Anycast End- to- end Resilience for Cloud Services over Virtual Op:cal Networks

Transcription

1 Anycast End- to- end Resilience for Cloud Services over Virtual Op:cal Networks Minh Bui*, Brigi,e Jaumard *, and Chris Develder *: CSE Dept., Concordia University, Montreal, Canada : Ghent University IBBT, INTEC IBCN, Ghent, Belgium

2 Op:cal clouds OpHcal networks crucial for increasingly demanding cloud services, e.g., CompuHng: High energy physics Amazon EC2, MicrosoQ Azure Online storage: Dropbox, Google Drive, etc. CollaboraHon tools: MSOffice 365, Google Doc Video streaming: NeYlix, YouTube C. Develder, et al., "Op9cal networks for grid and cloud compu9ng applica9ons", Proc. IEEE, Vol. 100, No. 5, May 2012, pp M. Bui, et al., "Anycast end- to- end resilience for cloud services over virtual op9cal networks", ICTON 2013

3 Network virtualiza:on Physical network is logically parhhoned in isolated virtual networks VNO 1 VNO 2 Virtual Network Operators (VNO) operate logically separate networks Physical Infrastructure Providers (PIP) have full control over infrastructure (fibers, OXCs) PIP J.A. García- Espín, et al., "Logical Infrastructure Composi9on Layer: the GEYSERS holis9c approach for infrastructure virtualisa9on", in Proc. TERENA Networking Conference (TNC 2012), Reykjavík, Iceland, May 2012.

4 Overview 1. IntroducHon 2. Problem statement 3. Model & soluhon approach 4. Case study 5. Conclusions

5 Resiliently provisioning virtual cloud networks How to choose the virtual to physical mapping, such that Services remain available in case of network failures Bandwidth for providing services is minimal? Note: Anycast: requests coming from users can be served by any server Cloud services offered by VNO Cloud services run on top PIP Users app? Grid/cloud resources Grid Resources B. Jaumard, A. Shaikh and C. Develder, "Selec9ng the best loca9ons for data centers in resilient op9cal grid/cloud dimensioning (Invited Paper)", in Proc. 14th Int. Conf. Transparent OpHcal Netw. (ICTON 2012), Coventry, UK, 2-5 Jul

6 Protec:on scheme concept Covered failures Network links à disjoint primary and backup paths Data centers à disjoint primary and backup server locahons Primary DC Δ k Service source δ k Backup DC Note: synchroniza:on between data centers for smooth fail- over switching! We assume: sync needs frachon of service bandwidth

7 Two proposed protec:on schemes Scheme 1: VNO- resilience Scheme 2: PIP- resilience V 7 V 6 V 3 S V 7 V 6 V 3 S S' V 2 S' V 2 W V 5 V 9 W B V 5 V 9 V 8 B V 4 V 8 V 4 V 1 V 10 V 1 V 10

8 Related work TradiHonal dimensioning (no virtualisahon, no resilience): Develder et al. 2009: Anycast, flexibility in choosing data center Resilient dimensioning problem: Shaikh et al. 2011, Develder et al. 2012: scalable method, no synchronizahon between working and backup DCs RouHng cloud service requests and mapping a VNet to the physical infrastructure separately: Lee et al. 2009, Yu et al. 2010: Survivable VNet embedding, but assume VNet is given Jiang et al. 2012, Alicherry et al. 2012: OpHmal server selechon and rouhng of anycast services in the physical layer for intra- and inter- DC networks but no resilient network design in the virtual layer VNet planning problem: Barla et al. 2012, Barla et al. 2013: using mixed integer linear programming, but no synchroniza9on between working and backup DCs

9 Problem statement Given Cloud network topology: G = (V, L), with V = nodes, L = links LocaHons of the data centers, V D V Set of service requests, K v k : source of service Δ k : bandwidth requirement Services originahng from the same source are aggregated Find Choice of primary and backup DC locahons for each service Primary, backup and synchronizahon paths Such that total used network bandwidth uhlizahon is minimized

10 Solu:on: Column genera:on model Column generahon idea: Many different configurahons Start from a restricted subset of such configurahons IteraHvely find addihonal configurahons that reduce the cost: (1) Restricted Master Problem (RMP) (2) Pricing Problem (PP) to find new configs A configurahon = Working path Backup path 2 sync paths, one in each direchon, between the primary & backup DCs Set of services protected by the set of 4 paths V 7 V 6 V 9 V 1 W V 8 B V 3 V 5 S V 10 S' V 4 V 2

11 Column genera:on solu:on algorithm Added columns RMP (MinimizaHon LP) Dual Values Pricing Problem Configura9on generator Selec9on of the most promising configura9ons Add improving column / configura9on Y Reduced cost < 0? N Found new cost op9mizing configura9on? ILP solu:on Solve ILP of RMP ILP solu:on

12 Restricted Master Problem (RMP) V 3 S ObjecHve: V 7 V 6 S' V 2 V 9 W B V 5 V 8 V 4 Constraints: working backup V 1 V 10

13 Pricing Problem (PP) Minimize Cubic and square terms!

14 Column genera:on solu:on algorithm - Heuris:c RMP (MinimizaHon LP) Selec9on of the most promising configura9ons Added columns Dual Values Choose next src Y Find closest DC, fix p W Fix α K =1 for k K vs Solve PP N Configura9on generator Add improving column / configura9on N Reduced cost < 0? Y ILP solu:on Solve ILP of RMP ILP solu:on

15 Case study Oslo Stockholm Topology: NobelEU European network 28 nodes and 41 bidir links (=82 directed) Two choices of 4 data centers (DCs) Sca]ered evenly: Lyon, Berlin, London, Vienna Pairs of close data centers: Amsterdam, Hamburg, Lyon, Zurich Glasgow Dublin London Bordeaux Madrid Copenhagen Hamburg Amsterdam Berlin Warsaw Frankfurt Brussels Paris Strasbourg Munich Lyon Barcelona Prague Zurich Budapest Vienna Milan Zagreb Rome Belgrade Athens SynchronisaHon bandwidth frachon: δ k = 0.1 or 0.9 Requests generated randomly with bandwidth in [0,1] wavelengths

16 Results: evenly distributed DCs DCs in Lyon, Berlin, London, and Vienna Model 1: VNO- resilience, Model 2: PIP- resilience bandwidth Intuitively expected: for low sync bandwidth, VNO-resilience better bandwidth Benefit diminishes for larger sync bandwidth number of requests number of requests

17 Results: close DC pairs DCs in Amsterdam, Hamburg, Lyon, Zurich Model 1: VNO- resilience, Model 2: PIP- resilience bandwidth VNO-resilience advantage almost disappears, esp. for large sync bandwidth bandwidth number of requests number of requests

18 Conclusions Scalable column- generahon based method for resilient VNet planning IntuiHon: VNO- resilience has lower physical network requirements than PIP- resilience But relahve advantage of VNO- resilience may be limited When accounhng for synchronizahon bandwidth between DCs If DCs occur in nearby locahons Future work: OpHmizaHon of choice of DC locahons? Incorporate DC capacity constraints (e.g., limit max load)

19 Thank you any ques:ons?? Prof. Chris Develder Ghent University iminds M. Bui, et al., "Anycast end- to- end resilience for cloud services over virtual op9cal networks", ICTON 2013