Modeling and Analysis of 2D Service Differentiation on e-commerce Servers
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1 Modelng and Analyss of D Servce Dfferentaton on e-commerce Servers Xaobo Zhou, Unversty of Colorado, Colorado Sprng, CO zbo@cs.uccs.edu Janbn We and Cheng-Zhong Xu Wayne State Unversty, Detrot, Mchgan {bwe,czxu}@wayne.edu More nfo s avalable at
2 Outlne Servce Dfferentaton Slowdown Metrc n e-transactons -D Servce Dff Modelng Alg : Proportonal Slowdown Dff. Alg : Optmal Allocaton Algorthm Performance Evaluaton Conclusons C. Wayne State Unv. Servce Dff on e-commerce Servers
3 Servce Dfferentaton Provde dfferent QoS levels to dff. requests SD vs Best-effort, same servce to all Support clent-aware QoS adaptve applcatons Create ncentves for dfferentated prcng Protect servers from DDoS attack!! C. Wayne State Unv. Servce Dff on e-commerce Servers 3
4 Servce Dff. n Network QoS control n core swtches IntServ (995): tght control for ndvdual flows DffServ(998): loose control for classes Proportonal DffServ: relatve per-class QoS guarantee (Dovrols, et al 999) Network alone s not suffcent to support end-toend servce dff. C. Wayne State Unv. Servce Dff on e-commerce Servers 4
5 Server Perspectves for SD On a Web content hostng ste Treat clents of dfferent content provders dfferently (Almeda, et al 998) On a streamng or meda-rch ste Adapt to varous devces & access patterns (Chandra, et al 000, Zhou, et al 003) On an ndscrmnate Web ste Control behavors of aggressve clents for farness (Zhu et al 00) On an E-commerce ste Gve hgher prorty to sessons of buyers than vstors n admsson control (Cherksova, et al 00, Chen & Mohapatra 00) Accept/Reect Gradual perf. degradaton C. Wayne State Unv. Servce Dff on e-commerce Servers 5
6 D Sesson-based Servce Dff. Inter-sesson Dfferent customers exhbt varous navgaton patterns. Accordng to Menasce 00 Heavy buyer class: 5% and 56% of them completed Occasonal buyer & vstor class: 95% Intra-sesson A sesson has a number of states: browsng, search, select, check-out, etc States have dfferent probabltes to transt to the fnal buy state, and cost dfferent resource usage on the average C. Wayne State Unv. Servce Dff on e-commerce Servers 6
7 Customer Behavors (Menasce, et al 000) Heavy Buyer Occasonal Buyer C. Wayne State Unv. Servce Dff on e-commerce Servers 7
8 A -D Servce Dff. Model Inter-sesson vs Intra-sesson Each dmenson meets the basc PCF propertes: Predctablty: schedules must be consstent, ndependent of varatons of the class workloads Controllablty: controllable parameters to adust qualty factors between classes Farness: lower classes not be overcompromsed, especally when workload s hgh C. Wayne State Unv. Servce Dff on e-commerce Servers 8
9 Performance Metrc Slowdown vs Response Tme Slowdown = Queung tme/servce tme Requests have dfferent servce tme; users tend to tolerate long delays for large requests Slowdown translates more drectly to userperceved system load (Bender 88, Harchol-Balter 0, Rska 0, Zhu et al 0) C. Wayne State Unv. Servce Dff on e-commerce Servers 9
10 Proportonal Slowdown Qualty spacng between states be proportonal to ther pre-defned dfferental weghts α and β. S S S S,,,, = = α α β β α β Sesson β States n the same sesson α α3 α β C. Wayne State Unv. Servce Dff on e-commerce Servers 0
11 Assumptons m n M/M/ queues wth GPS schedulng Posson sesson arrval process of each customer class and arrval rates of dfferent classes are ndependent Posson arrval rate of requests n each state λ Requests from a customer are dependent, but Requests from dfferent customers are ndependent. Number of re-vsts are small C. Wayne State Unv. Servce Dff on e-commerce Servers
12 Processng Rate Allocaton Let d be the demanded resource by requests n state of sesson ; Denote c the allocated resource to the requests Resource constrant Slowdown of a state m n = = c C s = c λ d λ d C. Wayne State Unv. Servce Dff on e-commerce Servers
13 Alg : Proportonal-Share Alloc Obectves: S S,, α, = and = S, β α PS allocaton rate: S β ~ λ = αβλd PS allocaton guarantees PCF schedules for requests of dfferent classes. But, t may not be optmal wth respect to an overall resource utlzaton. C. Wayne State Unv. Servce Dff on e-commerce Servers 3
14 Optmzng -d Servce Dff A D resource allocaton problem Mnmze Subect to m n αβs = = m n = = c C Slowdown of a request and S > 0 s, = λ d, m m n = =, n ( C λ d ) α β λ d = =, α β λ d, C. Wayne State Unv. Servce Dff on e-commerce Servers 4
15 Propertes of Allocaton Dff. weght of a class ncreases, ts qualty factor ncreases at the cost of others. Qualty factor of a class drops wth the ncrease of ts arrval rate Protecton of DoS attack Load varaton of a hgher-weghted class causes a bgger qualty change of others. Any load shft from a hgher-weghted class to a lower-weghted class leads to an ncrease of the qualty of each class. C. Wayne State Unv. Servce Dff on e-commerce Servers 5
16 C. Wayne State Unv. Servce Dff on e-commerce Servers 6 Proportonal-sharng Property The qualty spacng of requests n each dmenson s square-root proportonal to ther pre-defned dff. weghts.,,, v v S S α α λ λ =,,,, d d S S β β =,,,, d d S S β α β α λ λ =
17 Smulaton Model TPC-W e-commerce workload C. Wayne State Unv. Servce Dff on e-commerce Servers 7
18 Smulaton Parameters Heavy Buyer vs Occasonal Buyer Ther sesson arrval rates are :9 (Menasce 00) Sesson dff. weghts are : 4, based on ther vst ratos Sx States Pay, Add-to-Cart, Select, Search, Browse, and Entry State dff. weghts are 5:4:3::: C. Wayne State Unv. Servce Dff on e-commerce Servers 8
19 System Slowdown System Slowdown no sevce dff proportonal optmal no sevce dff (expected) proportonal (expected) optmal (expected) 0% 0% 40% 60% 80% 00% Server Load (%) C. Wayne State Unv. Servce Dff on e-commerce Servers 9
20 D Servce Dff. n Slowdown Slowdown Slowdown.5 Occasonal buyer Heavy buyer 0.5 Entry state Search state Browse state Select state Add state Buy state Entry state Search state Browse state Select state Add state Buy state 0 Occasonal buyer Heavy buyer Optmal Allocaton C. Wayne State Unv. Servce Dff on e-commerce Servers 0
21 Mcroscopc Vew of Servce Dff seldom buy busy buy 8 0% 7 home search 6 browse 5 select cart 4 pay Inter-sesson SD (search state) % 0% 80% % Intra-sesson SD (class B) 80% C. Wayne State Unv. Servce Dff on e-commerce Servers
22 Long-term vew of SD Slowdown Inter-sesson servce dfferentaton seldom buy busy buy seldom buy (calculated) busy buy (calculated) Slowdown Intra-sesson servce dff home search select cart pay 0% 0% 0% 30% 40% 50% 60% 70% 80% 90% Server Load -0% 0% 30% 50% 70% 90% Server Load C. Wayne State Unv. Servce Dff on e-commerce Servers
23 Impact of Sesson Arrval Rate.5 Class A Class B Slowdown :7 :8 :9 :3 :4 :5 :6 9: 8: 7: 6: 5: 4: 3: : : : Rato of sesson arrval rate between class A and class B As the rato ncreases, the qualty spacng narrows When the rato goes up to 7:, the predctablty of nter-sesson SD becomes volated C. Wayne State Unv. Servce Dff on e-commerce Servers 3
24 Impact of Sesson Weght 3.5 Class A Class B Slowdown : 3: :3 :4 Rato of sesson weght between class A and class B 0:5 9:6 8:7 7:8 No dfferentaton volaton untl the sesson weght raton goes down to :4. The degree of volaton ncreases as the rato contnues to drop. C. Wayne State Unv. Servce Dff on e-commerce Servers 4
25 Conclusons D Servce Dfferentaton Model for e-commerce transactons Heavy Buyers vs Vstors Dfferentate Check-out from Browsng Proportonal Slowdown Slowdown versus Queung delay On-gong Work Implementaton of processng rate alloc C. Wayne State Unv. Servce Dff on e-commerce Servers 5
26 Modelng and Analyss of D Servce Dff. on e-commerce Servers Thanks More nfo s avalable at
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