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Autonomic microcell assignment in massively distributed online virtual environments

Autonomic microcell assignment in massively distributed online virtual environments. Bruno Van Den Bossche , Bart De Vleeschauwer , Tom Verdickt , Filip De Turck , Bart Dhoedt , Piet Demeester Journal of Network and Computer Applications, vol . 32, no. 6, 2009. Outline. Overview

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Autonomic microcell assignment in massively distributed online virtual environments

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  1. Autonomic microcell assignment in massively distributed online virtual environments Bruno Van Den Bossche, Bart De Vleeschauwer, Tom Verdickt, FilipDe Turck, Bart Dhoedt, Piet Demeester Journal of Network and Computer Applications, vol. 32, no. 6, 2009

  2. Outline Overview Introduction Problem description Algorithm description Simulations & Results Conclusions Reference

  3. Overview • In this paper we present a number of algorithms that determine the microcell allocation and runtime adaptations of the microcell allocation to optimize the deployment. • pro-active • reactive • The microcell approach and the algorithms are evaluated through large scale simulations with input parameters based on measurements on a prototype implementation.

  4. Introduction Microcell Hotspot Player migration Inter-server/microcell communication

  5. Problem description • To solve when using the microcell approach is finding an allocation of the microcells over the servers in such a way that the maximum load experienced by these servers is minimized. • The load that an individual microcell generates consists of a number of components. • the actions that are performed by the players • these actions should be forwarded to neighboring microcells or receive from neighboring microcells • players who move between microcells

  6. Problem description (Cont.)

  7. Problem description (Cont.) The load of each server

  8. Algorithm description Assignment a = currentAssignment; Server s = highestLoadedServer (); cellreassigned = true; while(cellreassigned){ Microcell c = highestMigration (s); Server s = migrationNeighbor (c); Assignment b = reassign (a,c,s); improvement = a.maxLoad - b.maxLoad; if(improvement > threshold) s = highestLoadedServer(); else cellreassigned = false; } Continuous Strong Locality Adjustment (CSLA)

  9. Algorithm description (Cont.) Assignment a = currentAssignment; for(Player p : monitoredplayers){ Microcell c = getMicrocell(p); Neighbors n = getNeighbors(c); Server s = c.getServer(); Assignment b = reassign(a,n,s); if(loadImprovement(a,b)) a = b; } Player Route Adjustment (PRA)

  10. Algorithm description (Cont.) Assignment a = currentassignement; for(iterations){ Assignement b = ILPSolve(a, timeLimit, nrAllowedMoves); if(b.maxLoadoa < maxLoad) a = b; else no improvement: break; } • Linear Limited Microcell Move Algorithm (LLMCM) • Using Integer Linear Programming(ILP) model • NP-hard

  11. Algorithm description (Cont.) • Locality Aware Load Shedding (LALS) • The basic principle of this algorithm is to shed cells from overloaded servers.

  12. Algorithm description (Cont.)

  13. Simulations & Results MASON

  14. Simulations & Results (Cont.)

  15. Simulations & Results (Cont.)

  16. Simulations & Results (Cont.)

  17. Conclusions We introduce a number of algorithms to autonomic optimize the microcell assignment. The obtained simulation results show that proactive continuous algorithms perform significantly better than reactive algorithms but at the cost of extra microcell reassignments and thus an additional runtime overhead.

  18. Reference Bruno Van Den Bossche, Bart De Vleeschauwer, Tom Verdickt, Filip De Turck, Bart Dhoedt, and Piet Demeester. 2009. Autonomic microcell assignment in massively distributed online virtual environments. J. Netw. Comput. Appl. 32, 6 (November 2009), 1242-1256.

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