cache is
Under the assumption of a MMRP output process at the first cache and w/o filtering, the miss probability at node i is
where xi is the cache size at node i.
In presence of request filtering,
For a binary tree topology and in absence of request filtering,
where pk(i)=f(qk(i)), and
where pfilt,k(1) accounts for the probability to aggregate requests for the same content in the PIT.
Parameters: M= 20000, K=400, m=50, =0.7, chunk size= 10KB, =690 chunks (6.9MB), x=200000 (2GB), =40 content/s, W=1, no filtering.
RTT=2ms at each hop.
Request rate reduction up to 20% at 3rd node with filtering.
The miss probabilities contribute to the definition of the VRTT for contents in class k. The average throughput in steady state is
Letting W vary over time we can define a control over the interest rate such to achieve a target average throughput
Average delivery time: Tk= /Xk
The throughput formula is a powerful tool for cache sizing under throughput or delivery time guarantees.
The filtering does not have a large impact of VRTT/throughput values, but it helps reduce overall request traffic.
Throughput gain due to parallel downloading is more important for most popular classes due to a smaller VRTT
The model can be used as a tool to
quantify network cost to guarantee a certain performance or
predict end user performance under a given per-service resource allocation.
Traffic/Storage capacity trade-off
Performance/Network cost trade-off
Parameters: Binary tree, same parameters as before except =2.3 (YouTube, Daily Motion)
every route is characterized by a number of parallel transfers
document requests arrive according to a Poisson process of rate
transfers in the same route perfectly share available bandwidth
transfers in different routes share bandwidth in the max-min sense
a transfer gets a rate from route when
there exists a bottleneck for route
that determines the fair rate
A given data transfer makes use of multiples sub-routes according to the miss probabilities
the delay to retrieve data from cache i is
According to the VRTT formula we obtain
The average data delivery time
the traffic load on link i
(C1 ,C2, C3 )=(10,20,30)Mbps
(C1 ,C2, C3 )=(30,10,20)Mbps
Content items: M=20k different files, K=2000 classes, packets of size 10kB, packets (90% of the most popular files in the catalog)
Definition of a CCN transport control mechanism
We design a receiver-driven Interest Control Protocol for CCN (ICP), whose definition still lacks in literature.
Efficient and Fair use of network resources
We prove that ICP realizes the fair and fully efficient resource sharing described in our previous work
we provide an analytical characterization of average rate, expected data transfer delay and queue dynamics in steady state on a single and multi-bottleneck network topology.
= 10ms, C=100 Mbps,R~0
The steady state solution is a periodical limit cycle of duration
with time averages
down-link capacity of link i
System dynamics are described by:
σ = 5000 chunks, α = 1.7, x(i) = 50.000 chunks, M = 20.000 files, K = 5 classes
= 4ms (C1 ,C2)=(100,50)Mbps
σ = 5000 chunks, α = 1.7, x(i) = 50.000 chunks, M = 20.000 files, K = 5 classes
fixed , (C1 ,C2, C3 )=(100,40,100)Mbps
A fixed timer value results in an inefficient regime
σ = 5000 packets, α = 1.7, x(i) = 50.000 packets, M = 20.000 files, K = 4.000 classes, R ≈ 0, l=1 Content/s , infinite buffer size
(C1 ,C2, C3 )=(50,100,100)Mbps
(C1 ,C2, C3 )=(100,40,100)Mbps
«Bandwidth and Storage Sharing in Information-Centric Networks », G.Carofiglio, M.Gallo, L.Muscariello, in Proc. of ACM ICN Sigcomm 2011.
«Experimental evaluation of storage management in Content-Centric Networking », G. Carofiglio, V. Gehlen, D. Perino, in Proc. of IEEE ICC 2011.
«ICP: Interest Control Protocol in Content-Centric Networking », G. Carofiglio, M.Gallo, L.Muscariello, in Proc. of IEEE Infocom Nomen 2012.
«Evaluating per-application storage management in content-centric networks»,G.Carofiglio, M.Gallo, L.Muscariello, D.Perino, under submission.
design of optimal (min delay, min network cost) interest forwarding with no or little state per FIB entry per node