1. A Service Platform for Development,
Deployment and Runtime Management
of Real-Time Online Services
Dominik Meiländer and Sergei Gorlatch
University of Muenster, Germany
24th February 2012 S-Cube Industry Workshop, Thales

2. S-Cube Virtual Campus
 Relevant Knowledge Model terms:
– Software Engineering Life-Cycle
– Service Adaptation
– Grid & Cloud Computing
 Relevant learning packages in Virtual Campus
– JRA-1.1: Designing and Migrating Service-Based Applications
– JRA-1.2: Adaptation and Evolution of Service Based Applications
– JRA-2.3: Service Infrastructure Management
The research leading to these results has received funding from S-Cube,
the European Network of Excellence in Software Services and Systems.

3. Real-Time Online Interactive Apps (ROIA)
Real-Time Online Interactive Applications (ROIA), e.g., online games or interactive
e-learning, are large-scale Internet apps with challenging requirements:
 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 (up to 100 Hz)
 Short response time to user actions (0.1 – 1.5 ms)
 Variable user load, daytime-dependent
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4. ROIA Development and Provision
Clients Clients
Current ROIA development:
 Choose a distribution/parallelization concept
 Create & maintain a customized run-time monitoring/controlling
 Implement trust and security (authentication, authorization, ...)
 Optimize for low-bandwidth and high-latency connections
RTF liberates developer from these cumbersome tasks…
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5. The Real-Time Framework (RTF)
RTF is a high-level development API & runtime environment that enables:
 Simplified app. development via parallelization and distribution techniques.
 Hiding and optimizing communication & synchronization (sockets in C++).
 Scalable and seamless use of resources (servers).
 Service adaptation and QoS for a changing user demand.
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6. Real-Time Application Loop
A model for application execution: Real-Time Loop
 Operated with high frequency (5-100Hz), in real time
1. Transfer user actions from the clients, receive in the server
2. Process the new game state: apply app logic, move entities
3. Transfer the app state update to the clients
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7. Distributing the App State for Parallelism
 RTF supports a variety of application state distributions and allows the developer
to combine different distributions
Game World Game World
Single
Game Server
Game Entities (Avatars, NPCs, items)
Basic Single Server Multi-Server Zoning
Game World
[..] instance [..]
servers
[..] [..] shadow entity
active entity
Multi-Server Instancing Multi-Server Replication
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8. Combining the Distribution Concepts
 RTF supports all three distribution concepts and their combinations
 Developer’s task: define zones and their possible replication
 RTF’s role: automatically manages distribution, parallel computation of entities
and all necessary communications
– (re)assignment of zones to the servers during runtime in a seamless way for
the developer, e.g., because of overload
– management related tasks, e.g. gathering of monitoring data
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9. A Demonstration of RTF
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10. Case Study: RTF Integration in Quake III
 Quake III: Popular commercial FPS game
– Fully fledged, fast-paced game
– Hand-optimized for high performance
and low network traffic
 Limitation of Quake III: does not scale
 Solution: RTF’s support for scalability
– RTF replaced the network module
of the Quake III game engine
– Quake III was modified for multiple servers
using RTF’s support for replication
 Experimental Results:
– In-game response time with RTF is nearly
equal to the original implementation
– RTF can scale Quake III (next slide)

11. ROIA Development and Adaptation
along the S-Cube Lifecycle Model
Two coexisting cycles support each other during application lifetime
 Evolution (right cycle):
– Design-time iteration cycle, supporting long-term changes due to, e.g.,
changing user preferences, continuous adaptation actions
– Targeted by RTF
 Adaptation (left cycle):
– Runtime cycle addressing adaptation to support short-term changes, e.g.,
daytime-dependent user load
– Targeted by our novel
RTF-RMS (Resource
Management System)
addressing ROIA adaptation
on Clouds (next slide)

12. ROIA on Clouds
 ROIA benefit from using Clouds/Infrastructure-as-a-Service (IaaS ):
– Choosing optimal hardware for particular ROIA
– Distribution of ROIA processing on arbitrary number of resources
– Dynamic adaptation of ROIA sessions
– Efficient and economic resource usage
 State-of-the-art:
– Cloud platforms offer adaptation services based on generic system
information (e.g., CPU load, bandwidth, etc.)
– Open challenge: Adaptation mechanisms taking into account application-
specific monitoring information & real-time communication QoS
 Our RTF-RMS (Resource Management System) provides:
1. Cloud resource allocation: addressing specific challenges on Cloud like
startup times, static leasing periods, etc.
2. ROIA adaptation on Clouds: addressing real-time QoS requirements of
ROIA
 RTF-RMS is built on top of the Real-Time Framework (RTF)
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13. RTF-RMS: Resource Allocation
Cloud resource allocation in RTF-RMS addresses the following challenges:
 Startup time optimization: Cloud resources are started in advance and stored in the
Resource Buffer
 Utilization of static leasing periods: dispensable resources are stored in the
Resource Buffer until they are demanded again or their leasing period finishes
 Minimizing cost-overhead: analytical model for Resource Buffer size
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14. RTF-RMS: Adaptation
RTF-RMS implements adaptation according to the S-Cube Lifecycle Model:
1. Operation & Management: receive monitoring values from RTF
2. Identify adaptation need: check monitoring data against trigger rules
3. Identify adaptation strategy: choose among four different adaptation strategies
4. Enact adaptation: enact the chosen adaptation strategy using RTF
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15. RTF-RMS: Adaptation Strategies
 RTF-RMS chooses between the following four adaptation strategies:
1. User migration: client connections are
switched seamlessly between two application
servers replicating the same zone
2. Replication enactment: new servers are
added to provide more computation power to a
highly frequented zone by replicating it
3. Resource substitution: running resources are
substituted by more powerful resources
4. Remove resource: dispensable resources are Replication: a single zone is processed
removed by multiple application servers
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16. Identify Adaptation Strategy: Example
 Monitoring data is summarized in zone reports
 Average load is compared with thresholds
– e.g., addResourceThreshold update rate: 25 Hz
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17. Experiments
 Experimental setup:
– Fast-paced 3D online game
– 260 clients are simulated by bots moving
randomly & shooting at opponents
– update rate should be > 25 Hz
 User migration (top picture):
– Avg. update rate increased from
25 Hz to ~100 Hz
 Replication enactment (bottom picture):
– Integration in app processing in ~15 s
– Without resource buffer: > 130 s
– Avg. update rate increased from
> 20 Hz to ~100 Hz
 Result: RTF-RMS adaptation improves QoS
for a changing number of users
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18. Industrial Collaborations
 Previous and existing collaboration with industry
– Spinor Gmbh (Germany): game engines
– Darkworks (France): video games
– BMT Cordah Ltd (UK): e-learning/training
 Looking for new industrial scenarios and
collaborations
– Interactive training scenarios requiring real-
time communication
– High-performance real-time interactive
simulations
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