Dynac Resource Allocaton n Clouds: Sart Placeent wth Lve Mgraton Mahlouf Had Ingéneur de Recherche ahlouf.had@rt-systex.fr Avec : Daal Zeghlache (TSP) daal.zeghlache@teleco-sudpars.eu
FONDATION DE COOPERATION SCIENTIFIQUE 2
NOS VALEURS ❶ Excellence - Talents - Résultats - Echanges ❷ Souplesse - Fonctonneent - Partenarat - Proets ❸ Rgueur - Proprété Intellectuelle - Confdentalté - Exécuton 3
PROGRAMMES DE R&D Ingénere nuérque des Systèes et Coposants 4
PROJETS R&D Proets opératonnels depus le lanceent 9 15,5 Proets Opératonnels M Fnanceent Industrel 110 42 12 ETP/an sur 3 ans Partenares Industrels Partenares Acadéques 29 Thèses 5
I- Sart Placeent n Clouds
Sart Placeent n Clouds VM Placeent proble Proble: Based on allocatng and hostng N VMs on a physcal nfrastructure of X Serveurs, what s the best anner to optally place worloads to nze dfferent nfrastructure costs? VMs deand anageent Cloud End-Users ESX 1 ESX 2 ESX N Non optal placeent VMs placeent strategy??? Optal placeent ESX 1 ESX 2 ESX N ESX 1 ESX 2 ESX N Benefts Resources optzaton, Mnzaton of nfrastructure costs, Energy consupton optzaton. Challenges of the proble: Exponental nuber of cases to enuerate. Infrastructure servers Defne the best strategy to place VMs worloads leadng to optally reduce nfrastructure costs. 7
Sart Placeent n Clouds French Provders Pont of Vew 8
Sart Placeent n Clouds Due to fluctuatons n users deands, we use Auto-Regressve (AR()) process, to handle wth future deands: d t 1 d t t Geston de la deande de VM sall Forcastng & Schedulng Utlsateurs des servces Cloud large ESX 1 ESX 2 ESX N Proble Coplexty : NP-Hard Proble: One can construct easly a plynoal reducton fro the NP-Hard notary proble of the Bn- Pacng. Infrastructure serveurs 9
Sart Placeent n Clouds Matheatcal forulaton: Forulaton as ILP: The correspondng atheatcal odel s an Integer Lnear Prograng: dffcultes to characterze the convex hull of the consdered proble and the optal soluton. n Z 1 d Subect To: x x N y x C y, I N,, 1f VM 0 else., I, 1 1 1 1 N I y N 1, N s hosted n server I P x 10
Sart Placeent n Clouds Mnu Cost Maxu Flow Algorth Instance (2; 0,23) S T Legend: (capacty; cost) 11
Sart Placeent n Clouds Sall Instance Mnu Cost Maxu Flow Algorth (2; 0,23) Medu Instance S T (2; 0,23) 12
Sart Placeent n Clouds Sulaton Tests: Case of (0;1) Rando Costs Rando Hostng Costs Scenaro We consder (0; 1) Rando hostng costs between each couple of vertces (a, b), where a s a fctf node, and b s a physcal achne (server). 13
Inverse Hostng Costs Scenaro Sart Placeent n Clouds Sulatons Tests: Case of Inverse Hostng Costs: We consder nversed hostng costs functon between each couple of vertces (a, b), where a s a fctf node, and b s a physcal achne: g 1 f C ab ab ab f ( Cab) Where C represents the avalable capacty on the consdered arc. est une foncton non nulle. ab f 0, otherwse g 14
II- Lve Mgraton of VMs
Lve Mgraton of VMs Mgraton process: Xen ESX KVM Hyper-V 16
Lve Mgraton of VMs Mn Polytops, faces and facets c x Subect To constrants: a xb, 1,..., 0,1, 1 n x,..., x 1 1 Polyhedral Approachs face x* facets x 2 2 20
Lve Mgraton of VMs Soe Vald Inequaltes of our Proble: Polyhedral Approachs Decson Varables: Z 1 Prevent bacword graton of a VM: f A VM s grated fro to (0 else). Z Z l 1 Server s destnaton unqueness of a VM graton: Z 1, 1 Servers power consupton ltaton constrants: Etc q 1 1 p z p p 1 y,ax, current 18
Lve Mgraton of VMs Polyhedral Approachs 19 0 otherwse dle s server 1f 0 otherwse to, fro grated s VM 1f 1 1,, 1,, 1,, 1,,,, 1, 1, Subect To: ax 0 1 ax, 1, 1 1 1 1,,ax 1, 1 1 1 1, y z T t z P P y y q z y P P z p z l l q q z z z p y P M current q q current l q dl
Lve Mgraton of VMs Polyhedral Approachs Nuber of used servers when tang nto account Mgratons 20
Convergence Te (n seconds) of Mgraton Algorth: 21
Lve Mgraton of VMs Polyhedral Approachs Percentage of Ganed Energy when Mgraton s Used 5 10 15 20 25 30 10 35,55 36,59 00 00 00 00 20 27,29 34,00 35,23 38,50 00 00 30 17,48 27,39 35,21 40,32 41,89 36,65 40 16,77 18,85 22,02 32,31 39,90 40,50 50 10,86 16,17 19,85 22,30 39,20 36,52 60 08,63 14,29 18,01 22,13 25,15 30,68 70 08,10 14,00 14,86 15,90 22,91 23,20 80 07,01 10,20 10,91 15,34 17,02 21,60 90 06,80 09,32 10,31 14,70 16,97 19,20 100 05,90 07,50 08,40 12,90 16,00 14,97 22
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