2 Basic goals WAN oriented DDBS Multiple sites –e.g., 1000 Scalable Locally autonomous Easy to evolve
3 Solution Traditional DBMS techniques –a non-solution Economical paradigm rooted in –Computer Life Litwin & Wiederhold, 1990 Contract nets –Smith & ? –Implemented at Xerox Parc
4 DBMS architecture Object-relational data model Tables are fragmented –hash, range, or user defined fragments –replicas for better throughput –fragments can split or coalesce (group) –fragments can move among sites –SDDSs could be used for the fragmentation
5 Manipulations SQL-3 Queries are translated to operations on fragmentes An acceptable execution plan is produced –perhaps with strides The plan is optimized through an economical tuning –choose cheapest servers of subqueries
6Architecture Server query proc. Broker Bidder Client query proc. Server query proc. Bidder Server query proc. Bidder Name Server Bidder Name Server Bidder
7 Query Processing Overview Rule based language for bidders, brokers and storage managers on the servers –RUSH system (A.Sah & al) brokers start query auctions –sendout price / time curves in $$ provided to sites by the network bank –choose best bidders –adjust query plans decreases the price as long as the total time remains acceptable
8 Bidders Propose prices & timesPropose prices & times –for CPU, IO, Memory for fragments Advertise their capabilities –with name servers Select a, b from T -> projections of T Select a, b from T where c = '123' -> selections and projections OK Advertise multioperational pricing plans –volume reduction, coupons...
9 Pricing policy CPU & IO c = cost per time unit –e (c, q) estimate of the subquery q cost –l system current load l = 1,2... –final price p p = e * l Idle servers become work hungry Successful servers become expensive Work becomes naturally distributed
10 Memory services Similar rules as for queries, but Load l is the storage load factor The price is continuous per time unit Fragments can get sold among sites –moves or copies –The bidder bids only for queries to fragments it has permanently to temporary results it has from previous subquery
11 BiddingBidding Expensive protocol –auction out –collect replies –compute cheapest plan –sendout queries Cheap protocol –use only advertised capabilities –issue purchase orders to selected bidders –pay the bill (or go to court)
12 Name service Pretty classical naming schema thought : –name location discovery is dynamical –is payable –fragement moves are not synchronously posted to name servers as there are many name servers as in SDDSs for clients
13 Semantic Heterogeneity Name heterogeneity among sites resolved –locally –by name servers Data type and representation heterogeneity –resolved using canonical representation Integer CR Integer
14 Conclusion Mariposa is an attempt to build large MDBSs –based on human society management tools –claimed more efficient at large scale than the traditional DBMS tools –local autonomy –no free lunch It is on-going, and 1st version implemented First results are successful One searches to further improve the economical tools
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