1. Adrian Daniel Popescu, Mohamed A. Sharaf, Cristiana Amza MDM 2009 SLA-Aware Adaptive On-Demand Data Broadcasting in Wireless Environments 1

2. Outline Introduction Motivation System model Tardiness-aware scheduling -- SAAB-T Utility-aware scheduling -- SAAB-U Experiments Conclusions 2

3. Introduction in a dynamic mobile environment motivated us to use Service Level Agreements (SLAs) where a user specifies the utility of data as a function of its arrival time. SLAs provide users with the flexibility to define the utility of delayed data Broadcasting Pull-based(on-demand) Push-based 3

4. Introduction On-demand data broadcasting More scable users submit requests for data items of interest and the broadcast server aggregates requests for the same data item and broadcasts it only once. If a data item is highly popular, then broadcasting that data item to all interested users substantially reduces the number of transmissions. 4

5. Motivation optimizing response time is not sufficient to maximize data usability since it overlooks the user’s requirements and expectations SLA-aware adaptive data broadcast (SAAB) scheduling policy for maximizing the system utility under SLA-based performance measures minimizing response time or drop rate 5

6. System model on-demand data broadcasting environment 6

7. System model Request 1) Data Item (Ii): which is the data item corresponding to request Ri, 2) Arrival Time (Ai): which is the point of time where request Ri is issued, and 3) Deadline (Di): which is the soft deadline associated with request Ri. 7

8. System model Scheduling Queue 1) Service Time (C j ): is the time required for transmitting data item I j on the downlink channel 2) Popularity (P j ): is the number of pending requests for data item I j, and 3) Requests Vector (R j ): is a requests vector of length P j, where each entry in R j corresponds to one of the P j pending requests for I j (i.e., R j = R j1 R j 2 … R j P j ) 8

9. Tardiness-aware scheduling -- SAAB-T 9 there is some pending request R x for that data item I x with deadline D x Slack : assume two data items I 1 and I 2 schedule of choice (1)X I 1 first,then I 2 (2)Y I 2 first,then I 1

10. Tardiness-aware scheduling -- SAAB-T 10 Under schedule X Assume that the number of these requests is P 1,def, where 0≤ P 1,def ≤ P 1. The sum of all requests which currently have a negative slack, S 1,j tardiness of requests for item I 1

11. Tardiness-aware scheduling -- SAAB-T 11 Assume that the number of these requests is P 2,def, where 0≤ P 2,def + P 2,add ≤ P 2 The sum of all requests which currently have a negative slack, S 2,j Pending requests to I 2 had positive slack when the scheduling decision was made but that slack became negative tardiness of requests for item I 2

12. Tardiness-aware scheduling -- SAAB-T 12 total tardiness under schedule X and Y

13. Tardiness-aware scheduling -- SAAB-T 13 Example: C 1 = 5, C 2 = 10 2R21R22R23 Deadline7513 Negative slack-3-53 add2 1R11R12R13 Deadline3818 Negative slack-2313 add7 T 1y = -(-3-5)=8 T 2y =-(-2)+(10-3) =9 Ty = 17 T 1x = -(-2)=2 T 2x =-(-3-5)+(5-3) =10 Tx = 12

14. Tardiness-aware scheduling -- SAAB-T 14 If Tx < Ty, priority:

15. Utility-aware scheduling -- SAAB-U SLA -- Utility functionSAAB-U -- Utility function 15

16. Utility-aware scheduling -- SAAB-U 16 each request for I 2 is delayed by the amount of time needed to transmit I 1 general priority function

17. Experiments 17 SAAB-T Slack factor (SF) SJF = 1/Ci high load EDF = 1/Di low load MRF = Pi W-SJF = Pi/Ci

18. Experiments 18 SAAB-U

19. Conclusions 19 This request aggregation is efficient since it allows for fewer data broadcasts which saves bandwidth and reduces delays. minimizing response time, or drop rate SAAB is highly sensitive to workload conditions