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“A cost-based admission control algorithm for digital library multimedia systems storing heterogeneous objects” – I.R. Chen & N. Verma – The Computer Journal – Vol. 46, No. 6, Oct. 2003, pp. 645-659. Andy Connors. Abstract. Multimedia Systems Mixed workloads – Video, Images & Text

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Andy connors

“A cost-based admission control algorithm for digital library multimedia systems storing heterogeneous objects” – I.R. Chen & N. Verma – The Computer Journal – Vol. 46, No. 6, Oct. 2003, pp. 645-659

Andy Connors


Abstract
Abstract library multimedia systems storing heterogeneous objects”

  • Multimedia Systems

  • Mixed workloads – Video, Images & Text

  • Cost-based admission control algorithm

  • Based on rewards & penalties

  • Resource reservation instead of serving requests until all resources exhausted

  • Reservation based on maximizing total reward

  • Exploit left over resources

  • Simulate algorithm and compare to other schemes


Multimedia system
Multimedia System library multimedia systems storing heterogeneous objects”


Challenge
Challenge library multimedia systems storing heterogeneous objects”

  • Service mixed workloads

    • Real-time video/audio request – resource demanding and varying data rates

    • Discrete media – images and text

  • Need algorithm to “squeeze” in image & text requests without affecting QoS of video requests

  • However, 70% of data types on Web are image & text


Previous algorithms
Previous algorithms library multimedia systems storing heterogeneous objects”

  • Video taking higher priority over image/text data

    • not justified as 70% of requests are image/text not video

  • Shenoy & Vin – two-level disk scheduling framework

    • Level 1: class-independent scheduler – assign bandwidth to application classes – used to dynamically allocate bandwidth to adapt to workload changes – no details on adaption scheme

    • Level 2: class-specific scheduler – order requests into a common queue for access – minimizes seek time and rotational latency overhead – satisfies QoS requirements of each class – discussed in detail

  • To & Hamidzadeh – Continious Media-to-Discrete Media redirection ratio

    • Redirect bandwidth from CM to DM

    • Allocate more buffer space to CM – reduces admissible CM requests

    • Optimize disk reads

    • Use leftover bandwidth for DM requests

    • How much bandwidth to move from CM to DM requests?


Basic idea
Basic Idea library multimedia systems storing heterogeneous objects”

  • Dynamically partition resources based on run-time workload changes

    • Maximize value metric

    • Ensuring that response time requirements met

    • Image/text have “own” resources rather than use “leftovers”

  • Assign value/penalty pair to each request

    • Value: reward if serviced successfully

    • Penalty: loss if service rejected due to lack of resources

    • High value → video higher priority over image/text


Multimedia server model
Multimedia Server Model library multimedia systems storing heterogeneous objects”

  • Cycle based disk scheduling:

    • All requests serviced in TSR – service round duration

    • Image/text either serviced after video/audio or interleaved – use interleaving to minimize disk seek time and latency

  • Video/audio requests

    • As many data blocks as covered by TSR

    • Double buffered – disk buffer & network buffer

  • Image/text requests

    • As many blocks to cover requests object

  • SCAN algorithm:

    • Requests ordered and heads traverse in one direction only

    • Minimizes seek time


Refresher scan algorithm
Refresher - Scan Algorithm library multimedia systems storing heterogeneous objects”


Resource partitioning
Resource Partitioning library multimedia systems storing heterogeneous objects”

  • Text/images serviced in batch

    • Depart at end of service cycle

    • Two FIFO queues, one for text, other for images

  • Statistics of each multimedia object

    • Distribution of all images and text objects

    • Histogram of distribution of size needed to satisfy playback

  • Partition TSR into three parts – video, image and text

    • Based on cost & workload

    • Estimate maximum amount of resources allocated to each type

  • Use left-over time to service more image/text requests


Performance metric
Performance Metric library multimedia systems storing heterogeneous objects”

  • Maximize reward without compromising QoS (bandwidth & response time)

  • Reward rate

    vVNV + vINI + vTNT - qVMV + qIMI + qTMT

    N{V,I,T} = requests completed per unit time

    M{V,I,T} = requests rejects per unit time

    v{V,I,T} = average reward values

    q{V,I,T} = average penalty values


Algorithm
Algorithm library multimedia systems storing heterogeneous objects”

  • Use models derived from queing theory

  • Build lookup table for run-time bandwidth allocation

    • Estimation of reward rate under given workload condition

    • Best bandwidth allocation to maximize reward rate

    • f{V,I,T} = ratio of disk bandwidth for video, image & text requests

    • fV + fI + fT = 1 (when normalized)

    • Service times: f{V,I,T}TSR = disk service time

    • Use statistical admission control to compute number of requests of each type so that probability of disk overload is below a threshold (10-4)

    • (fV, fI, fT) → (nV, nI, nT)

    • System behaves like three separate partitions – three queues

  • For image/text requests

    • n{I,T} image/text requests per TSR

    • Total of K{I,T} * n{I,T} image/text requests – K{I,T} = maximum queue size for image/text requests – can use requests in queue to use left-over bandwidth – K{I,T} depends on QoS


Video request model
Video Request Model library multimedia systems storing heterogeneous objects”

  • M/M/nV/nV queue

    • each video stream acts as if served by separate server until departs

    • V, V = arrival/departure rate of video requests


Video request model1
Video Request Model library multimedia systems storing heterogeneous objects”

  • Pv(j) = probability that j video out of nV slots occupied

  • 0 ≤ j ≤ nV

  • V, V = arrival/departure rate of video requests


Video request reward
Video Request Reward library multimedia systems storing heterogeneous objects”

  • With probability Pv(j), reward rate = j*vV*V

  • So total reward gained = jvVV Pv(j)

  • Rejection rate = V Pv(nV)

  • Lost reward = qV V Pv(nV)

  • Reward rate from video = RV

    RV = (jvVV Pv(j) ) - qV V Pv(nV)


Image text model
Image & Text Model library multimedia systems storing heterogeneous objects”

  • For K{I,T}≥1- M/M/1[n {I,T}]/ K{I,T}* n{I,T} queue

  • Let K{I,T} = 2


Image text model1
Image & Text Model library multimedia systems storing heterogeneous objects”

  • PI(j) = probability that j video out of nV slots occupied

  • 0 ≤ j ≤ nI

  • I, I = arrival/departure rate of video requests

  • Let KI = 1


Image text model2
Image & Text Model library multimedia systems storing heterogeneous objects”

  • PI(j) = probability that j video out of nV slots occupied

  • 0 ≤ j ≤ nI

  • I, I = arrival/departure rate of video requests

  • Let KI = 2


Image text request reward
Image/Text Request Reward library multimedia systems storing heterogeneous objects”

  • With probability PI(j) reward rate =

    • j*vI*I if j < nI

    • nI*vI*I if j ≥ nI

  • Rejection rate = I PI(KInI)

  • Lost reward = qI IPI(KInI)

  • Reward rate from video = RI

    RI = ( jvII PI(j) ) + (nIvII PI(j) ) - qI I PI(KInI)

    j = 1 … nI -1j = nI … KInI


Maximizing reward
Maximizing Reward library multimedia systems storing heterogeneous objects”

  • Given

    V,V,I,I,T,T,vV,qV,vI,qI,vT,qT

  • Maximize R by searching for optimal

    (nV, nI, nT) → (n*V, n*I, n*T)

  • Subject to condition (normalized to text requests)

  • Here NV, NI, NT are maximum number of requests that can be served of each type (if all bandwidth allocated to each type)

  • To use total disk bandwidth


Search
Search library multimedia systems storing heterogeneous objects”

  • Exhaustive

    • Search all possible solutions

    • Complexity O(NT2)

    • Once found all solutions build lookup table

  • Nearest Neighbor

    • When NT is too large and exhaustive is computationally too expensive

    • Complexity O(NT)

    • Fix one nV, nI, nT then next etc.

    • Heuristic – largest product of arrival rate and reward selected first


Admission control algorithm
Admission Control Algorithm library multimedia systems storing heterogeneous objects”

  • Use lookup table to dynamically change to a set of (n*V, n*I, n*T) depending on workload

  • By monitoring input rates

  • Use for admission control

  • Worst case response time for image and text is K{I,T} TSR

  • Use common schedule queue for disk requests

  • If total schedule time < TSR use image/text at head of respective queues to use up remaining time by moving to common queue

  • Probablity that image will be placed on queue f*I/ (f*I+f*T)

  • And for text f*T/ (f*I+f*T)


Analysis
Analysis library multimedia systems storing heterogeneous objects”

  • Numerical analysis of reservation system

  • Parameters:

    • Disk Array

      • 4 disks

      • Average seek time = 11ms

      • Rotational latency of 5.5ms

      • Read/write rate  = 33.3MBps

      • TSR = 1

      • Block size = 4 sectors (512bytes) = 2Kbytes

    • Images

      • Evenly distributed across [10kB, 500kB]

    • Text

      • Evenly istributed across [1kB, 50kB]

    • Video

      • Star Wars – 7200 groups of pictures = 0.5s playback time

      • 12 frames per group

    • Calculate

      • NV = 53, NI = 37, NT = 57

    • Simulate

      • V in range [10,100] arrivals/min, V in range [100,2000], I in range [100,2000]


Other schemes
Other schemes library multimedia systems storing heterogeneous objects”

  • Compare with other algorithms:

    • Video First

      • Highest priority to video requests

      • Left-overs used for image/text

      • (nV, nI, nT) = (NV, 0, 0)

      • Use queue sizes of K{I,T} n*{I,T}

    • Greedy

      • Allocates disk in proportion to product of reward and arrival rate

      • (nV, nI, nT) = ( , , )


Analysis results
Analysis Results library multimedia systems storing heterogeneous objects”


Effect of arrival rates
Effect of Arrival Rates library multimedia systems storing heterogeneous objects”

  • Effect of varying image/text arrival rates as video arrival rate increases

  • For lower image/text rates

    • reward rate increases as video rates increase until hit a maximum where we see a decrease

  • For higher image/text rates

    • Steadingly decreases due to rejects


Effect of video departure rate
Effect Of Video Departure Rate library multimedia systems storing heterogeneous objects”

  • Using varying video departure rates shows effect on increasing video arrival rate

  • At higher departure rates

    • See an increase in reward rate as arrival rate increases until a threshold where server is heavily loaded and rejects requests

  • At lower

    • Video requests stay in system for longer time and so system admits fewer requests


Effect of video reward value
Effect Of Video Reward Value library multimedia systems storing heterogeneous objects”

  • Using varying video reward values shows effect on increasing video arrival rate

  • At higher reward rates

    • Systems admits more requests – threshold shifts higher


Results reward rate
Results – Reward Rate library multimedia systems storing heterogeneous objects”

  • Under light loads

    • Close to predicted lower-bound reward rates

  • At higher loads

    • Higher than calculated – due to effect of using left-over bandwidth which is more pronounced at higher loads

  • In limit

    • Returns back to theoretical as text/image queues are full and consume all server resources

  • Same as video-first at lower loads

    • as system can accommodate most users at these loads

  • At higher loads

    • Out performs both video-first and greedy algorithms


Results response time
Results – Response Time library multimedia systems storing heterogeneous objects”

  • Under light loads

    • Close to other algorithms

  • At higher loads

    • As explicitly allocate time for image/text request see better response times than video-first – difference between 1s and 5s

    • Greedy favors video/text and so has better response times – but compares favorably


Results utilization
Results – Utilization library multimedia systems storing heterogeneous objects”

  • Does not show greedy algorithm as shows same trends as reservation algorithm

  • For video-first

    • Higher utilization for video requests – lower for image/text

  • For reservation

    • Better utilization for image/text

    • Lower for video


Results rejection rates
Results – Rejection Rates library multimedia systems storing heterogeneous objects”

  • At higher loads

    • Rejects fewer image/text requests than video-first or greedy

    • Achieved by rejecting more video requests

    • Video-first rejects 0 video requests but a high number of image/text


Conclusions
Conclusions library multimedia systems storing heterogeneous objects”

  • Significant improvement in reward rate compared to video-first and greedy algorithms

  • Without sacrificing performance metrics such as response time & system utilization