Admission Control and Dynamic Adaptation for a Proportional-Delay DiffServ-Enabled Web Server

1. Admission Control and Dynamic Adaptation for a Proportional-Delay DiffServ-Enabled Web Server Yu Cai

2. Paper • Admission Control and Dynamic Adaptation for a Proportional-Delay DiffServ-Enabled Web Server • Sam C. M. Lee, John C. S. Lui, David K. Y. Yau • SIGMETRICS 2002

3. Why this paper? • Information fusion in distributed environment. • Data transmission from multiple sources with QoS requirements. • Then come to real data fusion techniques. • Data transmission is related to our specialties and my thesis. • Paper last week: video stream • a carefully designed scheme to define the data transmission unit: avoid overhead and provide flexibility. • data transmission through multiple path: algorithm

4. Summary of this paper • A web server that can provide proportional-delay differentiated services to clients with different QoS requirements. • 3 classes - 1, 2, 3 • average waiting time of class 1 W1=1.4 * W2 • W2=1.4 * W3 • Real world example: an e-commerce site: general browsing - 1; client in the process of buying - 2; client check out - 3

5. Summary of this paper • Admission control and class assignment • 6 clients : a,b,c,d,e,f,g • with maximum traffic rate (work load) λmax • with maximum average waiting time (QoS) Wmax • Admit clients to different classes and meet the QoS requirements • class vector C={1,2,1,1,3,1}

6. Summary of this paper • Dynamic class adaptation • Previous step is based on the maximum client workload λmax • Dynamically adjust the service classes based on the client real traffic condition • C={1,2,1,1,3,1} => C={1,2,1,1,2,1} • Goal: admit client to the lowest possible class while meeting the QoS requirements • The higher class is, the higher price to pay

7. Formulation • 3 classes: 1,2,3 • W1=1.4 * W2; W2=1.4 * W3 • 6 clients: a,b,c,d,e,f,g • with λmax, Wmax • Class vector C={1,2,1,1,3,1} • Considerations when admit a client • Is it possible to admit the client with the QoS? • All other admitted clients need to keep the QoS • Assign lowest classes if possible

8. Formulation • Based on conservation law: • λ1* W1 +λ2* W2 +λ3* W3 = (λ1 + λ2 + λ3)*W • W is measurable, λ1, λ2, λ3 is measurable. • W1 = 1.4 * W2 ; W3 = 1/1.4 * W2 • λ1* 1.4 * W2 +λ2* W2 +λ3 / 1.4 * W2 = (λ1 + λ2 + λ3)*W • So W2 = (λ1 + λ2 + λ3)*W / (λ1* 1.4 +λ2+λ3 / 1.4) • similarly W1 and W3 • Out goal is to maximize : • V = 1/ W1 + 1/ W2 + 1/ W3 • Same as minimize the sum of Wi

9. Original formula

10. Admission control algorithms • Maximum Profit Algorithm (MPA) • Rationale: • two clients i and j with requirements of Wmaxi and Wmaxj • Wmaxi < Wmaxj , • client i is willing to pay a higher usage cost than client j so as to receive better service. • By admitting client i, the service provider may obtain a higher profit. • admit a client who has a more stringent maximum average waiting time requirement first.

11. MPA • Sort Wa<=Wb<=Wc<=Wd<=We<=Wf • For i=1 to 6{ • Assign client i in class 1 • While{ • Compute delay of all admitted clients • If all admitted clients meet QoS, admit client i, quit while • If some admitted clients don’t meet QoS • If anyone of them is in the lowest class N, then • stop admitting client i, quit while//impossible to admit • Else • increase all their classes by 1//update class • }//end while • }//end for

12. MPA analysis • In essence, MPA is a greedy algorithm. Therefore it can only give local optimal. It likely will not achieve the global optimal. • The authors kind of mislead the readers by making a vague and complicated definition: “MPA can always give a minimum feasible admitted class vector” • Sounds like global optimal, but still local optimal • C= {2,2,1,1,3,1}; C1={1,2,1,1,3,1}; C2={2,1,1,1,3,1} • MPA knows C1 and C2 are better than C, but can not decide which is better between C1 and C2

13. MPA analysis • MPA updates all the unsatisfied clients’ class by 1, which adjust too much. • C1={1,2,1,1,3,1} => C2={2,3,1,1,3,2}; • But probably C3={2,2,1,1,3,2} is enough, or perhaps C4={3,1,1,1,3,2} is better

14. Maximum Admission Algorithm (MAA) • Rationale: • Admit as many clients as possible to popularize the site • Admit clients with larger maximum average waiting time first, because they consume less resources. • Still greedy algorithm, same local optimal problem and adjust too much problem.

15. Dynamic Class Adaptation • Why • AC is based on the maximum client workload λmax • Need to dynamically adjust the service classes based on the real traffic condition • Measurement window: a period of time for web server to estimate the client arrival rate. • Assumption: no major change in the future workload.

16. SBDA • Centralized Approach: Server-Based Dynamic Adaptation (SBDA) • Server measures the client arrival rate λ • Server use λ as λmax to calculate the class vector • Server send this vector info to clients • Client then get the upper bound of the class to choose from. Client needs to choose the lowest possible class • Say, client b get class 2 assigned from server, it means if client choose class 2, the server guarantee the QoS; so the client needs to decide if it can go lower to class 1; if yes, use class 1, if no, use class 2.

17. CBDA • SBDA drawbacks • Computation complexity • Communication overhead: unicast • Distributed & Game-Theoretic Approach: Client-Based Dynamic Adaptation (CBDA) • Server measure and estimate the arrival rate for each class • Server multicast the information • Client can determine its lowest class based on the received information and its own arrival rate information

18. CBDA • In essence, this is a non-cooperative game problem in which distributed optimization is performed by each client • Guaranteed termination: upper bound is given in Admission control. • Low computational complexity: client can even ignore this step.

19. My questions • A typical client traffic pattern is like this: • 80 % is λmax , 10 % is 0.9 * λmax , 10 % is 0.8 * λmax • Is this a realistic traffic pattern? • If traffic changes too much, the result seems to get bad. • Algorithm improvement. Greedy? adjust too much? • No simulation or analysis on how much the MPA and MAA is different from the optimal result

20. For our project • Multiple data sources with different QoS and different data amount/load, • Server side has a fixed data fusion ability • how to admit and classify the data into different classes based on the data load and server capacity while keeping the QoS?

21. Example