An Opportunistic Resource Sharing and Topology-Aware Mapping Framework for Virtual Networks

1. 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. 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 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

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