Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration

Transcription

1 Dynac Resource Allocaton n Clouds: Sart Placeent wth Lve Mgraton Mahlouf Had Ingéneur de Recherche [email protected] Avec : Daal Zeghlache (TSP) [email protected]

2 FONDATION DE COOPERATION SCIENTIFIQUE 2

3 NOS VALEURS ❶ Excellence - Talents - Résultats - Echanges ❷ Souplesse - Fonctonneent - Partenarat - Proets ❸ Rgueur - Proprété Intellectuelle - Confdentalté - Exécuton 3

4 PROGRAMMES DE R&D Ingénere nuérque des Systèes et Coposants 4

5 PROJETS R&D Proets opératonnels depus le lanceent 9 15,5 Proets Opératonnels M Fnanceent Industrel ETP/an sur 3 ans Partenares Industrels Partenares Acadéques 29 Thèses 5

6 I- Sart Placeent n Clouds

7 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

8 Sart Placeent n Clouds French Provders Pont of Vew 8

9 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

10 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, N I y N 1, N s hosted n server I P x 10

11 Sart Placeent n Clouds Mnu Cost Maxu Flow Algorth Instance (2; 0,23) S T Legend: (capacty; cost) 11

12 Sart Placeent n Clouds Sall Instance Mnu Cost Maxu Flow Algorth (2; 0,23) Medu Instance S T (2; 0,23) 12

13 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

14 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

15 II- Lve Mgraton of VMs

16 Lve Mgraton of VMs Mgraton process: Xen ESX KVM Hyper-V 16

17 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

18 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

19 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, ,,ax 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

20 Lve Mgraton of VMs Polyhedral Approachs Nuber of used servers when tang nto account Mgratons 20

21 Convergence Te (n seconds) of Mgraton Algorth: 21

22 Lve Mgraton of VMs Polyhedral Approachs Percentage of Ganed Energy when Mgraton s Used ,55 36, ,29 34,00 35,23 38, ,48 27,39 35,21 40,32 41,89 36, ,77 18,85 22,02 32,31 39,90 40, ,86 16,17 19,85 22,30 39,20 36, ,63 14,29 18,01 22,13 25,15 30, ,10 14,00 14,86 15,90 22,91 23, ,01 10,20 10,91 15,34 17,02 21, ,80 09,32 10,31 14,70 16,97 19, ,90 07,50 08,40 12,90 16,00 14,97 22

23 Than you