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CloudNets: Combining Clouds with Virtual Networking
Stefan Schmid December, 2012
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CloudNets: Combining Clouds with Virtual Networking
Stefan Schmid December, 2012
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3 Telekom Innovation Laboratories Stefan Schmid
Vision: Virtual Networking Cloud Resources.
Cloud computing is a big success!
But what is the point of clouds if they cannot be accessed?
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4 Telekom Innovation Laboratories Stefan Schmid
Next Natural Step for Virtualization!
Success of Node Virtualization a.k.a. end-host virtualization VMWare revamped server business VM = basic unit in datacenters… … hardly any physical resources! VM = flexible allocation, migration.. «Elastic computing»
Trend of Link Virtualization
MPLS, VPN networks, VLANs Software Defined Networks
(SDN), OpenFlow, ... «The VMWare of the net» «Elastic networking»
Unified, fully virtualized networks: CloudNets „Combine networking with heterougeneous cloud resources (e.g., storage, CPU, ...)!“
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5 Telekom Innovation Laboratories Stefan Schmid
For Tomorrow’s Internet…
Today: Internet is one solution for everything!
CloudNets: Flexibly specifiable virtual networks, executing different protocol stacks, cohabiting the same substrate.
Innovation!
Innovation!
Innovation?
Feldmann et al.:
ERCIM News 2012
Vision: facilitate innovation in network core
Make network core programmable (e.g., own intrusion detection system) Service-tailored networks (for social networks, bulk data transfer, life streaming, etc.) Co-existing virtual networks with QoS guarantees No dependencies on IPv4, BGP, ...
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6 Telekom Innovation Laboratories Stefan Schmid
Requests with Flexible Specification. Optimization and Migration?
Datacenters „VPN++“
Migration / Service Deployment Spillover/Out-Sourcing Goal: Move with the sun, with the commuters, (QoS) allow for maintenance, avoid roaming costs…: e.g., SAP/game/translator server, small CDN server…
Goal: Fully specified CloudNet mapping constraints (e.g., end-points for a telco), but with QoS guarantees (e.g., bandwidth) along links
Berlin
Tel Aviv Palo Alto
1Mbit/s 1Mbit/s
1Mbit/s
„November 22,
1pm-2pm!“
Berlin
„any European cloud provider (e.g. due to legal issues?)“
Berlin (corporate access network)
< 50ms
< 50ms
„50 TB storage, 10 Tflops computation!“
any
any any < 10ms
> 100 MB/s
„Guaranteed resources, job deadlines met, no overhead!“
“Network may delay execution: costly for per hour priced VM!”
< 10ms
> 100 MB/s < 10ms > 100 MB/s
Elastic computing
See, e.g., Octopus system (SIGCOMM 2011)
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7 Telekom Innovation Laboratories Stefan Schmid
Opens New Business Roles.
Roles
Service Provider (SP): uses CloudNets to offer its services (e.g., value-added application CloudNet, or transport CloudNet)
Virtual Network Operator (VNO): Installs and operates CloudNet over topology provided by VNP, offers tailored connectivity service, triggers cross-provider migration (by setting requirements), ...
Virtual Network Provider (VNP): „Broker“/reseller that assembles virtual resources from different PIPs to provide virtual topology (no need to know PIP, can be recursive, ...)
Physical Infrastructure Provider (PIP): Owns and manages physical infrastructure
Focus of our work architecture (unlike, e.g., single authority in GINI testbed), on multitude of players, providers, ...! (Of course, cross-layer infos if all same company...)
Roles
QoS from PIP up to VNO or service provider: accounting via complete set of contracts! (unlike „sending party pays“)
Schaffrath et al.:
SIGCOMM VISA 2009
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8 Telekom Innovation Laboratories Stefan Schmid
Use Cases (1): Migrate Resources. Resource allocation and migration where needed (energy saving otherwise!).
CDN in CloudNet or attached to VNet?
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9 Telekom Innovation Laboratories Stefan Schmid
Use Cases (2).
Physical infrastructure
(e.g., accessed by mobile clients)
Specification:
1. close to mobile clients
2. >100 kbit/s bandwidth for synchronization
Provider 1
Provider 2
CloudNet requests
CloudNet 2: Mobile service w/ QoS CloudNet 1: Computation
Specification:
1. > 1 GFLOPS per node
2. Monday 3pm-5pm
3. multi provider ok
Connecting Providers (Geographic Footprint).
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10 Telekom Innovation Laboratories Stefan Schmid
Architecture / Anatomy of Prototype.
Plugin based
VLink technology (VLAN, OpenFlow, MPLS, ...)
Embedding algorithm (two stage!)
Cloud operating system
Prototype: proof-of-concept (flexibility&generality rather than speed)
VLink plugin: VLANs (VPN tunnel for WAN)
Embedding: MIP
Cloud OS: own implementation
Plan: other plugins, OpenStack, ...
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11 Telekom Innovation Laboratories Stefan Schmid
Two sites: TU Berlin and NTT Docomo Eurolabs Munich
Testbed.
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12 Telekom Innovation Laboratories Stefan Schmid
The Prototype (1).
State of the Art
Prototype based on KVM+Xen (nodes) and VLANs (links)
Layer 2 comform: no need for end-to-end / routing (routing inside CloudNet only)
Different VNets may have same
internal virtual node addresses VLAN ensures isolation
PIP and VNP implemented
Under Submission
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13 Telekom Innovation Laboratories Stefan Schmid
The Prototype (2).
VLAN1
VLAN2
VLAN2
VLAN1 VLAN2
Migration
IP 1
IP 2
IP 3
IP 1
IP 2
IP 3
Open vSwitch supports VLAN bridging
Demultiplexing eth1 plus VLAN-tag to port in kernel
To VM looks like Ethernet (no VLAN)
Each virtual link is a VLAN (broadcast domain)
Migration: reconfigure VLANs, not addresses of virtual nodes!
Transparent for users...
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14 Telekom Innovation Laboratories Stefan Schmid
The Prototype (3).
Life of a CloudNet request:
Topology broken down for PIPs
Transit link (tunnel bridge and OpenVPN)
To VM looks like Ethernet (no VLAN)
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15 Telekom Innovation Laboratories Stefan Schmid
Need for a Language. Communicate CloudNets, substrate resources and embeddings to business partners or customers:
Store embedding state internally:
ICCCN 2012
SP PIP
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16 Telekom Innovation Laboratories Stefan Schmid
Exploiting Flexibilities: Resource Description Language.
Network Elements = nodes or links! Connected via Network Interfaces
Support for omission
Support for multicast links
Support for white and black lists...
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17 Telekom Innovation Laboratories Stefan Schmid
FleRD Requirements.
Support all kinds of node (storage, computation, ...) and link
(latency, bw, full-duplex/asymmetric, ...) resources (heterogeneity)
Extensible, allow for syntactic changes over time, no need for
global agreement on semantic values Facilitate resource leasing and allow PIPs to open abstract
views on their substrate Allow for vagueness and omission: customers are unlikely to
specifiy each CloudNet detail (e.g., KVM or Xen is fine, outsource to any European cloud provider): this opens ways for optimization (exploiting flexibilities)!
Allow for aggregation of resources (business secret?)
Non-topological requirements (e.g., wordsize compatibility)
aggregate
resources
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18 Telekom Innovation Laboratories Stefan Schmid
Exploiting Flexibilities: Resource Description Language.
Use Case: Web Service
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19 Telekom Innovation Laboratories Stefan Schmid
Exploiting Flexibilities: Resource Description Language.
Web Service / Overlay 0:
Mapping Layer: an virtual element per substrate element (n:m mapping)
Overlay 1: with splitters, two virtual links...
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20 Telekom Innovation Laboratories Stefan Schmid
Exploiting Flexibilities: Resource Description Language.
Underlay 1: all-provider view (splitter once collocated and once seperate)
Underlay 0: provider 1 view (NFS at SHost3)
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21 Telekom Innovation Laboratories Stefan Schmid
http://www.youtube.com/watch?v=llJce0F1zHQ
Demo.
YouTube Migration Demo
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22 Telekom Innovation Laboratories Stefan Schmid
(Theoretical) Research Overview.
Access Control
and Embedding
Service Migration Security Issues
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23 Telekom Innovation Laboratories Stefan Schmid
Online Access Control.
Access Control
and Embedding
Service Migration Security Issues
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24 Telekom Innovation Laboratories Stefan Schmid
Offline Embedding.
Access Control
and Embedding
Service Migration Security Issues
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25 Telekom Innovation Laboratories Stefan Schmid
How to Embed CloudNets Efficiently?
Computationally hard...
Our 2-stage approach:
Stage 1: Map quickly and heuristically
(dedicated resources)
Stage 2: Migrate long-lived CloudNets
to «better» locations (min max load, max
free resources, ...)
Typically: heavy-tailed durations, so old
CloudNets will stay longer!
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26 Telekom Innovation Laboratories Stefan Schmid
Flexible Embeddings.
Logo
T-Labs History
General Mathematical Program (MIP)
Advantages: 1. Generic (backbone vs datacenter) and allows for migration 2. Allows for different objective functions 3. Optimal embedding: for backgound optimization of heavy-tailed (i.e., long-lived) CloudNets, quick placement e.g., by clustering But: slow...
Schaffrath et al.:
UCC 2012
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27 Telekom Innovation Laboratories Stefan Schmid
The Solution.
Logo
T-Labs History
General Mathematical Program (MIP)
Advantages: 1. Generic (backbone vs datacenter) and allows for migration 2. Allows for different objective functions 3. Optimal embedding: for backgound optimization of heavy-tailed (i.e., long-lived) CloudNets, quick placement e.g., by clustering But: slow...
Schaffrath et al.:
UCC 2012
Advantages of MIP:
- Very general
- Supports easy replacement of objective functions
- Can use standard, optimized software tools such as CPLEX, Gorubi, etc.
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28 Telekom Innovation Laboratories Stefan Schmid
Generality of the MIP.
Objective functions: - minimize maximum load (= load balance) - maximize free resources (= compress as much as possible), ... Migration support: - costs for migration: per element, may depend on destination, etc. - answer questions such as «what is cost/benefit if I migrate now?» Embedding: - embedding full-duplex on full-duplex links - full-duplex on half-duplex links - or even multiple endpoint links (e.g., wireless) supported!
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29 Telekom Innovation Laboratories Stefan Schmid
On the Use of Migration.
Migration: Useful to increase the number of embeddable CloudNets, especially in under-provisioned scenarios
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30 Telekom Innovation Laboratories Stefan Schmid
Performance of the MIP: Setup.
Substrate: Rocketfuel ISP topologies (with 25 nodes) CloudNets: Out-sourcing scenario, CloudNets with up to ten nodes, subset of nodes fixed (access points) and subset flexible (cloud resources) Solver: CPLEX on 8-core Xeon (2.5GHz)
fixed access network
flexible nodes
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31 Telekom Innovation Laboratories Stefan Schmid
Performance of the MIP.
- Runtime below 1 minute per CloudNet, slightly increasing under load
- Impact of CloudNet size relatively small
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32 Telekom Innovation Laboratories Stefan Schmid
Performance of the MIP.
- Enabling option to migrate can increase execution time significantly (log scale!)
- Also number of flexible CloudNet components is important
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33 Telekom Innovation Laboratories Stefan Schmid
Use of Flexibility.
How much link resources are needed to embed a CloudNet with specificity s%?
PoS
Ludwig et al.:
UCC 2012
Up to 60%, even a little bit more if no migrations are possible!
Skewed (Zipf) distributions worst when not matching.
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34 Telekom Innovation Laboratories Stefan Schmid
CloudNet Embedding.
Goal: Where to realize CloudNet such that spec is met? Objective, e.g.: minimize allocation resources, minimize max load, save energy, … Currently focus on optimizing existing CloudNets (heavy-tailed lifetime assumption): but we are also working on quick embeddings (clustering, iterative, ...)
Mapping and Allocation
Logo
T-Labs History
Goal: Decide online which VNet requests to accept, such that profit is maximized
Online Access Control
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35 Telekom Innovation Laboratories Stefan Schmid
Online Access Control (1).
Physical Infrastructure
CPU, location, OS, ....
bw, latency, duplex, ...
benefit, duration, compatibility, ...
CPU, location, OS, ....
bw, latency, duplex, ....
VNet
embed
embed
SP
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36 Telekom Innovation Laboratories Stefan Schmid
Online Access Control (2).
VNet
100 $
A VNet request can specify a benefit and many different QoS requirements: - benefit if accepted - a set of terminals to connect (or just some reqs!) - desired bandwidth and allowed traffic patterns - a routing model - duration (from when until when?)
If VNets with these specifications arrive over time, which ones to accept online?!
Even et al.:
ICDCN 2012 (Best Paper)
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37 Telekom Innovation Laboratories Stefan Schmid
Online Access Control (3).
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38 Telekom Innovation Laboratories Stefan Schmid
CloudNet Specifications (1): Traffic Model.
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39 Telekom Innovation Laboratories Stefan Schmid
CloudNet Specifications (2): Routing Model.
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40 Telekom Innovation Laboratories Stefan Schmid
Competitive Embeddings.
Online algorithms make decisions at time t without any knowledge of inputs / requests at times t’>t.
Online Algorithm
How to deal with dynamic changes (e.g., mobility of users, arrival
of VNets, etc.)?
An r-competitive online algorithm ALG gives a worst-case performance guarantee: the performance is at most a factor r worse than an optimal offline algorithm OPT!
Competitive Analysis
Competitive ratio r, r = Cost(ALG) / cost(OPT) Is the price of not knowing the future!
Competitive Ratio
In virtual networks, many decisions need to be
made online: online algorithms and network
virtualization are a perfect match!
No need for complex predictions but still good!
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41 Telekom Innovation Laboratories Stefan Schmid
Buchbinder&Naor: Primal-Dual Approach.
Algorithm design and analysis follows online primal-dual approach recently invented by Buchbinder&Naor! (Application to general VNet embeddings, traffic&routing models, router loads, duration, approx oracles, ...)
1. Formulate dynamic primal and dual LP
2. Derive GIPO algorithm which always produces feasible primal solutions and where Primal >= 2*Dual
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42 Telekom Innovation Laboratories Stefan Schmid
Result.
Theorem
The presented online algorithm GIPO is log-competitive
in the amount of resources in the physical network!
If capacities can be exceeded by a log factor, it is even
constant competitive.
Also works for router loads
(determined by packet rate)!
Can also readjust embedding with migration! (Update LP weights...)
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43 Telekom Innovation Laboratories Stefan Schmid
Algorithm and Proof Sketch (1).
GIPO invokes an oracle procedure to determine cost of VNet
embedding!
Algorithm efficient... except for oracle (static, optimal embedding)!
What if we only use a suboptimal embedding here?!
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44 Telekom Innovation Laboratories Stefan Schmid
Algorithm and Proof Sketch (2).
Problem: computation of optimal embeddings NP-hard!
Thus: use approximate embeddings! (E.g., Steiner tree)
GIPO: Embedding Approx.:
<insert your favorite
approx algo>
Competitive ratio Approx ratio r
Lemma
The approximation does not reduce the overall competitive
ratio by much: we get *r ratio!
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45 Telekom Innovation Laboratories Stefan Schmid
Proof Sketch (1): Simplified LP.
maximize
benefit!
realization of i-th
request (will be integer,
accept fully or not at all)
... while ensuring
capacity and
no more than demand!
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46 Telekom Innovation Laboratories Stefan Schmid
Proof Sketch (2): Simplified LP. essentially, exponential load...
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47 Telekom Innovation Laboratories Stefan Schmid
Proof Sketch (3): Simplified LP.
oracle
(triangle only)
update primal
variables if accepted
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48 Telekom Innovation Laboratories Stefan Schmid
Proof Sketch (4): Simplified LP.
after each request,
primal variables
constitute feasible
solutions...
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49 Telekom Innovation Laboratories Stefan Schmid
Migration.
Access Control
and Embedding
Service Migration Security Issues
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50 Telekom Innovation Laboratories Stefan Schmid
Online Service Migration.
Logo
T-Labs History
Goal: E.g., QoS (=“move with the sun”, or with commuters); or for maintenance or to turn off resources (energy conservation).
Online Migration and Allocation
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51 Telekom Innovation Laboratories Stefan Schmid
The Virtual Service Migration Problem.
Assume: one service, migration cost size/bw (e.g., service interruption cost),
access cost sum of link delays along migration path.
When and where to move for offline algorithm or optimal competitive ratio?
A special instance of Metrical Task Systems.
on service!
Bienkowski et al.:
SIGCOMM VISA 2010
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52 Telekom Innovation Laboratories Stefan Schmid
Deterministic Algorithm.
Center of Gravity
For each node v, use COUNT(v) to count access cost if
service was at v during entire epoch. Call nodes v with
COUNT(v) < m/40 active. If service is at node w, a
phase ends when COUNT(w)¸m: the service is
migrated to the center of gravity of the remaining
active nodes („center node“ wrt latency or hop distance).
If no such node is left, the epoch ends.
Idea: Migrate to center of gravity when access cost at current
node is as high as migration cost! Time between two migrations: phase, multiple phases constitute an epoch:
In each phase go to center of nodes which are better!
Arora et al.:
IPTComm 2011
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53 Telekom Innovation Laboratories Stefan Schmid
Center of Gravity Algo: Example.
53
Before phase 1:
active
inactive
on service!
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54 Telekom Innovation Laboratories Stefan Schmid
Center of Gravity Algo: Example.
54
Before phase 2:
active
inactive
on service!
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55 Telekom Innovation Laboratories Stefan Schmid
Center of Gravity Algo: Example.
55
Before phase 3:
active
inactive
on service!
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56 Telekom Innovation Laboratories Stefan Schmid
Center of Gravity Algo: Example.
56
Epoch ends!
active
inactive Of course, not converging if demand is dynamic!
(Simplified example.)
on service!
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57 Telekom Innovation Laboratories Stefan Schmid
Center of Gravity Algo: Result.
Competitive analysis? Assume constant bandwidths!
r = ALG / OPT ?
Lower bound cost of OPT:
In an epoch, each node has
at least access cost m, or
there was a migration of cost m.
Upper bound cost of ALG:
We can show that each phase
has cost at most 2m (access
plus migration), and there are
at most log(n) many phases
per epoch!
Theorem
ALG is log(n) competitive! A special uniform metrical task system (graph metric for access)!
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58 Telekom Innovation Laboratories Stefan Schmid
Optimality?
Theorem «Center of Gravity» algorithm is
log(n) competitive!
Lower Bound No online algorithm can be better
than log(n)/loglog(n) competitive!
Also a much simpler randomized
algorithm achieves this!
Reduction to online
function tracking.
Theorem There is an algorithm «Follower»
that achieves log(n)/loglog(n)!
Schneider et al.:
INFOCOM 2013
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59 Telekom Innovation Laboratories Stefan Schmid
Extension: Inter-Provider Migration.
Migration across provider boundary costs transit/roaming costs (# transit
providers), detailed topology not known, etc.
PIP 1
PIP 2
PIP 4
PIP 3 Theorem Competitive ALGs still exist!
PIP 3
Adding page migration aspects...
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60 Telekom Innovation Laboratories Stefan Schmid
Extension: Multiple Servers.
Multiple servers allocated and migrated dynamically depending
on demand and load, servers have running costs, etc.
PIP 3 Theorem Competitive ALGs still exist!
on service!
on service!
Arora et al.:
USENIX Hot-ICE 2011
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61 Telekom Innovation Laboratories Stefan Schmid
Extension: Economical Aspects.
How to price resources?
Pay-as-you-go vs Pay-as-you-come?
on service!
Hu et al.: ICDCN 2013
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62 Telekom Innovation Laboratories Stefan Schmid
Simulation.
Dynamics due to mobility: requests cycle through a 24h pattern: in the morning, requests distributed widely (people in suburbs), then focus in city centers; in the evening, reverse.
Commuter Scenario
Dynamics due to time zone effects: request originate in China first, then more requests come from European countries, and finally from the U.S.
Time Zone Scenario
Algorithm which uses optimal static server placements for a given request seq.
Static Algorithm
Predictable scenarios, but we do not exploit that. Reality less predictable!
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63 Telekom Innovation Laboratories Stefan Schmid
Results.
Competitive ratio generally relatively low. Increases for more correlated requests and more dynamics.
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64 Telekom Innovation Laboratories Stefan Schmid
Security of Embedding.
Access Control
and Embedding
Service Migration Security Issues
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65 Telekom Innovation Laboratories Stefan Schmid
Security Issues.
Are VNet embeddings a threat for ISPs?
Do embeddings leak information about infrastructure?
Pignolet et al.:
DISC 2012, Mini-INFOCOM 2013
?
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66 Telekom Innovation Laboratories Stefan Schmid
Request Complexity.
Are VNet embeddings a threat for ISPs?
How many embeddings needed to fully reveal topology?
Request Complexity
?
?
?
Yes
Yes
No
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67 Telekom Innovation Laboratories Stefan Schmid
Some Properties Simple...
«Is the network 2-connected?»
?
No
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68 Telekom Innovation Laboratories Stefan Schmid
Others not…
PIP 3 Tree Can be explored in O(n)
requests. This is optimal!
PIP 3 General Graph Can be explored in O(n^2)
requests. This is optimal!
Cactus Graph Can be explored in O(n)
requests. This is optimal!
Lower bound: via number of possible trees and binary information.
Idea: Make spanning tree and then try all edges. (Edges directly does not work!)
Via «graph motifs»! A general framework exploiting poset relation.
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69 Telekom Innovation Laboratories Stefan Schmid
The Project Website.
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70 Telekom Innovation Laboratories Stefan Schmid
Collaborators and Publications. People
T-Labs / TU Berlin: Anja Feldmann, Carlo Fürst, Johannes Grassler, Arne Ludwig, Matthias Rost, Gregor Schaffrath, Stefan Schmid
Uni Wroclaw: Marcin Bienkowski Uni Tel Aviv: Guy Even, Moti Medina NTT DoCoMo Eurolabs: Group around Wolfgang Kellerer LAAS: Gilles Tredan ABB: Yvonne Anne Pignolet IBM Research: Johannes Schneider Arizona State Uni: Xinhui Hu, Andrea Richa
Publications
Prototype: ICCCN 2012, ERCIM News 2012, SIGCOMM VISA 2009
Migration: INFOCOM 2013, ICDCN 2013, Hot-ICE 2011, IPTComm 2011, SIGCOMM VISA 2010
Embedding: INFOCOM 2013 (Mini-Conference), 2 x ACM UCC 2012, DISC 2012, ICDCN 2012 (Best Paper Distributed Computing Track)
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71 Telekom Innovation Laboratories Stefan Schmid
Dr. Stefan Schmid
Telekom Innovation Laboratories Ernst-Reuter-Platz 7, D-10587 Berlin E-mail: [email protected]
Project website: http://www.net.t-labs.tu-
berlin.de/~stefan/virtu.shtml
Contact.