Fault tolerance in cloud technologies presented as a service

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1 Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce

2 INTRODUCTION Improvements n technques for vrtualzaton and performance n cloud networkng technologes over the last decade have made t possble to acheve greater abstracton n the form of cloud computng. Cloud can be defned as a framework to delver servces for varous knds of resources lke computng, software, storage etc. over the global network - Internet.

3 RELEVANCE OF THE PROBLEM Fault tolerance of falures n cloud computng s one of the man concerns of users and provders of ths type of modern servces. Today publc clouds provde relablty and good connectvty. Let's take the example of a global ste whch we all know ths s EBAY.com. Today UPTIME on ths ste s 99.95%, whle 6-9 years ago ths percentage was 99.1%.

4 RELEVANCE OF THE PROBLEM In ths scentfc work, we ntroduce the concept of fault tolerance as a servce based on user requrements and the agreement between the provder - user. Through ths model tenant cloud servce can choose dfferent specfcatons of the huge base, as a place to be located vrtual machne, type of connecton, speed of delvery of nformaton, and many other parameters.

5 BASIC MODEL Let T = {T1, T2, T3 Tp} be a set of p tenants. The FTaaS has a plannng span of τ slots over tme duraton of T, each tme slot of the same duraton. The cost assocated wth each of the two fault tolerance methods are as follows: C C C SS VM cp prce for storage for each clent prce for each vtual machne prce a tenant has to pay for each nvocaton of a coordnated check pontng

6 BASIC MODEL Let V be the maxmum number of Vrtual Machnes that can be provsoned and let S be the maxmum amount of storage [4] that s avalable for storng checkponts. In the SLA for tenant T s represented: N< N, S, t, c, R > Where, sla sla N - checkpont nterfal functon of the tenant T; S - sze of storrage for tenant T; sla t - functon of TMR (0,1); sla c - functon of SLA (0,1)

7 BASIC MODEL Tenant can request to be served ether by TMR or COORD as fault tolerance [5]. Hence, sla sla c ( k) t ( k) 1 C C sla ( k) 1 ( k) 0 sla Smlarly, when the tenant wants to be served n approprate Example of fault tolerance SLA Below s an example of fault tolerance provded by the two tenants of cloud servces. It s planned to test for a day - a total of 24 hours. Ths whole day s dvded nto 24 slots each lastng one hour.

8 EXAMPLE OF FAULT TOLERANCE SLA Ths whole day s dvded nto 24 slots each lastng one hour. Slot Fault Tolerance method Chekpont nterval (N) Crtcal Yes or No (R) 1-4 COORD 0,25 hr N 5-9 COORD 0,5 hr N TMR - N COORD 0,5 hr N 23 TMR - Y 24 COORD 0,5 hr N Checkpont sze 2 mb Tab. 1

9 EXAMPLE OF FAULT TOLERANCE SLA Slot Fault Tolerance method Chekpont nterval (N) Crytcal (R) 1-4 COORD 0,5 hr N 5-8 TMR - N 9-12 COORD 0,5 hr N TMR - N COORD 0,5 hr Y TMR - Y Checkpont sze 4 mb Tab. 2

10 EXAMPLE OF FAULT TOLERANCE SLA Slot 23 s marked as crtcal both for tenants and servce provders, so that should ether be dened or to add a new vrtual machne from the servce provder. Tables 1 and 2 show the test results there of overnght at two dfferent clents. If the suppler FTaaS has a maxmum capacty of 4 vrtual machnes, t wll not be able to meet the requrements of both clent slots 13, 14 and 23

11 DEFINE OF THE PROBLEM Consderng the above results can be clearly get an dea what s the man role of FTaaS. The provder has n number of vrtual machnes, and space, whch s known n advance, the man task of FTaaS s to analyse whether t s possble to meet the re-qurements for fault tolerance for all tenants. Let's look at the man problems of the global Internet network n terms of denal of servces. Let get followng varables: At any slot, the total number of VMs cannot be more Than what the FTaaS provder can provson. The TMR mode requres 3 VMs for each FTVM and COORD mode requres 1 VM for a FTVM.

12 DEFINE OF THE PROBLEM Therefore, p sla sla (3t ( k) c (k)).y V : 1 k 1 At any slot, the total amount of stable storage cannot exceed the amount of stable storage avalable wth the FTaaS (S) provder. The TMR mode does not requre any Storage [6]. However each FTVM n COORD mode requre stable storage commensurate to ts checkpont sze (S).

13 DEFINE OF THE PROBLEM p 1 sla ( S.c (k)).y S : 1 k In order to maxmze the gan, the solver wll try to select a set of tenants such that the combned expresson of gan above s maxmzed, whle all other constrants are honored.

14 Results Presented are the results based on smulated data usng IBM log cplex optmzer [8]. We have 20 the clent 24 wth the slot length n a day,.e., 24 hours. Requrng vendors of servces s not lmted by the methods of fault tolerance range from 10 to 100, and the cost of vrtual machnes from 20 to 200. For our experment we consdered that the prce for TMR fault tolerance method s more expensve than the COORD.

15 Results Fg. 1 Revenues generated aganst VM (V) Fg. 2 Revenues generated data storage (S) V for S = 29for S=52

16 Results Fg. 1 shows total revenues correspondng to V wth a fxed value S, as prepared by formulatng ILP. As expected, the more revenue may be obtaned wth greater separaton of the vrtual machnes. However, when the maxmum possble tenants more revenue saturated fatty acds. As expected, revenue generated wth the help of our model s more than the revenue generated by the smple model because the crmnal model has strategc flexblty for more tenants for a vrtual machne. However, when a provder has suffcently large number of vrtual machnes on hand, the strategc advantage decreases. Fg. 2 shows the total revenue, correspondng to an S wth a fxed value V, as obtaned by formulatng ILP. As expected, the more revenue s generated wth a stable storage for both methods. However, the crmnal model generates more revenue than the smplfed model, thanks to strategc flexblty.

17 CONCLUSION In ths report we presented SLA soluton for fault tolerance n cloud computng. Convertng fault tolerance concept and presented as a servce soluton to customers potentally would be much more effcent and flexble n ts use n modern communcaton envronments. Wth the expanson of computer servces and the growng trend of cloud computng we beleve that such a decson would have produced promsng results n shapng the busness needs n the cloud computng.

18 REFERENCES [1] M. Al-Fares, A. Loukssas, and A. Vahdat, A scalable, commodty data center network archtecture, n Proceedngs of the ACM SIGCOMM 2008 conference on Data communcaton. ACM, 2008, pp [2] J. von Neumann, Probablstc logcs and the synthess of relable organsms from unrelable components, Automata Studes, pp , [3] B. Cully, G. Lefebvre, D. Meyer, M. Feeley, N. Hutchnson, and A. Warfeld, Remus: hgh avalablty va asynchronous vrtual machne replcaton, n 5th USENIX Symp. on Networked Systems Desgn and Implementaton, ser. NSDI. USENIX Assocaton, 2008, pp perodcally [4] Hp chlled-water performance optmzed data centers 20c and 40c, [Onlne]. Avalable: com/v2/getpdf.aspx/4aa3-0122enw.pdf

19 REFERENCES [5] C. Guo, H. Wu, K. Tan, L. Sh, Y. Zhang, and S. Lu DCell: A scalable and fault-tolerant network structure for data centers, ACM SIGCOMM Computer Communcaton Revew, vol. 38, no. 4, pp , [6] M. Al-Fares, S. Radhakrshnan, B. Raghavan, N. Huang, and A. Vahdat, Hedera: Dynamc flow schedulng for data center networks, n Proceedngs of the 7th USENIX conference on Networked systems desgn and mplementaton. USENIX Assocaton, 2010, p. 19. [7] Y. Tamura. (2008, Jun) Kemar: Vrtual Machne Synchronzaton for Fault Tolerance usng DomT. [8] IBM log cplex optmzer, software/n/ntegraton/optmzaton/cplex/.

20 Thank you! For contact:

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