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Expanding the CASE Framework to Facilitate Load Balancing of Social Network Simulations Amara Keller, Martin Kelly, Aaron Todd.

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Presentation on theme: "Expanding the CASE Framework to Facilitate Load Balancing of Social Network Simulations Amara Keller, Martin Kelly, Aaron Todd."— Presentation transcript:

1 Expanding the CASE Framework to Facilitate Load Balancing of Social Network Simulations Amara Keller, Martin Kelly, Aaron Todd

2 Unbalanced Loads

3 Introduction Spatial Simulations (previous work) – Each agent has a location in space at all times Ex. Traffic flow, migration patterns – Use kd trees to assign slaves to regions of space to distribute work

4 Introduction Social Simulations (our focus) – 0-dimensional simulations are not spatial in nature Ex. Social networks like Facebook – No concern about physical locations Give each agent an ID – How to distribute work??? Load balancing could depend on simulation Create load balancing heuristic for ALL simulations

5 Things that need to happen first… Increase usability Add ability to do distributed computation

6 GOALS!!! Create a system for load balancing 0- dimensional simulations of social networks

7 CASE Usability Create an easier to use system of simulation configuration Create a visual client to manipulate simulation configuration once configuration mechanism is in place Create a configuration file for the visual client that would tell the client necessary information for it to initialize a simulation Create a standardized system to log simulation data while simulation is running Allow CASE to import GIS data

8 Distributed Computing Must be able to use multiple machines. All load balancing research is IMPOSSIBLE until this is implemented…

9 Load balancing of 0-dimensional Social Simulations Relatively unexplored Load balancing of spatial simulations has been done… but kd tress have been used to split spatial regions Social simulations are constantly changing – Agents are not available by location – Friends are changing Must partition agents in a “good” way, even while simulation is running

10 Proposed Solutions Usability – CASE will load XML configuration files – Import GIS data into CASE Distributed computing – Scala uses Java’s RMI libraries  super important! Load balancing of social simulations

11 1.Centralized: use properties of the graph 2.Slave level: each one adjusts its load according to the work its doing

12 Load balancing of social simulations Both approaches have same goal – Equal processor load on each slave – Maximal intraslave communication – Minimized interslave communication Both attempt to partition the graph into clusters and distribute clusters – Ideal clusters have a large amount of interconnection and few connections to other clusters Ex. clique

13 Partitioning approaches Centralized approaches – Adapt spatial method of recursive bisection partitioning to a non-spatial graph – Partition based on minimum cuts in the graph Implementation of Ford-Fulkerson method – Simulation starts Use bottom-up approach- look for agents to move – Shift cut line with algorithm

14 Partitioning approaches Slave-based approaches – No central organization – Slaves figure out when they are over or under worked – Agents can have the ability to respond to both network and processor load – Requires slaves to have a good heuristic to govern how load should be passed around – Have slave store a record of how much “work” an agent has been doing and what slaves they communicate with most

15 Partitioning Approaches Shadowing – Agents can be in multiple places at once Single agent receives messages from every agent in the simulation but spends very little time processing these messages – Allows the agent to exist in two places until it chooses the optimal one

16 Proposed Experiment Domain Create a set of test simulations with knowledge of optimal distribution Test using small and large amounts of agents Create a set of test simulations where optimal distribution is unknown and attempt to understand distribution – All agents on one slave – Create a static social network of clique grouped agents equal to number of slaves – Change cliques

17 Proposed Timeline Initial Tasks – Continue literature review – Improve core functionality of CASE Intermediate Tasks – Explore approaches to load balancing problem Final Tasks – Test and tweak selected load balancing algorithm Wrap Up – Finalize the test set and prepare final paper and presentation

18 Acknowledgements Trinity University Department of Computer Science Advisor: Dr. Mark Lewis For being awesome and feeding us snacks: Becky and Rosie!


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