XtreemOS WP3.2 - T3.2.3 Scalable Directory Service Design State of Arts and Proposals

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Martina Baldanzi, Massimo Coppola, Domenico Laforenza, Laura Ricci ISTI/CNR Pisa. XtreemOS WP3.2 - T3.2.3 Scalable Directory Service Design State of Arts and Proposals. DISTRIBUTED DIRECTORY SERVICES . Consider a system where any resources R is defined by k attributes. R
Martina Baldanzi, Massimo Coppola, Domenico Laforenza, Laura Ricci ISTI/CNR Pisa XtreemOS WP3.2 - T3.2.3 Scalable Directory Service DesignState of Arts and Proposals XtreemOS IP project is funded by the European Commission under contract IST-FP6-033576 DISTRIBUTED DIRECTORY SERVICES Consider a system where any resources R is defined by k attributes. R corresponds to a point in a k-dimensional case Directory services (DS) =A service returning the name(s) or address(es) of all the items (resources) characterized by a k given values of the attributes. Some functionalities of the DS: indexing of distributed resources complex queries requiring resources satisfyinga set of constraints dynamic attributes pub/sub functionalities. notification of a resource state updates COMPLEX QUERIES: CLASSIFICATION Queries may be classifed according to the number of attributes considered by the query k-dimensional queries, k, k2 (DHT support 1-dimensional queries only) type of attributes:static, dynamic, semi-dynamic constraint defined on each attribute exact match query:Arch.='x86' and CPU-Speed='3 Ghz' and RAM='256MB' partial match queries:CPU-Speed='3 Ghz' and RAM='256MB' (and Arch.=*) range queries1Ghz<CPU-Speed<'3Ghz' and 512MB<RAM<1Gb similarity queries (o nearest neighbour queries) require the definition of ametric in the attribute space the user submit an exact match query, which defines a point P in the attribute space. P may not correspond to any resource. Output: k resources nearest to P, according to the defined metric COMPLEX QUERIES: CLASSIFICATION Query Multi-Dimensional Uni-Dimensional Partial Match Range Queries Similarity Queries Exact Match
  • Data returned by these queries are close in the attribute space
  • Data locality is destroyed by the hash mapping defined in DHT. Points, i.e. resources, close in the attribute space may be mapped to nodes far from each other on the overlay
  • This is due to theuniform mappingdefined by the hash function
  • Definition of an indexing layer above the DHT to recover loss of locality
  • NODE ARCHITECTURE Query Layer Indexing Layer DHT Layer Look-up (key) put(key, data) DHT (Chord, Pastry, CAN, Kademlia...) TCP/IP EXISTING APPROACHES DHT based approaches Locality preserving hash functions 1-dimensional-range queries (MAAN,CHORD#) k-dimesional range queries: locality preserving mapping from a k-dimensional space to a 1-dimensional space based on space filling curves (Squid) Space partitioning based approaches The k-dimensional attribute space is partitioned into a set of zones The granularity of the domain of the hash function is increased Mapping of zones to peers (Gao-Steenkiste, Ratnasamy,....) Other approaches (not DHT based) Voronoi based overlays definition Important Remark: No proposal defines a single framework for range queries, multidimensional queries, dynamic attributes K-DIMENSIONAL SPACES ON DHT Red-blue Line= Space filling curve Space linearization: Each point of the k-dimensional space is mapped to a point of the blue-red line Points on the blue-red line are indexed by integer numbers. Mapping of the points of the line on the DHT: keys= indexes of points Example: Point (010, 010) (red point ) is mapped on Successor(001000). Points close on the blue-red line are close in the k-dimensional space are mapped to the same node of the DHT or to close nodes SPACE FILLING CURVES: QUERY RESOLUTION Range Query Resolution points which are close on the red line are also close in the k-dimensional space Points close in the k-dimensional space may be not close on the red line Cluster= set of points belonging to the same segmentof the red line Range Query Resolution Detection of clusters covering data covered by the query For each cluster, the query is sent to the nodes storing that cluster A single message for each cluster.
  • Range query (101, 100-111)
  • covers the orange zone
  • defines two different clusters
  • SPACE PARTITIONING APPROACHES Attributes space is partitioned into zones Each zone is assigned to a different peer Hash functions maps zones, instead that single points. Attribute space partitioning is described by a tree-like index structure which is distributed to the nodes of the system Given a k-dimensional query corresponding to a point P in the k-dimensional space, the tree is exploited to detect the zone Z including P The querying node exploits the hash function to map Z to a node SPACE PARTITIONING APPROACHES Open research problems: Range query support Definition of highly distributed data structures Replication/consistency of the data structure Dynamic indexes Load Balancing techniques FIRST PERIOD PROTOTYPE The prototype developed in the first period of the project must integrate k-dimensional and range queries within a single framework based on locality preserving hash functions dynamic attributes? experiments on a real distributed platform (GRID 5000) Afterwards, investigate more complex solutions Tree based indexes Space filling curves FIRST PERIOD PROTOTYPE A directory service supporting k-dimensional range queries: based on DHT exploiting locality preserving hash functions (Locality Preserving Bamboo, Chord#,...?) load balancing: insertion of new nodes in 'crowded regions' k-dimensional range queries replication. A resource R defined by k attributes is registered under k different keys. One key for each attribute value k-dimensional Range Queries simple, but inefficient solution: intersectionofk 1-dimensional range sub-queries (one for each attribute). improvement: definition of a dominant attribute. decreasing the size of the search space A query for the dominant attribute, sub-query managed by each detected node FIRST PERIOD PROTOTYPE DYNAMIC ATTRIBUTES detect static and dynamic attributes define groups of resources characterized by the same values of a static attribute Ex: all the host with CPU=2.5GHz define a multicast group G for each group of resources Hashing is applied to the static attribute. The resulting node returns a node R acting as the root of the a multicast tree associated to G. R forward the query to any peer P belonging to the multicast group. Each P checks the value of the dynamic attributes Exploit the DHT routing level (ex: Scribe application level multicast on Pastry) to define an application level multicast
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