In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.

1. Gabriele D’Angelo <gda@cs.unibo.it> http://www.cs.unibo.it/gdangelo/ Department of Computer Science University of Bologna From Simulation to Online Gaming: the need for adaptive solutions

3. Parallel and Distributed Simulation (PADS)

4. Adaptive migration-based approach

5. Simulation of scale-free networks

6. Gossip protocols

7. Scale-free networks and gossip protocols for online gaming

8. Conclusions and future work

10. The simulation is a widely used technique in the performance evaluation, risk assessment and security analysis of physical systems (or during the design phase)

11. It is fundamental for the creation of Digital Virtual Environments ( DVE s) and Online Games

13. For example, w ireless networks, under the simulation viewpoint, have very strict requirements in terms of level of detail

14. Monolithic simulators are unable to fulfill these scalability requirements due to memory constrains and excessive amount of time required to obtain the results

15. A Parallel And Distributed Simulation ( PADS ) can be used to aggregate memory and computational resources

17. Each LP is responsible to manage only a subset of Simulated Entities and is allocated on a different Physical Execution Unit ( PEU )

18. The PEU s are interconnected together using tightly coupled (parallel simulation) loosely coupled (distributed simulation) communication networks

20. Optimistc (Time-Warp)

25. In our vision, the reduction of the communication overhead and the computational load-balancing problems have to be considered at the same time

26. In practice, it is an instance of a dynamic partitioning problem in which there are many SE s, a (variable size) set of heterogeneous PEU s (with the corresponding LP s) and a communication network (with performance varying in time)

28. It is necessary to employ a dynamic and adaptive approach that reacts to imbalances in the architecture and proactively enhances the model partitioning

29. We propose a migration-based approach : each entity in the simulation can be dynamically relocated (migrated), moving from a source PEU to a new destination PEU

31. This can be done considering the communication pattern of each model entity that is in the simulation

35. The synchronization points in the distributed architecture are exploited to tag each LP as “ fast ” or “ slow ”

39. the algorithm has to be “ fully distributed ” (without any centralization point) and therefore has to take decisions without any “global vision” of the distributed system

40. the mechanism has to react very quickly to internal ( i.e. the creation of new simulated entities ) and external events ( i.e. a burst of CPU or network load ) but without introducing oscillatory behaviors

42. it adaptively reacts to the behavior of the simulated model, such as hotspots in the simulated area

43. the execution platform can be very heterogeneous and shared with other tasks . The system is able to adaptively reconfigure at runtime looking for the better allocation pattern (this is very well suited for cloud computing and “on demand resources”)

44. ARTÌS + GAIA As part of our research effort we have implemented a PADS middleware called ARTÌS in which the GAIA module implements the migration mechanism previously described

46. This is due to the nature of the wireless propagation and the locality of most wireless transmissions

47. In the last years we have tested the ARTÌS middleware in many case studies and simulating wireless network with different degrees of detail in the simulated model

48. In all cases the results have confirmed that it is possible to obtain a very good speed-up also using simple and general heuristics

49. In this way, massively populated networks (e.g. 1 million of nodes) using detailed MAC protocols (e.g. IEEE 802.11) can be simulated using Commodity-Off-The-Shelf (COTS) hardware

51. ( usually: 2 < α < 3 )

53. evolving networks in biology

54. transmission of diseases

55. citation graphs

57. a very large number of poorly connected nodes

58. a very small network diameter

61. huge amount of communication when simulating real-world protocols on top of such models

67. Based on the Advanced RTI System ( ARTÌS ), a middleware used to implement sequential/parallel/distributed simulations that follows an event-based approach

68. It can exploit the adaptive simulation features provided by the Generic Adaptive Interaction Architecture ( GAIA )

69. PaScaS will be soon freely available: http://pads.cs.unibo.it

75. Each LP manages the evolution of a part of the model and it is usually run by a different CPU

76. Each node in the scale-free net is modeled as a Simulated Entity (SE) , therefore each LP manages a set of SE s

89. An approach based on dynamic and adaptive clustering of the simulated entities can give valuable results

90. From the simulator to the simulation model : are gossip protocols a good choice to build dissemination mechanisms on scale-free networks?

92. mirrored servers

94. Under the scalability viewpoint the peer-to-peer approach is very promising

96. The peers are organized in some form of overlay network

97. The dissemination of game events is obtained by passing messages through the overlay

99. fully connected graph

100. random graph

101. scale-free network (?)

103. Some properties of scale-free networks (e.g. the very small diameter: d ~ ln ln n, with a network of n nodes ) are very valuable in supporting scalability and responsiveness

104. In our view, the dissemination of game events will be obtained through probabilistic approaches, for example using gossip protocols

105. The game events generated at peers are disseminated to the whole network, using very simple gossip protocols and without any form of centralization or predefined routing

106. Parameter Value number of nodes 0-500 message generation exponential distribution mean = 50 time-steps cache size (local to each node) 256 slots message Time To Live ( ttl ) 6 probability of dissemination ( v ) 0.5, 0.8, 1 (i.e. 50-80-100%) fanout value 5 (# of nodes) probability of broadcast ( p b ) 0.5, 0.8, 1 (i.e. 50-80-100%) simulated time (gaming time) 1000 time-steps (after building) Performance evaluation : simulation-based

108. Evaluation: coverage rate (%)

109. Evaluation: number of hops

110. Evaluation: total number of messages

111. Evaluation: coverage rate (%)

112. Evaluation: number of hops

113. Evaluation: total number of messages

115. Common gossip protocols are unable to disseminate the whole event trace and their overhead is very high

116. Simple mechanisms such as caching of packets, ttl and protocols tweaking are quite ineffective or with limited impact on performances ( e.g. # of routed messages )

118. adaptive protocols and behaviors

119. more information shared among network nodes

121. The benefits are not limited to wireless models: also more general environments (such as scale-free networks) can obtain very satisfactory results

123. the implementation of simulators based on this paradigm and taking advantage of cloud-computing (on-demand) resources

125. L. Bononi, M. Di Felice, G. D'Angelo, M. Bracuto, L. Donatiello. MoVES: A framework for parallel and distributed simulation of wireless vehicular ad hoc networks . ComNet Journal, Special issue on &quot;Emerging Wireless Networks: Performance Modeling and Analysis&quot;, Volume 52, Issue 1, Elsevier

126. S. Ferretti, G. D'Angelo. Multiplayer Online Games over Scale-Free Networks: a Viable Solution? Workshop on DIstributed SImulation and Online gaming (DISIO 2010)

127. G. D'Angelo, S. Ferretti. Simulation of Scale-Free Networks . Proceedings of 2nd ACM/ICST International Conference on Simulation Tools and Techniques (SIMUTools 2009)

128. M. Bracuto, G. D'Angelo. Detailed Simulation of Large-Scale Wireless Networks . Proceedings of the 11-th ACM/IEEE International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2007)

129. L. Bononi, M. Bracuto, G. D'Angelo, L. Donatiello. Exploring the Effects of Hyper-threading on Parallel Simulation . Proceedings of the 10-th ACM/IEEE International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2006)

130. L. Bononi, M. Bracuto, G. D'Angelo, L. Donatiello. An Adaptive Load Balancing Middleware for Distributed Simulation . Proceedings of Frontiers of High Performance Computing and Networking - ISPA 2006 Workshops

131. L. Bononi, G. D'Angelo, L. Donatiello. HLA-based adaptive distributed simulation of wireless mobile systems . Proceedings of the 17th ACM/IEEE/SCS Workshop on Parallel and Distributed Simulation (PADS '03)

134. Gabriele D’Angelo <gda@cs.unibo.it> http://www.cs.unibo.it/gdangelo/ Department of Computer Science University of Bologna From Simulation to Online Gaming: the need for adaptive solutions