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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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ServiceWave 2010
ServiceWave 2010

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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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
ROIA: New Class of Interactive Distributed Apps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object], / 15
Service-Oriented System Engineering for ROIA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],3 / 14
The S-Cube Lifecycle Model ,[object Object],[object Object],[object Object],4 / 14
Case Study Architecture: Actors ,[object Object],[object Object],[object Object],[object Object],[object Object]
Case Study Architecture: Services ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Real-Time Framework (RTF) ,[object Object],[object Object],[object Object],[object Object],[object Object],7 / 14 Clients Clients
Adaptation Strategies in RTF ,[object Object],[object Object],[object Object],[object Object],[object Object],8 / 14
ROIA Development in S-Cube Lifecycle ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],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
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
State of implementation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Zoning Replication
Summary and Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],13 / 14
 

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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. 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
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  • 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
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Editor's Notes

  1. (c) Prof. Dr. Klaus Pohl
  2. (c) Prof. Dr. Klaus Pohl
  3. (c) Prof. Dr. Klaus Pohl
  4. Figure: Without RTF the developer must implement application logic and he must also manually implement mechanisms for distribution management, network communication, monitoring and controlling. Using RTF he must only implement the application logic because RTF handles all the other tasks for him (c) Prof. Dr. Klaus Pohl
  5. (c) Prof. Dr. Klaus Pohl
  6. (c) Prof. Dr. Klaus Pohl
  7. (c) Prof. Dr. Klaus Pohl