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A Hybrid Caching Strategy for Streaming Media Files. Jussara M. Almeida Derek L. Eager Mary K. Vernon University of Wisconsin-Madison University of Saskatchewan November 2001. Outline. Characteristics of Streaming Media (SM) files Delivery of SM files

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a hybrid caching strategy for streaming media files

A Hybrid Caching Strategy for Streaming Media Files

Jussara M. Almeida Derek L. Eager Mary K. Vernon

University of Wisconsin-Madison

University of Saskatchewan

November 2001

outline
Outline
  • Characteristics of Streaming Media (SM) files
  • Delivery of SM files
  • Hypothesis and Assumptions
  • Previous Caching Policies
  • New Policy Performance Comparison
  • New Caching Policies
  • Conclusions and Future Work
characteristics of sm files
Characteristics of SM Files
  • Large file size
    • cache on disk
  • Sustained I/Obandwidth
    • inserting and reading new content
  • Clients access partial files
    • initial portion
    • favored segment
    • base + variable number of layers of layered encoding
delivery of sm files
Delivery of SM Files
  • Unicast streaming:
    • server bandwidth is linear in client request rate
    • goal: maximize byte hit ratio
  • Multicast streaming
    • save bandwidth
    • cost sharing introduces new tradeoffs
slide5
Caching for Multicast Streams: Tradeoffs
  • example:

10 distributed proxy servers each serving a local region,

100 requests (on avg) arrive per region during a given popular video

need 7 streams per region, or 12 streams at the remote server

slide6
Caching for Multicast Streams: Tradeoffs
  • caching popular content reduces the load on the remote server and network
  • delivering popular content from the remote server amortizes the cost of a stream over more clients
  • earlier portions of a popular video require more bandwidth and have less cost-sharing than later portions
new caching policies research
New Caching Policies Research
  • Hypothesis: popularity-based strategy will outperform replacement-based strategy
    • significant fraction of requests to uncached files may be for files that are accessed very sporadically
  • Assumptions:
    • limited disk space implies limited disk bandwidth
    • proxy bandwidth for delivering cached streams is equal to min of proxy disk bw and proxy network bw

(call this proxy disk bandwidth)

current web caching policies
Current Web Caching Policies
  • Replacement based (cache on each miss)
  • Top replacement candidate is an ad-hoc combination of:
    • large files
    • least recently access or lower access frequency
    • miss penalty (server latency, bandwidth)
  • Cache whole file or none
  • Unicast
  • Ignore limited disk bandwidth
previous sm caching policies
Previous SM Caching Policies
  • Interval Caching [DaSi93, KaRT95]
  • Resource Based Caching (RBC) [TVDS98]
  • Least Frequently Used (LFU)
  • Block-based insertion and deletion [AcSm00]
  • Popularity-based caching for layered encoding [RYHE00]
  • Prefix and Segment Caching for smoothing [SeRT99,WZDS98]
interval caching
0

T

S1

S2

S1

Time

0

T

S2

S1

0

T

S3

S2

S1

0

T

Interval Caching
  • Cache smallest intervals
  • Target: memory caches (lots of insertions)

Filef

slide11
Resource Based Caching
  • Cache entire files and intervals/runs
  • Goal: efficiently utilize the limited resource
    • limited space: cache smallest space requirement
    • limited bandwidth: cache smallest write overhead
  • Pre-allocate bandwidth to each cached entity
  • Complex algorithm
    • Complex implementation
    • High time complexity
slide12
Step 1: Selecting entity x  {interval, run, file} of file i

1) If Ubw > Uspace + 

Choose the entity with lowest

2) If Uspace > Ubw + 

Choose the entity with minimum space requirement Si,x

3) If Uspace -  < Ubw < Uspace + 

Choose the entity with largest

Step 2: Caching decision for entity x

1) If enough unallocated space and unallocated bandwidth:

Cache entity x

2) If enough unallocated space but bandwidth constrained:

Use bandwidth goodness list to select candidates for eviction

3) If enough unallocated bandwidth but space constrained:

Use space goodness list to select candidates for eviction

4) If both bandwidth and space constrained:

Walk on both lists: at each step, remove entity from bandwidth goodness list or from space goodness list.

Step 3: Allocate spaceandbandwidth for entity x

RBC Algorithm

slide13
Least Frequently Used
  • Different implementation options:
    • What to do when receive first access to an object?
    • How to estimate frequency?
  • Version studied: Currently Most Popular (CMP)
    • Insert only most frequently accessed (file or segment)
    • On-line popularity estimate: future research
previous comparison rbc vs cmp tvds98
Previous comparison : RBC vs. CMP [TVDS98]
  • Fixed fileaccess frequencies
  • RBC outperforms CMP for all parameter values studied
  • Limited design space
    • e.g.: total cache size  16GB
  • Inconsistent results
new performance comparison
New Performance Comparison
  • Re-evaluate byte hit ratio of CMP and RBC
    • Simulation with synthetic workload
    • Broad design space
  • New Pooled RBC
  • New simple hybrid CMP/interval caching (CMP/IC) policy
system assumptions
System Assumptions
  • Arrivals: Poisson()
    • extra experiments with Pareto(,k)
  • File access frequency: Zipf()
  • Perfect File popularity
    • extra experiments with approximate file popularity
  • Uniform file size and delivery rate
    • extra experiments with variable file size and delivery rate
  • Load balanced across multiple disks
system parameters
System Parameters
  • n : number of files
  •  : Zipf parameter
  • N : arrival rate (avg. number of requests per avg. file duration T)

N =   T

  • C : cache size (fraction of media data accessed)
system parameters1
B: normalized disk bandwidth

(fraction of the average number of simultaneous streams needed to deliver data that is cached by CMP)

B depends on N, , n, C and disk technology

Relative performance of policies depends mainly on B

B = 1.0 : CMP system is bandwidth balanced

B  1.0 : CMP system is bandwidth deficient

B  1.0: CMP system is bandwidth abundant

System Parameters
normalized disk bandwidth b example
Normalized Disk Bandwidth (B)Example
  • Ultrastar 72ZX disk :
    • disk space: 116.76hours of MPEG-1 video (73.4GB)
    • disk bandwidth: 108 MPEG-1 streams (22-37 MB/s )
  • Assume: 100 requests / hour for cached files
  • If cache contains 2-hour movies:
    • Need 200 streams
    • B =108/200 = 0.54
  • If cache contains 30-minute TV shows:
    • Need 50 streams for cache content
    • B =108/50 = 2.16
rbc vs cmp
RBC vs. CMP
  • CMP outperforms RBC if B  1.0
  • RBC slightly outperforms CMP if B  1.0 and small caches

N = 450, n= 100, =0

files cached by rbc
Files Cached by RBC
  • Average fraction of each file cached by RBC (N = 450, n = 100, C=0.25)

B = 0.75

B = 1.0

B = 2.0

space and bandwidth utilization
Space and Bandwidth Utilization

B = 0.75

B = 1.0

B = 2.0

pooled rbc
Pooled RBC
  • Three improvements over RBC
    • simpler rule to select entity to cache
    • can keep cached intervals when deleting a full file
    • pool of pre-allocated bandwidth
  • Similar complexityas RBC
pooled rbc rbc and lfu
Pooled RBC, RBC and LFU
  • Pooled RBC  CMP
  • BUT, Pooled RBC is much more complex than CMP

N = 450, n= 100, =0

hybrid cmp ic policies
Hybrid CMP/IC Policies
  • Do interval caching on a separate (small) cache
    • Interval Cache in Main Memory: CMP/ICmem and Pooled RBC/ICmem
    • Interval Cache on Disk: CMP/ICdisk
      • e.g. 5% of disk cache
cmp ic mem vs pooled rbc ic mem
CMP/ICmem vs. Pooled RBC/ICmem

N = 450, n= 100, =0

  • Memory cache improves CMP and Pooled RBC
  • B  1.0 : greater improvement for CMP
slide27
CMP/ICdisk vs. Pooled RBC

N = 450, n= 100, =0

  • CMP/ICdisk  Pooled RBC  CMP
conclusions
Conclusions
  • Simple CMP
    • simple to implement
    • performance similar to Pooled RBC, CMP/ICdisk (static file popularities)
  • Hybrid CMP/IC policy
    • Performance  Pooled RBC
    • simple to implement
    • possibly more robust (imperfect and dynamic popularity measures)
future work
Future Work
  • Develop on-line estimate of file popularity
  • Server log analysis
    • client behavior and workloads (NOSSDAV’01 paper)
    • More logs!!!!
  • Caching Policies for Multicast Streams
    • popular file has greater cache-sharing if not cached
    • determine cache content that minimizes per-client cost
    • caching principles / on-line policy
    • (coming up soon)
  • Prototype, experimental ( live ) workloads
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