adrian daniel popescu, mohamed a. sharaf, cristiana amza mdm 2009 sla-aware adaptive on-demand data...
TRANSCRIPT
Adrian Daniel Popescu, Mohamed A. Sharaf , Cristiana Amza
MDM 2009
SLA-Aware Adaptive On-Demand Data
Broadcasting in Wireless Environments
1
OutlineIntroduction
Motivation
System model
Tardiness-aware scheduling -- SAAB-T
Utility-aware scheduling -- SAAB-U
Experiments
Conclusions
2
Introductionin 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
IntroductionOn-demand data broadcasting
More scableusers 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
Motivationoptimizing 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
System modelon-demand data broadcasting environment
6
System modelRequest1) 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, and3) Deadline (Di): which is the soft deadline
associated with request Ri.
7
System modelScheduling Queue1) Service Time (Cj ): is the time required
for transmitting data item Ij on the downlink channel
2) Popularity (Pj ): is the number of pending requests for data item Ij , and
3) Requests Vector (Rj ): is a requests vector of length Pj , where each entry in Rj corresponds to one of the Pj pending requests for Ij (i.e., Rj = Rj1 Rj2 … RjPj )
8
Tardiness-aware scheduling -- SAAB-T
9
there is some pending request Rx for that data item Ix with deadline Dx
Slack :
assume two data items I1 and I2schedule of choice (1)X I1 first,then I2
(2)Y I2 first,then I1
)( xxx CtDS
Tardiness-aware scheduling -- SAAB-T
10
Under schedule XAssume that the number of these requests
is P1,def , where 0≤ P1,def ≤ P1.The sum of all requests which currently
have a negative slack, S1,j
tardiness of requests for item I1
defP
jjX ST
,1
)( ,1,1
Tardiness-aware scheduling -- SAAB-T
11
Assume that the number of these requests is P2,def , where 0≤ P2,def + P2,add ≤ P2
The sum of all requests which currently have a negative slack, S2,j
Pending requests to I2 had positive slack when the scheduling decision was made but that slack became negative
tardiness of requests for item I2
adddef PP
jjX SCT
,2,2
)( ,21,2
Tardiness-aware scheduling -- SAAB-T
12
adddefdef PP
jj
P
jjX SCST
,2,2,1
)()( ,21,1
adddefdef PP
jj
P
jjY SCST
,1,1,2
)()( ,12,2
total tardiness under schedule X and Y
Tardiness-aware scheduling -- SAAB-T
13
Example:C1 = 5, C2 = 10
2 R21 R22 R23
Deadline 7 5 13
Negative slack
-3 -5 3
add 2
1 R11 R12 R13
Deadline 3 8 18
Negative slack
-2 3 13
add 7
T1y = -(-3-5)=8T2y=-(-2)+(10-3) =9Ty = 17
T1x = -(-2)=2T2x=-(-3-5)+(5-3) =10Tx = 12
)( xxx CtDS
Tardiness-aware scheduling -- SAAB-T
14
If Tx < Ty,
priority:
)(1 ,
,,,
addiP
j
jiaddidefi
iri
C
SPP
CP
))1(
(1 ,
,,,
addiP
j
jiaddidefi
iri
Cn
SPP
CP
)(1
)(1 ,2,1
1
,2,2,2
22
,1,1,1
1 addadd P
j
jadddef
P
j
jadddef C
SPP
CC
SPP
C
Utility-aware scheduling -- SAAB-U
SLA -- Utility functionSAAB-U -- Utility function
15
Utility-aware scheduling -- SAAB-U
16
each request for I2 is delayed by the amount of time needed to transmit I1
general priority function
21
)()( ,21,2,1,1
P
jjj
P
jjjX ECuEuU
12
)()( ,12,1,2,2
P
jjj
P
jjjY ECuEuU
ii P
jjiji
P
jjijiri CnEuEuP ))1(()( ,,,,
Experiments
17
SAAB-TSlack factor (SF)
SJF = 1/Ci high loadEDF = 1/Di low loadMRF = Pi W-SJF = Pi/Ci
Experiments
18
SAAB-U
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