Service Placement in Network-aware Cloud Infrastructures

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1 Service Placement in Network-aware Cloud Infrastructures Visions of Future Generation Networks (EuroView2009) Andreas Reifert July, 28th 2009 Universität Stuttgart Institute of Communication Networks and Computer Engineering (IKR) Prof. Dr.-Ing. Andreas Kirstädter Agenda Motivation Can the Cloud host telecommunication s? Integrated infrastructure Service and infrastructure model Service placement Algorithms Evaluation methodology Results Conclusion 2

2 Motivation Telecommunication requirements Maximal network delay guarantees / Maximal response time guarantees Due to Interactivity State lookup Data synchronization Particular locations of components Specialized components Replicated components Bandwidth guarantees Due to Stream transfers Large content transfers Particular locations of components Specialized components 3 Motivation Telecommunication requirements Maximal network delay guarantees / Maximal response time guarantees Due to Interactivity State lookup Data synchronization Particular locations of components Specialized components Replicated components Bandwidth guarantees Due to Stream transfers Large content transfers Particular locations of components Specialized components 4

3 Motivation Telecommunication s in the Cloud? Cloud = Infrastructure as a Service Cloud Decoupling of Service Provider is customer of Resource Infrastructure Provider (Cloud Provider) Components Loosely coupled Placement / location not relevant Usually one location Sufficient bandwidth Low inter-component delay No Network View No delay guarantees No bandwidth guarantees 5 Motivation IMS Another real world telecommunication Source: Wikipedia 6

4 Motivation Integrated Infrastructure and Network BMBF Project MAMS/MAMSplus Simple communication creation and execution for non-experts, Intelligent Service Oriented Network Infrastructure, Concepts and prototype Service AND resource management Integrated view of s, infrastructure, and network necessary 7 Model Service Description 8

5 Model Service Description 9 Model Service Description 10

6 Model Service Description 11 Model Service Description 12

7 Model Service Description 13 Model Resource Infrastructure / Network 14

8 Model Resource Infrastructure / Network 15 Model Resource Infrastructure / Network 16

9 Model Resource Infrastructure / Network 17 Model Placement 18

10 Model Placement Matching and Selection Placement 19 Placement Strategies RAND NODE TOP Type Random Greedy Greedy Optimal Principle Uninformed Node-based (Only Node resources) Topology-based (Service and Network inc. resources) MILP (Mixed Integer Linear Program) Quality Best Uninformed worst Low RAND NODE TOP High Complexity 20

11 Placement Evaluation Methodology Monte Carlo Simulation Independent samples with random placed on random infrastructure/network Parameters Allocated resources in infrastructure/network 0% (Empty) 100% (Full) Characteristic of Total Resource Demand Heavy-weight 80% Light-weight 20% Centralized Topology Distributed Performance Metrics Ability Fraction of non-placed s (Rejection) Quality Comparison of total link bandwidth allocation w.r.t. allocation 21 Results Ability to Find Placement Centralized, heavy-weight Centralized heavy-weight Low optimization potential % 90 RAND 80 Available Resources [%] Centralized, heavy weight % Non Placed Services 1% 0.1% 0.01% Allocated Resources [%] 22

12 Results Ability to Find Placement Centralized, heavy-weight Centralized heavy-weight Low optimization potential % 90 RAND 80 Available Resources [%] Centralized, heavy weight % Non Placed Services 1% 0.1% Best Functional Approximations (Regressions) 0.01% Allocated Resources [%] 23 Results Ability to Find Placement Centralized, heavy-weight Centralized heavy-weight Low optimization potential TOP close to % 90 RAND NODE 80 Available Resources [%] Centralized, heavy weight Service- and networktopology matter Non Placed Services 10% 1% TOP 0.1% Best Functional Approximation 0.01% Allocated Resources [%] 24

13 Results Ability to Find Placement Distributed, light-weight Distributed, light-weight Significant observed differences in algorithmic behavior High optimization potential Up to several orders of magnitude Even between TOP and Non Placed Services % 10% 1% Available Resources [%] Centralized, heavy weight RAND NODE TOP RAND NODE TOP Distributed, light weight 0 Service- and networktopology matter Simple algorithms leave significant room for improvement 0.1% Best Functional Approximation 0.01% Allocated Resources [%] 25 Results Quality of Found Placement PRELIMINARY RESULTS Centralized, heavyweight TOP almost optimal Distributed, light-weight TOP with acceptable placements If found! Behavior of NODE not yet understood Improved performance in high occupancy region due to few possible placements Total Link Bandwidth Allocation w.r.t. Available Resources [%] % Centralized, heavy weight 500% RAND 400% 300% 200% 100% Distributed, light weight RAND NODE NODE TOP TOP 10 0 Improvements without modification to routing! 0% Allocated Resources [%] 26

14 Conclusion Current IaaS Clouds not prepared for telecommunication s Network view essential for channels between components and towards end-systems Delay requirements Required bandwidth guarantees Integrated view Service/Infrastructure/Network necessary for system management Detailed model Placement of components has significant impact on Number of running s Bandwidth consumption Good placement algorithms must match - and network-topology Especially for distributed s TOP leaves room for improvement 27

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