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Using  a Lifecycle Model for Adaptable Interactive Distributed Applications D. Meiländer , S. Gorlatch, C. Cappiello, V. M...
ROIA: New Class of Interactive Distributed Apps <ul><li>Real-Time Online Interactive Applications (ROIA) , e.g.  online ga...
Service-Oriented System Engineering for ROIA <ul><li>To meet these requirements ROIA use service-oriented architectures fo...
The S-Cube Lifecycle Model <ul><li>Two coexisting cycles support each other during application lifetime </li></ul><ul><ul>...
Case Study Architecture: Actors <ul><li>Multi-layered software system </li></ul><ul><ul><li>Real-time adaptation, resource...
Case Study Architecture: Services <ul><li>Monitoring service  receives application-specific monitoring information </li></...
The Real-Time Framework (RTF) <ul><li>RTF simplifies ROIA development by providing: </li></ul><ul><ul><li>High-level API f...
Adaptation Strategies in RTF <ul><li>Zoning:   disjoint areas, each zone assigned to one application server </li></ul><ul>...
ROIA Development in S-Cube Lifecycle <ul><li>Adaptation triggers: </li></ul><ul><li>QoS (e.g., < 25 updates/s) </li></ul><...
Mapping: Adaptation Triggers    Adaptation Strategy 10 / 14 Adaptation trigger Monitored variable Adaptation trigger rule...
State of implementation <ul><li>Multiple sample applications: </li></ul><ul><ul><li>Porting existing apps to Grid & Cloud ...
Experimental Results <ul><li>Evaluation of derived adaptation strategies </li></ul><ul><li>Experimental setup: </li></ul><...
Summary and Future Work <ul><li>Summary </li></ul><ul><li>Generic engineering methodologies for adaptable ROIA </li></ul><...
 
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D. Meiländer, S. Gorlatch, C. Cappiello, V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications

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D. Meiländer, S. Gorlatch, C. Cappiello, V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications

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D. Meiländer, S. Gorlatch, C. Cappiello, V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications

  1. 1. Using a Lifecycle Model for Adaptable Interactive Distributed Applications D. Meiländer , S. Gorlatch, C. Cappiello, V. Mazza, R. Kazhamiakin, and A. Bucchiarone University of Muenster, Germany Politecnico di Milano, Italy Fondazione Bruno Kessler, Trentino, Italy ServiceWave 2010 13-15 December 2010
  2. 2. ROIA: New Class of Interactive Distributed Apps <ul><li>Real-Time Online Interactive Applications (ROIA) , e.g. online games or interactive e-learning, are large-scale Internet apps characterized by: </li></ul><ul><li>Huge number of concurrent users in a single application instance (e.g., more than 40.000 simultaneous participants in Eve Online) </li></ul><ul><li>Very high update rate of the app state (5-100 updates/sec) </li></ul><ul><li>Short response time to user actions (< 100 ms) </li></ul><ul><li>Tolerating high-latency and low-bandwidth Internet connections </li></ul><ul><li>Variable user load, daytime-dependent </li></ul> / 15
  3. 3. Service-Oriented System Engineering for ROIA <ul><li>To meet these requirements ROIA use service-oriented architectures for adaptation </li></ul><ul><li>Challenge: ROIA must adapt to changing environments (e.g., to address new user privileges, prevent fraudulent behaviour, etc.) </li></ul><ul><li>Goal: Devise generic engineering methodologies for adaptable ROIA </li></ul><ul><ul><li>For developers : design principles for adaptable ROIA </li></ul></ul><ul><ul><li>For service providers : transparent, autonomous & proactive adaptation at runtime </li></ul></ul><ul><li>Our previous work: </li></ul><ul><ul><li>Lifecycle Model for service-oriented apps from the European NoE S-Cube </li></ul></ul><ul><ul><li>Service-oriented architecture for ROIA from the European project [email_address] </li></ul></ul><ul><ul><ul><li>in particular distribution methods realized by the Real-Time Framework (RTF) </li></ul></ul></ul><ul><li>This talk: Applying the S-Cube Lifecycle Model to the edutain@grid architecture </li></ul><ul><ul><li>Develop suitable adaptation mechanisms and design patterns for ROIA </li></ul></ul>3 / 14
  4. 4. The S-Cube Lifecycle Model <ul><li>Two coexisting cycles support each other during application lifetime </li></ul><ul><ul><li>Evolution: Design-time iteration cycle, explicit re-design of the application to address new requirements (e.g., changing user preferences, Cheating, etc.) </li></ul></ul><ul><ul><li>Adaptation: Runtime cycle addressing adaptation </li></ul></ul>4 / 14
  5. 5. Case Study Architecture: Actors <ul><li>Multi-layered software system </li></ul><ul><ul><li>Real-time adaptation, resource management and scheduling </li></ul></ul><ul><li>Two main actors (gray) interacting with our service-oriented architecture </li></ul><ul><ul><li>End-user accesses a ROIA session through a (graphical) client </li></ul></ul><ul><ul><li>Hoster provides a computational & network infrastructure for ROIA sessions </li></ul></ul>
  6. 6. Case Study Architecture: Services <ul><li>Monitoring service receives application-specific monitoring information </li></ul><ul><li>Steering service identifies unexpected adaptation needs (using monitoring service) & finds suitable adaptation strategy </li></ul><ul><li>Resource allocation service allocates hoster resources to clients (depending on the load of resources) </li></ul><ul><li>Capacity management service identifies adaptation needs proactively (e.g., using load prediction) & finds suitable adaptation strategy </li></ul><ul><li>-> Adaptation triggered by Steering and Capacity management service </li></ul>
  7. 7. The Real-Time Framework (RTF) <ul><li>RTF simplifies ROIA development by providing: </li></ul><ul><ul><li>High-level API for scalable ROIA on multiple servers (Grid or Cloud) </li></ul></ul><ul><ul><li>A variety of distribution & parallelization techniques </li></ul></ul><ul><ul><li>Monitoring of application-specific parameters (e.g., updates/s) </li></ul></ul><ul><ul><li>Improving QoS by adding resources during runtime transparently for consumers </li></ul></ul>7 / 14 Clients Clients
  8. 8. Adaptation Strategies in RTF <ul><li>Zoning: disjoint areas, each zone assigned to one application server </li></ul><ul><li>Instancing: independently processed copies of subareas, each copy assigned to one application server </li></ul><ul><li>Replication: single zone processed by multiple application servers </li></ul><ul><li>User migration: client connection is switched seamlessly between two application servers replicating the same zone </li></ul><ul><li>QoS negotiation: includes (i) re-negotiation of existing contracts, (ii) negotiation of contracts with new hosters </li></ul>8 / 14
  9. 9. ROIA Development in S-Cube Lifecycle <ul><li>Adaptation triggers: </li></ul><ul><li>QoS (e.g., < 25 updates/s) </li></ul><ul><li>Computational context (e.g., server CPU load > 95 %) </li></ul><ul><li>Business context (e.g., new users) </li></ul><ul><li>Prediction (e.g., additional users) </li></ul><ul><li>Implement distribution and parallelization (using RTF) </li></ul><ul><li>Implement adaptation triggers on basis of RTF monitoring </li></ul><ul><li>Consider </li></ul><ul><li>priority of adaptation, </li></ul><ul><li>free resources, </li></ul><ul><li>etc. </li></ul>RTF distribution & parallelization techniques Introduce monitoring parameters and adaptation strategies Monitor adaptation triggers Identify application requirements (short response time, high update rate, proactive adaptation, etc.) Initial SLA negotiation with hosters 9 / 14
  10. 10. Mapping: Adaptation Triggers  Adaptation Strategy 10 / 14 Adaptation trigger Monitored variable Adaptation trigger rule Adaptation strategy Change in QoS Update rate, throughput, resource usage, latency update rate < 25 updates/s user migration, replication or instancing Change in comput. context CPU and memory load, incoming / outgoing bandwidth CPU load > 90% user migration, replication or instancing Change in business context # concurrent users, # requests per app # concurrent users >  user capability of app servers user migration, replication or instancing Prediction values number of users/hour, number of requests per application predicted users > current users +  (threshold) QoS negotiation, replication or instancing/zoning
  11. 11. State of implementation <ul><li>Multiple sample applications: </li></ul><ul><ul><li>Porting existing apps to Grid & Cloud </li></ul></ul><ul><ul><li>Quake 3 </li></ul></ul><ul><ul><li>High-performance simulations </li></ul></ul><ul><ul><li>PVS-Crowdsim </li></ul></ul><ul><ul><li>Interactive e-learning and training </li></ul></ul><ul><ul><li>edutain@grid Virtual Classroom </li></ul></ul><ul><ul><li>New gaming applications (MMORPG, massively multiplayer action games) </li></ul></ul><ul><ul><li>Hunter, RTFDemo, Bioclysm, Offshore </li></ul></ul>
  12. 12.
  13. 13. Experimental Results <ul><li>Evaluation of derived adaptation strategies </li></ul><ul><li>Experimental setup: </li></ul><ul><ul><li>Fast-paced 3D action game (25 updates/s) </li></ul></ul><ul><ul><li>Clients are simulated by bots moving randomly & shooting at opponents if in sight </li></ul></ul><ul><ul><li>Used Hardware: 2.66GHz C2D, 4 GB </li></ul></ul><ul><li>Zoning scales almost linearly </li></ul><ul><ul><li>1 Server: 450 clients </li></ul></ul><ul><ul><li>4 Server: > 1500 clients </li></ul></ul><ul><li>Replication allows to increase player density considerably </li></ul><ul><ul><li>4 Server: 1000 clients in a single zone </li></ul></ul><ul><li>Result: Scalability when using RTF </li></ul>Zoning Replication
  14. 14. Summary and Future Work <ul><li>Summary </li></ul><ul><li>Generic engineering methodologies for adaptable ROIA </li></ul><ul><ul><li>For developers: Design-time requirements for ROIA </li></ul></ul><ul><ul><li>For service providers: Transparent adaptation triggers & strategies for ROIA </li></ul></ul><ul><ul><li>-> Lifecycle Model supports development of high-quality, scalable ROIA </li></ul></ul><ul><li>Runtime experiments show the effectiveness of adaptation strategies implemented by RTF </li></ul><ul><li>Future Work </li></ul><ul><li>Implementation of adaptation triggers identified in our case study </li></ul><ul><li>Developing design patterns for identified adaptation scenarios </li></ul><ul><li>Demonstrate the effect of design decisions on performance & interdependence of various adaptation strategies </li></ul><ul><li>Implementation of our case study on Cloud systems </li></ul>13 / 14

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