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

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
Real-Time Online Interactive Applications (ROIA) , e.g. online games or interactive e-learning, are large-scale Internet apps characterized by:
Huge number of concurrent users in a single application instance (e.g., more than 40.000 simultaneous participants in Eve Online)
Very high update rate of the app state (5-100 updates/sec)
Short response time to user actions (< 100 ms)
Tolerating high-latency and low-bandwidth Internet connections
Variable user load, daytime-dependent
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Service-Oriented System Engineering for ROIA
To meet these requirements ROIA use service-oriented architectures for adaptation
Challenge: ROIA must adapt to changing environments (e.g., to address new user privileges, prevent fraudulent behaviour, etc.)
Goal: Devise generic engineering methodologies for adaptable ROIA
For developers : design principles for adaptable ROIA
For service providers : transparent, autonomous & proactive adaptation at runtime
Our previous work:
Lifecycle Model for service-oriented apps from the European NoE S-Cube
Service-oriented architecture for ROIA from the European project [email_address]
in particular distribution methods realized by the Real-Time Framework (RTF)
This talk: Applying the S-Cube Lifecycle Model to the edutain@grid architecture
Develop suitable adaptation mechanisms and design patterns for ROIA
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The S-Cube Lifecycle Model
Two coexisting cycles support each other during application lifetime
Evolution: Design-time iteration cycle, explicit re-design of the application to address new requirements (e.g., changing user preferences, Cheating, etc.)
Adaptation: Runtime cycle addressing adaptation
4 / 14

Case Study Architecture: Actors
Multi-layered software system
Real-time adaptation, resource management and scheduling
Two main actors (gray) interacting with our service-oriented architecture
End-user accesses a ROIA session through a (graphical) client
Hoster provides a computational & network infrastructure for ROIA sessions

Case Study Architecture: Services
Monitoring service receives application-specific monitoring information
Steering service identifies unexpected adaptation needs (using monitoring service) & finds suitable adaptation strategy
Resource allocation service allocates hoster resources to clients (depending on the load of resources)
Capacity management service identifies adaptation needs proactively (e.g., using load prediction) & finds suitable adaptation strategy
-> Adaptation triggered by Steering and Capacity management service

The Real-Time Framework (RTF)
RTF simplifies ROIA development by providing:
High-level API for scalable ROIA on multiple servers (Grid or Cloud)
A variety of distribution & parallelization techniques
Monitoring of application-specific parameters (e.g., updates/s)
Improving QoS by adding resources during runtime transparently for consumers
7 / 14 Clients Clients

Adaptation Strategies in RTF
Zoning: disjoint areas, each zone assigned to one application server
Instancing: independently processed copies of subareas, each copy assigned to one application server
Replication: single zone processed by multiple application servers
User migration: client connection is switched seamlessly between two application servers replicating the same zone
QoS negotiation: includes (i) re-negotiation of existing contracts, (ii) negotiation of contracts with new hosters
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ROIA Development in S-Cube Lifecycle
Adaptation triggers:
QoS (e.g., < 25 updates/s)
Computational context (e.g., server CPU load > 95 %)
Business context (e.g., new users)
Prediction (e.g., additional users)
Implement distribution and parallelization (using RTF)
Implement adaptation triggers on basis of RTF monitoring
Consider
priority of adaptation,
free resources,
etc.
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
Multiple sample applications:
Porting existing apps to Grid & Cloud
Quake 3
High-performance simulations
PVS-Crowdsim
Interactive e-learning and training
edutain@grid Virtual Classroom
New gaming applications (MMORPG, massively multiplayer action games)
Hunter, RTFDemo, Bioclysm, Offshore

Experimental Results
Evaluation of derived adaptation strategies
Experimental setup:
Fast-paced 3D action game (25 updates/s)
Clients are simulated by bots moving randomly & shooting at opponents if in sight
Used Hardware: 2.66GHz C2D, 4 GB
Zoning scales almost linearly
1 Server: 450 clients
4 Server: > 1500 clients
Replication allows to increase player density considerably
4 Server: 1000 clients in a single zone
Result: Scalability when using RTF
Zoning Replication

Summary and Future Work
Summary
Generic engineering methodologies for adaptable ROIA
For developers: Design-time requirements for ROIA
For service providers: Transparent adaptation triggers & strategies for ROIA
-> Lifecycle Model supports development of high-quality, scalable ROIA
Runtime experiments show the effectiveness of adaptation strategies implemented by RTF
Future Work
Implementation of adaptation triggers identified in our case study
Developing design patterns for identified adaptation scenarios
Demonstrate the effect of design decisions on performance & interdependence of various adaptation strategies
Implementation of our case study on Cloud systems
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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

by ServiceWave 2010 on Dec 30, 2010

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D. MeilÃ¤nder, S. Gorlatch, C. Cappiello,V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications — Presentation Transcript

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

by ServiceWave 2010 on Dec 30, 2010

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Upload Details

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D. MeilÃ¤nder, S. Gorlatch, C. Cappiello, V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications — Presentation Transcript

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

D. MeilÃ¤nder, S. Gorlatch, C. Cappiello,V. Mazza, R. Kazhamiakin, and A. Bucchiarone: Using a Lifecycle Model for Adaptable Interactive Distributed Applications — Presentation Transcript