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Combining State and Model-based approaches for Mobile Agent Load Balancing

Combining State and Model-based approaches for Mobile Agent Load Balancing. Georgousopoulos Christos. Omer F. Rana. http://www.cs.cf.ac.uk/Digital-Library/. Load balancing overview. Load balancing overview. Aim : improve the average utilization and performance

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Combining State and Model-based approaches for Mobile Agent Load Balancing

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  1. Combining State and Model-based approaches for Mobile Agent Load Balancing Georgousopoulos Christos Omer F. Rana http://www.cs.cf.ac.uk/Digital-Library/

  2. Load balancing overview Load balancing overview Aim: improve the average utilization and performance of tasks on available servers Kinds of Load Balance (LB): Load balance Keren & Barak: mobile LB has a 30-40% improvement over the static placement scheme mobile static model state • only a price • sophistiated auction protocols • a pricing mechanism without • any negotiation Market mechanism Specialized agents gather System state information • roam through the network • bid for resources

  3. Our approach on LB Our approach on LB • Provide a LB mechanism to evenly distribute agent tasks among the available servers (i.e. equitably server the agents, there are no priorities between agents based on the time needed for their task to be accomplished) • We propose a LB mechanism based on a combination of the model-based and state-based approaches (i.e. decisions on LB are based upon a model which adapts due to the information gathered from the state-based approach) • We demonstrate this approach for a MAS operating on an active digital library composed of multi-spectral images of the Earth as part of the Synthetic Aperture Radar Atlas (SARA)

  4. The SARA LB mechanism The SARA LB mechanism • State-based approach (1/4) The management agents in the SARA architecture (2/4) Distribution of information among the management agents (3/4) Information maintained by management agents (4/4) Communication between management agents • Model-based approach (1/1)LB decision model

  5. The SARA architecture The SARA architecture

  6. (1/4) The management agents in the SARA architecture (1/4) The management agents in the SARA architecture The SARA architecture The SARA architecture (state-based approach) (state-based approach) • A management agent exists for every server • Their common objective: optimize system performance web server  UMA (Universal Management Agent) i) optimize mobile agents’ itinerary ii) avoid unnecessary migrations iii) identification & comparison of agent task Info. server  LMA (Local Management Agent) i) inform mobile agents for updates • Why multiple management agents ? i) no central point of failure ii) over a centralized scheme: as the number of agents increase, the network load is increased • LB decisions are supported through the management agents

  7. Advantages of having management agents control over LB decisions Advantages of having management agents control over LB decisions • Minimization of information transmitted over the network (i.e. only 2 messages are exchanged between a mobile agent and a management agent: the agent’s requirements & the agent’s itinerary ) • Minimization of the mobile agent’s size (i.e. the decision support algorithm is within the management agents. Alternatively mobile agents would have to carry it during their migration) • System optimization Information used for LB decisions may also be reused for: i) undertaking similarity analysis between agent requests i.e. tasks ii) cache techniques are possible to be applied iii) lay the foundations for an efficient monitoring system

  8. (2/4) Distribution of information among the management agents (2/4) Distribution of information among the management agents (state-based approach) (state-based approach) • centralized scheme :a global database is used to hold all information for each server • agent interactions stored in one location • information: network overload increases • in a case of a failure • distributed scheme :information is distributed among the servers no central point of failure i) global network map • each server has all the information: replication (for integrity) (provides all information for each server) network overload decreases ii) map of the surrounding area (provides information for the local server but information is reduced more and more for servers which are not in the local region) • impose agents to have a kind of intelligence iii) neighbor map (provides information for the local server and its neighbor servers only)

  9. (3/4) Information maintained by management agents (3/4) Information maintained by management agents (state-based approach) (state-based approach) SARA LB uses the global network map for decentralized information distribution with a slight variation …

  10. (4/4) Communication between management agents (4/4) Communication between management agents (state-based approach) (state-based approach)

  11. LB decision model LB decision model (model-based approach) (model-based approach) • LB decisions are based on a model which accepts as: input: an agent’s requirements & System state information output: the appropriate server where an agent should migrate to • The model is a function of: i) agents’ tasks ii) servers’ utilization (performance load) iii) availability of resources iv) network efficiency

  12. LB decision model LB decision model (model-based approach) (model-based approach) The model may be better expressed with reference to the agents’ task…

  13. LB decision model LB decision model (model-based approach) (model-based approach) examples of different agents’ tasks… case 5: Agent’s task  Not similar (not chased)  Do not need filtering case 3: Agent’s task  Similar (cashed)  Exactly the same  Need filtering  Custom filter +Ts where: Tav = the average time an agent needs to complete a task (regarding all servers) Uav = the average utilization of all servers Us = the utilization of a server Sa.code= the file-size of an agent’s code. B2 = the bandwidth between 2 information servers Τs = time needed for a server to became available utilization of a server where: a = the number of agents on that server μ = the average task time of the agents L = the processing power of the server

  14. LB decision model LB decision model (model-based approach) (model-based approach) Mathematica simulation of Case 5 Agent’s task  Not similar (not chased)  Do not need filtering

  15. Advantages of the proposed LB technique Advantages of the proposed LB technique • More accurate LB decisions (LB model uses the state-based information) • LB decisions are supported by the management agents • Distribution of information between the management agents (the variation of the global network map decentralizedinformation distribution implies reduction of information replication)

  16. Conclusion – Future work Conclusion – Future work • we demonstrated a combination of the state and model-based approaches for mobile agent load balancing were specializedstationary agentsare used togather system state informationandmake decisions on the distribution of mobile agents among the servers, based on amodelofprobabilistic estimationsin relation with the informationprovided by the stationary agents • implement the proposed LB technique… • … to optimize the intelligence of the management agents

  17. The End The End

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