1. Sheng Zhanga, Zhuzhong Qiana, Jie Wub, and Sanglu Lua
An Opportunistic Resource Sharing and Topology-Aware Mapping Framework for Virtual Networks
Sheng Zhanga, Zhuzhong Qiana, Jie Wub, and Sanglu Lua
aNanjing University
bTemple University
INFOCOM 2012
Orlando, FL
March 25 – 30, 2012

2. Network Virtualization
Infrastructure provider (InP): physical/substrate network (SN)
Service provider (SP) purchases slices of resource (e.g., CPU, bandwidth, memory) from the InP and then creates a customized virtual network (VN) to offer value-added service (e.g., content distribution, VoIP) to end users

3. Virtual Network Mapping
VNM is to embed multiple VN requests with resource constraints into a substrate network
Different virtual nodes -> different substrate nodes
VN requests arrive over time: first come, first serve
The objective is to maximize the revenue of InP, that is, maximize the utilization ratio of physical resources
VN request 1
VN request 2

4. Virtual Network Mapping
Given a VN request and a substrate nerwork, the problem of determining whether the request can be embeded without any constraints violation is NP-hard
[Andersen 2002]

5. Related Work
Simulated Annealing: [Ricci et al. 2003][Zhang et al. 2011]
Load Balancing: [Zhu & Ammar 2006]
Unlimited resources
Path Splitting: [Yu et al. 2008]
Multi-commodity flow problem
Location Constraints: [Chowdhury et al. 2009]
Integer Linear programming + determinstic/randomized rounding
Inter-domain mapping: [Chowdhury et al. 2010]

6. unefficient resource utilization
Motivation
It is difficult to predict the workload precisely
SP potentially target users all over the world
SPs often over-purchase physical resources
To cope with a peak workload on demand
Double Win: details , please see paper.
unefficient resource utilization

7. The ORSTA framework 1: Topology-aware node ranking (MCRank)
2: Macro level mapping
- Greedy node-to-node mapping
- maximum first
- Link-to-link mapping
- shortest path
3: Micro level assignment: for each substrate node and link,
- Local time slot assignment
Online;
Run by InP;

8. Step 1: Topology-Aware Node Ranking -Motivational Example
12 CPU, 8 Bandwidth
VN request 1
stretch
12 CPU, 2 Bandwidth

9. Topology-Aware Node Ranking -Basic Idea
PageRank:
The importance of a web page is determined not only by its own contents but also its neighbors’
Our observation:
The importance of a substrate node is determined not only by its own resource but also its neighbors’

10. Topology-Aware Node Ranking -Details
A node has a higher rank if it has more highly-ranked neighbors
The more neighbors one node has, the less its influence on their rankings
Iterative effect
Actually, MCRank is the stationary distribution of a Markov chain
We prove the existence of MCRank, and also give an algorithm for calculating it. Please refer to paper for details.

11. Step 2: Macro Level Mapping
Phase 1: node-to-node
Sort VN nodes according to their CPU requirements
Sort SN nodes according to their MCRank
Maximum first matching
Phase 2: link-to-link
shortest path
y-z: G-H-D ?
G-F-E-D ?
k-shortest path
multiple edges
VN request 1

12. Step 3: Micro Level Time Slot Assignment - Capture the fluctuation of workload
Workload model
Basic part: always exists, its percentage is bwl
Variable part: each unit occurs with a probability, pwl, in each time slot
CPU busy time and network flow: expressed in time slots
proportional to the workload
Examples:
Node “x”: basic 6 + variable 6
The possible units needed: 6,7,8,…,12
Only focus on a substrate link
Results can be applied to substrate nodes without any major changes
Only focus on variable sub-traffic
For basic sub-traffic, we have no choice but to allocate the required number of time slots
bwl=0.5
pwl=0.2

13. Step 3: Micro Level Time Slot Assignment
Only focus on a substrate link
Results can be applied to substrate nodes without any major changes
Only focus on variable sub-traffic in a substrate link
For basic sub-traffic, we have no choice but to allocate the required number of time slots
For variable sub-traffic
SHARE !

14. Step 3: Micro Level Time Slot Assignment - Tradeoff
When more than one variable sub-traffic occurs at the same time slot, a collision happens.
To save time slots for upcoming requets
A slot is shared among, the more virtual links the better
To guarantee performance
A slot is shared among, the less virtual links the better
A tradeoff!

15. Step 3: Micro level Time Slot Assignment - Breaking the tradeoff
Bin Packing
First-fit
Given multiple variable sub-traffic and a collision threshold,
find an assignment to minimize the slots used
How to accelerate the calculation of collision probability? See paper.

16. Simulation Setup Performance metrics Algorithms in comparison
Acceptance ratio: the higher, the better
Node/link utilization: the higher, the better
Algorithms in comparison
ORSTA: our entire framework
TA: only considers topology-awareness
ORS: only considers opportunistic resource sharing
Greedy: traditional greedy node and link mapping

17. Results: Comparison of algorithms

18. Results: Comparison of algorithms

19. Results: Comparison of algorithms

20. Results: Impacts of parameters

21. Conclusions
We re-examined the virtual network embedding problem from two novel aspects
Topology-awareness
Opportunistic resource sharing
We proposed a mapping framework, ORSTA, which contains three main components
Topology-aware node ranking
Macro level mapping
Micro level time slot assignment

23. The Internet is a great success! Information exchange
Applications support
Critical infrastructure
What is the map? It is the Internet! We all know that the Internet
Models the way we access and exchange information in the modern world successfully
Supports multitude of distributed apps and a wide variety of network technologies
Becomes Critical infrastructure for global commerce, media and defense
However, like many successful technologies,
It is suffering the adverse effects of inertia
Like many successful technologies
the Internet is suffering the adverse effects of inertia

24. Internet Ossification
Multiple network domains with conflicting interests
multilateral relationship? Difficult!
Deploy changes/updates? Global agreement!
The ever-expanding scope and scale of the Internet’s use
security, routing stability, etc.
So, Internet Ossification comes out. It is basically resulted from two aspects:
1)….
2)….
In all, it lacks flexibility + deversity
Flexibility + Diversity

25. Simulation Setup Similar settings to several existing studies
Substrate network
Topology: ANSNET/Arpanet
CPU & Bandwidth: [50,100], uniform
Collision threshold: 0.1
Virtual network
# of nodes: [2,10], uniform
Each pair of nodes connects with probability 0.5
Lifetime: 10 minutes, exponential
Arrivals: Possion process (0.2 minutes)

26. 0.1$ for the shared unit per hour
Motivation 1: example
1$ for one unit per hour
InP gets: 8$
SP1 or SP2 pays: 4$
No Free Lunch!
Collision may happen. (0.028 here)
InP gets: (3+0.1)*3=9.3$
SP1 or SP2 or SP3 pays: 3.1$
Assumption: 4 units demand= 3 units (always needed) + 1 unit (needed with probability 0.1)
0.1$ for the shared unit per hour

27. Residual Resource Estimation
The residual room in a time slot is defined as:
the maximal probability of a variable sub-traffic that this slot can still accommodate.

28. The ORSTA Framework

29. Topology-Aware Node Ranking
PageRank’s core idea
A page has a higher rank if it is pointed to by more highly-ranked pages
The more pages one page points to, the less its influence on their ranking is
MCRank
We prove that the Markov chain determined by P has a stationary distribution, i.e., MCRank.