Data Scheduling for Multi-item and transactional Requests in On-demand Broadcast Nitin Pabhu Vijay Kumar MDM 2005

2 Outline Introduction Related Work Background and Motivation Scheduling Algorithm Experimental Results Conclusion and Future Work

3 Introduction Data broadcasting through wireless channel –become a common way –offer high scalability –satisfy multiple request –utilize bandwidth efficiently Broadcast scheduling algorithm is the main component of broadcasting systems –affect data latency and access time Broadcast systems –push-based –pull-based –hybrid

4 Introduction (Cont.) Push-based –server periodically broadcast a schedule using client’s access history –do not concern the current data access pattern Pull-based (On-demand) –client explicitly request for data item form server –server broadcast the data request pending in the service queue –perform better Hybrid –a mixture of push and pull approaches

5 Introduction (Cont.) This paper focus on on-demand multi-item broadcast system –use bandwidth efficiently –access to data timely –most of the users seek specific information

6 Related Work Push-based –deliver data based on the access probability Hybrid –a part of channel bandwidth is reserved for pull Pull-based –J. Wang FCFS (First Come First Serve) MRF (Most Request First) MRFL (Most Request First) LWF (Longest Wait First)

7 Related Work (Cont.) Xuan, Sen, … –request s associated a deadline –on-demand with EDF (Earliest deadline First) performs better Aksoy, Franklin –R*W R: number of requests for a data item W: waiting time of the first request in R –select the data item with maximum R*W value Vincezo, Liberatore –use the access pattern dependencies and access probability

8 Background and Motivation on-demand scheduling schemes for single item requests –there is a single server –client sends query or update transaction through uplink channel –server broadcasts relevant information over the downlink where user retrieves the result –assume that all data items are available on the server with equal size hence they have equal service time –The broadcast duration of a data item is a broadcast tick

9 Background and Motivation (Cont.) Current broadcast scheduling schemes –make decision at the data item level –does not consider all transactions that requested them –consistency problem –increase the access time

10 For example: if d 1 and d 2 is updated before 7 th broadcast tick compute d 1 + d 2 - d 7 => inconsistent Background and Motivation (Cont.)

11 Background and Motivation (Cont.) This have tried to overcome this problem With index, mobile client can check if its required data is in the current broadcast cycle –If NOT, mobile client can sleep and tune in the next broadcast cycle for index

12 Scheduling Algorithm Consistency –a database state: –data items are related through some constraints –mechanisms to update at periodic intervals –client gets consistent view of the database Response time

13 Scheduling Algorithm (Cont.) Tuning Time –the time spent by the client listening to the broadcast channel to access the data items –a measure of the power consumed –define: n = size of a database D (total number of data items) R di = number of request for data item d i TD i = data items accesses by transaction t i, TD i  D num i = number of data items accessed by transaction t i, num i = | TD i | T avg = average transaction size in terms of data items

14 Scheduling Algorithm (Cont.) Temperature of a Transaction –a measure of the number of hot data items that a transaction accessed –Temp i = the temperature of transaction t i –Temp i =  R dj / num i for all d j  TD i –Ex: TD 1 ={d 1,d 2,d 7 }, Rd 1 =5, Rd 2 =4,Rd 7 =1, num i =3 Temp 1 = (5+4+1)/3 = 3.33 take scheduling decisions at every broadcast cycle

15 Scheduling Algorithm (Cont.) broadcast cycle –content is same in every cycle at push-based –in this paper, content may vary depending on the current workload at the server –Purpose indexing can be used updates can be applied to the database at the interval

16 Scheduling Algorithm (Cont.) Definition –Request: the set of transactions –Bset: data items selected in the current cycle –Tlist: the set of transactions used to fill the current Bset Protocol at the Server 1.Calculating Temperature of transactions –Calculate temperature of all transactions 2.Sorting the Request list –By (Temp i *W i ) values 3.Selection for transactions for current broadcast cycle –Identify Bset and Tlist –K is the length of the broadcast cycle

17 Scheduling Algorithm (Cont.) 4.Arrangement of data items with in the broadcast cycle –Broadcast denotes the order all data items to be broadcasted in the current cycle

18 Scheduling Algorithm (Cont.) d). Select transaction t i with the highest value of ( overlap i / rem i ) If there is a tie, then select the one that has highest Temp i *W i value 1. Broadcast = Broadcast ∪ (TD i – (Broadcast ∩ TD i ) 2. Tlist = Tlist - t i 3. If | Broadcast | ≤ K then Go to step c 1. Broadcast= Broadcast ∪ (TD i – (Broadcast ∩ TD i ) 2. Tlist = Tlist – t i c). Calculate for every transaction t i ∈ Tlist 1. The overlap of the transaction t i with the Broadcast set as: overlap i = Broadcast ∩ TD i 2. Number of data items remaining to be broadcasted denoted rem i where rem i = |TD i – overlap i |

19 Scheduling Algorithm (Cont.) 5.Indexing –Adapt (1,m) indexing mechanism –Indexing at the beginning of every broadcast cycle (major index) and after every (K/m) broadcast slots (minor index) –i th minor index will contain (K – i*(K/m)) data items yet to be broadcasted

20 Scheduling Algorithm (Cont.) 6.Broadcast data –In the order determined in Broadcast set 7.Filtering Transactions –At the end of current broadcast cycle –Remove Request which were not selected in Tlist –Two categories of arrival 1.Before the current broadcast cycle 2.After the current cycle has began –T begin be the time when the broadcast cycle started –T i be arrival time of transaction t i For all the transaction which arrived till the end of the current broadcast cycle If (T i < T begin ) then { If (TD i ∩ Broadcast = TD i ) then Request = Request – t i } else 1. Select the next minor index (MI) that was broadcasted after time T i. 2. If (TD i ∩ MI = TD i ) then Request = Request – t i.

21 Protocol at client Scheduling Algorithm (Cont.)

22 Example Step5 (indexing) m=7 K/m=1 –major,d1,minor1,d2,minor3,d7,minor4,d3,minor5,d4,minor6,d6,minor7,d5 Step6 (broadcast) Step7 (filtering) –Rquest={t1,t2,t3,t4,t5} –t1: Rquest={t2,t3,t4,t5} –t2: Rquest={t3,t4,t5} –t3: Rquest={t4,t5} –t4: Rquest={t5} –t5: TD5  minor2  TD5, Rquest={t5}

23 Example (Cont.) T1=1,T2=2,T3=2,T4=3,T5=6, Tbegin=5 Step1 (calculate Temperature) –Temp1=3.33, Temp2=3.25, T3=3.33, T4=3.25 Step2 (compute Tempi*Wi) –TW1=9.99, TW2=6.5, TW3=6.66, TW4=3.25 => t1>t3>t2>t4 Step3 (select transaction) K=7 –t1: Tlist={t1}, Bset={d1,d2,d7} –t3: Tlist={t1,t3}, Bset={d1,d2,d3,d4,d7} –t2: Tlist={t1,t2,t3}, Bset={d1,d2,d3,d4,d6,d7} –t4: Tlist={t1,t2,t3,t4}, Bset={d1,d2,d3,d4,d5,d6,d7} Step4 (arrangement) –t1: Tlist={t1}, Broadcast={d1,d2,d7} overlap2/rem2 = 2/2=1, overlap3/rem3=1/2, overlap4/rem5=2/2=1, TW3>TW2 –t3: Tlist={t1,t3}, Broadcast={d1,d2,d7,d3,d4} overlap2/rem2=3/1=3, overlap4/rem4=3/1=3, TW2>TW4 –t2: Tlist={t1,t2,t3}, Bset={d1,d2,d7,d3,d4,d6} –t4: Tlist={t1,t2,t3,t4}, Bset={d1,d2,d7,d3,d4,d6,d5}

24 Experimental Results Simulation environment –Using a simulation model called CSIM –Use broadcast tick to measure simulated times

25 Experimental Results (Cont.) Performance compare with FCFS, MRF and R*W

26 Experimental Results (Cont.) Effect of broadcast period and the aborts comparison with FCFS, MRF and R*W

27 Experimental Results (Cont.) Effect of shift in hot spot on transaction waiting time –1000(10%) of the database

28 Conclusion and Future Work Contribution –Issue: scheduling multiple items and transactional requests in an on-demand broadcast environment Future –Dynamically determining the optimal size of broadcast cycle period Problem –Overhead of the scheduling and the transmission time were not concerned –The approach in this paper is more like hybrid