1/30 Cost-effcent deployment of dstrbuted software servces csorba@tem.ntnu.no
2/30 Short ntroducton & contents Cost-effcent deployment of dstrbuted software servces Cost functons Bo-nspred decentralzed optmzaton strateges Cross-valdaton o The deployment problem MAPE notaton target envronment o Intro to CEAS o How to encode pheromones o Scenaro 1 : Collaboratng software components o Scenaro 2 : Replcaton management o Scenaro 3 : Cloud computng o Valdaton usng ILP o Summary & not covered
3/30 The deployment problem and ts complexty Focus on: - load-balancng - remote comm. - replca management - and fnancal costs #S Deployment mappng #N
4/30 In the context of the MAPE cycle o REQs 1 6 : 1. Effcency 2. Robustness 3. Autonomcty 4. Adaptvty 5. Scalablty 6. Generalty & Extensblty Deployment mappng # S #N
5/30 Target envronment
6/30 Notaton example o Constants: N = {n 1 n 2 }; D = {d 1 }; d 1 = {n 1 n 2 }; S = {S 1 }; C = {c c }; K = {k k }; o Varables: D 1 = {d 1 }; H 1 = {n 1 n 2 }; L 1 = {30 => n 1 20 => n 2 }; M 1 = {c => n 1 c => n 2 };
7/30 Introducton to CEAS Cross-Entropy Ant System o Orgnally: robust dstrbuted path management n network nodes Schoonderwoerd et al. ant based routng scheme (1997) Rubnsten Cross-Entropy for stochastc optmsaton (1999) Helvk & Wttner dstrbuted autoregressve varant (2003) o 2 types of ants: Explorer => cover up the search space.e. do random search Normal => optmze mappng o 2 phases n an teraton Forward search => ants lookng for a deployment mappng Backtrackng => ants update the pheromone database
8/30 CEAS deployment strateges how t works... n 1 n 4.? r updatetemp(cost) C = Ø? F(). n 5 n 6? Fwd. search Pheromone update select get : ml nr nr?. n 2 n 3?.
9/30 CEAS auxlary adustments o Parallel nests In onng / splttng networks o Load-samplng To facltate load-balancng and gather nformaton o Guded random hop-selecton Taboo-lstng Cover domans and then nodes o Bndng & release of nstances / mantenance Ease convergence Condtonal
10/30 CEAS n a class dagram o Implemented n Smula/DEMOS o Peersm (Java) ongong
11/30 Pheromone encodngs Yes / No o Tables dstrbuted over the possble hosts Instances
12/30 Scenaro 1 : Collaboratng software components o Deployment of collaboratng components UML 2.0 collaboratons o PAPER A C o Target o E.g. Load-balancng (.e. mn. devaton from average load) Mnmzaton of remote communcaton S 1 : a smple clent-server S 2 : authorzaton and authentcaton server
13/30 Scenaro 1 (cont d) o Deployment cost functon wth global load-balance estmate and communcaton cost
Cost / DB sze 14/30 Scenaro 1 (cont d) o Example run from PAPER A Iteratons 2 7 = max DB sze 117 = optmal cost exploraton
15/30 Scenaro 1 (cont d) o Deployment costs for multple servces Elmnatng global knowledge (~T ) Multplcatve (~better convergence) o Deployment cost functon where
16/30 Scenaro 1 (cont d) Cost o Examples wth 3 servces from PAPER C Node error & repar New node Iteratons
17/30 Scenaro 2 : Replcaton management o Deployment of replcas o PAPER D E o Target o E.g. Load-balancng Node- & doman-dsontness S 3 : a smple DB replca S 4 : 3-way replcaton
18/30 Scenaro 2 (cont d) o Deployment cost functon Doman-dsontness Node-dsontness Load-balancng where and
19/30 Scenaro 2 (cont d) o Example from PAPER E 65 replcas 11 nodes n 5 domans
20/30 Scenaro 2 (cont d) o Example from PAPER E Costs of servce S 10 2000 explorer teratons Splttng of d 1 after 4000 teratons Mergng the regons after 5000 teratons
21/30 Scenaro 3 : Cloud computng o Deployment of VM replcas n a cloud-lke envronment o PAPER F o Target Load-balancng Node- & doman-dsontness Mnmze fnancal costs 5 prvate (costs 0) 2 publc clouds (costs 1 and 10) 18 clusters 130 nodes (cluster szes of 10 or 5 nodes) 5 x 25 servces replcaton level 5 => 625 VMs
22/30 Scenaro 3 (cont d) o Deployment cost functon o where the weghtng functon s defned as o and the sum of fnancal costs s
23/30 Scenaro 3 (cont d) o Combnaton of cost functon terms n F 4
24/30 Scenaro 3 (cont d) o Results of 100 smulaton runs each settng No cluster weghts Lnear weghtng Exponental weghtng
Cost 25/30 Valdaton usng ILP o o PAPER G ILP (AMPL/CPLEX) Examples
26/30 Valdaton usng ILP (cont d) o ILP bult wth obectve: o and correspondng constrants: o wth the varables: o Cross-valdaton of exact solutons vs. heurstcs Global-vew vs. decentralzed 1 1 mn K N col f col m b T 2 1 2 1 1 1 1 ) ( ; 1 ) ( ) ; (2 ; ; ; 1; k k l l k k k l l k C C N n n c c c c k col m m n c c c c k col m m n m e T n T m e n c b m c m ~ F 1 () Impedments: global vew n memory no support for dynamcty m sze exploson
27/30 Summary o CEAS Heurstc optmzaton Decentralzed algorthm ant-lke agents o Applcaton domans Deployment of collaboratng sw components Deployment of replcas VM nstance placement n prvate and publc clusters 1) Cost functons 2) Heurstc algorthms 3) Modelng scenaros 4) Cross-valdaton
28/30 Publcatons o Category I. Valdaton of mappngs o Category II. Load-balancng and communcaton costs o Category III. Load-balancng and replcaton costs o Category IV. Cluster costs
29/30 Not covered not mplemented not yet gven up o Translaton of the smulatons to PeerSm o Addtonal ILP models o The power savng dmenson o Mgraton costs o Scalng and larger problem szes o Incremental scalng o Coordnaton and desgnated nest selecton o Better and broader servce models o Introducng clents o Costs derved from servce models o Deployment dagrams o Feedback to functonal desgn o Experments usng a mddleware platform e.g. DARM
30/30 Thank You for your attenton!* * and many thanks to Poul E. Heegaard Peter Herrmann and Hen Melng