Aging Through Cascaded Caches: Performance Issues in the Distribution of Web Content.

1. Aging Through Cascaded Caches:Performance Issues in the Distribution of Web Content. Edith Cohen AT&T Labs-research Haim Kaplan Tel-Aviv University Stanford Networking Seminar

2. HTTP Freshness Control • Cached copies have: • Freshness lifetime • Age (elapsed time since fetched from origin) • TTL (Time to Live) = freshness lifetime – age • Expired copies must be validated before they can be used (request constitutes a ”cache miss”). Cache-directives header Body (content) Stanford Networking Seminar

3. Aging of Copies 8:00am Origin server Age = 0 TTL = 10 Freshness Lifetime = 10 hours Stanford Networking Seminar

4. Aging of Copies 12:00pm 3:00pm Origin server Age = 4 TTL = 6 Age = 7 TTL = 3 9:00am Age = 1 TTL = 9 Freshness Lifetime = 10 hours Stanford Networking Seminar

5. Aging of Copies Origin server Age = 10 TTL = 0 6:00pm Freshness Lifetime = 10 hours Stanford Networking Seminar

6. Aging thru Cascaded Caches 8:00am origin server Age = 0 TTL = 10 proxy caches reverse-proxy cache Stanford Networking Seminar

7. Aging thru Cascaded Caches origin server proxy caches 5:00pm reverse-proxy cache Age = 9 TTL = 1 Stanford Networking Seminar

8. Aging thru Cascaded Caches origin server Age = 10 TTL = 0 proxy caches 6:00pm reverse-proxy cache !! !! Stanford Networking Seminar

9. Aging thru Cascaded Caches origin server Age = 0 TTL = 10 proxy caches 6:00pm reverse-proxy cache Stanford Networking Seminar

10. TTL of a Cached Copy M M M TTL From Origin M M From Cache Freshness-lifetime Requests at client cache: t Stanford Networking Seminar

11. Age-Induced Performance Issues for Cascaded Caches • Caches are often cascaded (path between web server and end-user includes 2 or more caches.). • Copies obtained thru a cache are less effective than copies obtained thru an origin server. Reverse proxies increase validation traffic !! • More misses at downstream caches mean: • Increased traffic between cascaded caches. • Increased user-perceived latency. Stanford Networking Seminar

12. Research Questions • How does miss-rate depend on the configuration of upstream cache(s) and on request patterns ? • Can upstream caches improve performance by proactively reducing content age ? how? • Can downstream caches improve performance by better selection or use of a source? Our analysis: • Request sequences: Arbitrary, Poisson, Pareto, fixed-frequency, Traces. • Models for Cache/Source/Object relation: Authoritative, Independent, Exclusive. Stanford Networking Seminar

13. Basic Relationship Modelscache/source/object www.cnn.com Cache-A Cache-B Cache-C Cache-D Cache-3 Cache-2 Cache-1 • Authoritative: “Origin server:” 0 age copies. • Exclusive: all misses directed to the same cache. • Independent: each miss is directed to a different independent upstream cache. Stanford Networking Seminar

14. Basic Models… Object has fixed freshness-lifetime of T. Miss at time t results in a copy with age: • Authoritative age(t) = 0 • Exclusive age(t) = T - (t+a) mod T • Independent age(t) e U[0,T] Theorem:On all sequences, the number of misses obeys: Authoritative<Exclusive<Independent Theorem: Exclusive< 2*Authoritative Independent < e*Authoritative Stanford Networking Seminar

15. TTL of “Supplied” Copy Authoritative Exclusive Independent Source: TTL Freshness-lifetime Requests Received at source: t Stanford Networking Seminar

16. How Much More Traffic? Miss-rate for different configurations Stanford Networking Seminar

17. Rejuvenation at Source Caches client no rejuv. Rejuvenation: refresh your copy pre-term once its TTL drops below a certain fraction v of the Lifetime duration. TTL v=0.5 source 24h 12h t Requests at client: Stanford Networking Seminar

18. Rejuvenation’s Basic Tradeoff: • Increases traffic between upstream cache and origin (fixed cost) • Decreases traffic to client caches (larger gain with more clients) Downstream Client caches Upstream cache origin Is increase/decrease monotone in V (?) Stanford Networking Seminar

19. Interesting Dependence on V… • Independent(v) <> Exclusive(v) • Independent(v) is monotone: if v1 > v2, • Independent(v1)>Independent(v2) • Exclusive(v) is not monotone • (miss-rate can increase !!) • Integral1/v (synchronized rejuvenation): Exclusive(v) < Independent(v) and is monotone (Pareto, Poisson, not with fixed-frequency). Stanford Networking Seminar

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22. How Can Non-integral 1/v Increase Client Misses? Requests at Client cache: Copy at client is not synchronized with source. When it expires, the rejuv source has an aged copy. TTL Upstream Cache Downstream Client Cache Pre-term refreshes Freshness-lifetime t Stanford Networking Seminar

23. Why Integral 1/v Works Well? Requests at Upstream cache: Cached copies remain synchronized TTL Upstream Cache v=0.5 Downstream Client Cache Pre-term refreshes Freshness-lifetime t Stanford Networking Seminar

24. Some Conclusions • Configuration: Origin (“Authoritative”) is best. Otherwise, use a consistent upstream cache per object (“Exclusive”). • “No-cache” request headers: resulting sporadic refreshes may increase misses at other client caches. (But it is possible to compensate…). • Rejuvenation: potentially very effective, but a good parameter setting (synchronized refreshes) is crucial. • Behavior patterns: Similar for Poisson, Pareto, traces, (temporal locality). Different for fixed-frequency. • For more go tohttp://www.research.att.com/~edith Full versions of: Cohen, Kaplan SIGCOMM 2001 Cohen, Halperin, Kaplan, ICALP 2001 Stanford Networking Seminar