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

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

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

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]

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]

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

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;

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

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’

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.

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

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

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 !

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!

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.

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

Results: Comparison of algorithms

Results: Comparison of algorithms

Results: Comparison of algorithms

Results: Impacts of parameters

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

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

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

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)

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

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.

The ORSTA Framework

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.