Scoped and Approximate Queries in a Relational Grid Information Service

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Scoped and Approximate Queries in a Relational
Grid Information Service

• Dong Lu , Peter A. Dinda , Jason A. Skicewicz
• Prescience Lab, Dept. of Computer Science
• Northwestern University, Evanston, IL 60201

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Outline

• Introduction and motivation
• Powerful queries, but expensive to execute
• Trade off between result size and query time
• Our solutions Scoped query, Approximate query,
  Scoped Approximate query
• Nondeterministic query (SC Talk on Tuesday)
• Performance Evaluation

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What is RGIS?

• GIS A Grid Information Service stores
  information about the resources and services in a
  distributed computing environment and answer
  queries about it.
• RGIS Grid Information Service based on
  relational data model.

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Why RGIS?

• RGIS can answer complex compositional queries
• Relational algebra (SQL)
• Joins
• Difficult in a hierarchical model (directory
  service)
• Other reasons
• Indexes separate from data model
• Schema evoluation
• Transactional insert/update/delete
• Consistency

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RGIS Model of a Grid
module

• Annotated network topology graph
• Annotation examples
• Hosts memory, disk, OS, NICs, etc.
• Router/Switch backplane bandwidth, ports
• Link latency and bandwidth
• Highly dynamic data in streams, not DB
• Virtualization, Futures, Leases
• Virtual machines

Software
endpoint
router
iplink
host
Network
Data link
maclink
macswitch
Physical
connectorswitch
connectorlink
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The RGIS Design (Per Site)
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Challenge/Trade off

• Complex queries to a relational database can take
  a long time,
• Hours, days or even weeks when we want seconds.
• Typically, returned result set is unnecessarily
  big.
• Get back all results
• We need mechanisms to trade off the query time
  with the size of result set.

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Challenge/Trade off
All results
Approximate results
Nondeterministic results
Scoped results
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Example Cluster Finder
Find N hosts connected to the same router, with
total memory N512 MB, all running Linux, and
the bisection bandwidth of The cluster is no
less than 100Mbits/sec.
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Original SQL for 2 Host Cluster Finder
SELECT scoped-approx h1.distip, h2.distip
FROM hosts h1, hosts h2, iplinks l1, iplinks
l2, routers r WHERE h1.mem_mbh2.mem_mbgt1024
and h1.os'linux' and h2.os'linux' and
((l1.srcr.distip and l2.srcr.distip
and l1.desth1.distip and l2.desth2.distip)
or (l1.destr.distip and l2.destr.distip
and l1.srch1.distip and l2.srch2.distip))
and h1.distipltgth2.distip and L1.BW_MBS gt
100 AND L2.BW_MBS gt 100 SCOPED BY
r.distipX WITHIN 100 seconds
Original
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Original SQL for Cluster Finder

• It is 2N1 way join to look for a N node
  cluster. Not scalable.

Routers
IP links
Hosts
Cluster 1
Cluster 2
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Scoped Cluster Finder
Routers
IP links

• Query the hosts
• around a random
• router.

Hosts
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Scoped Cluster Finder
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Approximate Cluster Finder

• When searching for N hosts with total memory
  N512, we can approximate the query with search
  for N hosts with each having memory over 512.
• Thus reduced or avoided the number of joins.
• However, this wont find, say, N/2 hosts with 256
  MB and N/2 hosts with 768 MB

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Approximate Cluster Finder
SELECT R.DISTIP, H1.DISTIP FROM HOSTS
H1, IPLINKS L1, ROUTERS R WHERE
H1.MEM_MBgt512 AND H1.OS'LINUX' AND
L1.BW_MBS gt 100 AND ((L1.SRCR.DISTIP AND
L1.DESTH1.DISTIP) OR (L1.DEST
R.DISTIP AND L1.SRCH1.DISTIP)) AND R.DISTIP
IN (SELECT R.DISTIP FROM HOSTS
H1, IPLINKS L1, ROUTERS R WHERE
H1.MEM_MBgt512 AND H1.OS'LINUX' AND
L1.BW_MBSgt100 AND ((L1.SRCR.DISTIP
AND L1.DESTH1.DISTIP) OR (L1.DEST
R.DISTIP AND L1.SRCH1.DISTIP)) GROUP
BY R.DISTIP HAVING COUNT() gt
2) ORDER BY R.DISTIP
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Scoped Approximate Cluster Finder

• Combine approximate query with scoped query.
• Scoped to one randomly chosen router at a time,
  if no results found, choose another random router
  and repeat the query.
• Approximate N host join for 512N memory with
  searches for N hosts each with gt512.
• Always a THREE way join.
• regardless of the size of the cluster being
  searched for. Thus very scalable.
• may need to search multiple routers.

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Scoped Approximate Cluster Finder
The scoped approximate cluster finder has a fixed
number of joins.
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Time bounded queries

• The query rewriter will start the query as a
  child process.
• Parent kills the child process if no results
  returned within deadline.

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Limitations of Scoped and Approximate queries

• The returned results are subset of original
  query, and it is possible to report no results
  while the original query could return results
  after running a long time.
• Not all queries can be written as Scoped or
  Approximate queries.
• It is hard to automate the Scoped and Approximate
  query rewriting.

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Performance Evaluation

• Need to populate the database with large amount
  of data.
• Computational grids are still in early stages.
• No large data sets available.
• Use Smith MDS data for memory
• We generate synthetic grids that are
  representative of the Internet.
• Can generate very large grids

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GridG Generated Synthetic Grids

• Three-level network WAN, MAN, LAN. Nodes on WAN,
  MAN are routers, while nodes on LAN are hosts.
• Links IP links annotated with bandwidth and
  latency.
• Hosts annotated with memory size, architecture,
  number of processors, CPU clock rate, disk size,
  etc.
• User can control all the distributions and the
  size of network.

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GridG Synthesing Realistic Computational Grids
SC talk on Tuesday!
http//www.cs.northwestern.edu/urgis/GridG
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Experimental Setup

• Dell PowerEdge 4400 dual Xeon 1 GHz processors,
  2 GB memory, 240 GB RAID 5 storage system.
• Oracle 9i Enterprise edition, red hat Linux 7.1.
• Each test is repeated either 25 or 100 times, and
  we provide the average value.

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Performance of various Query Technique with
Cluster Finder

• Cluster size Standard Scoped Approx
  Scoped Approx
• 2 21.44 2.27
  7.62 1.16
• 4 gt7200 2047.9 7.48
  1.32
• 8 gt9000 gt3600 7.46
  1.43
• 16 N/A gt3600 7.51
  1.45
• 32 N/A gt3600 7.65
  5.96
• 64 N/A gt3600 gt120
  9.58

(Time to run query in Seconds)
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Performance of Scoped Approximate Queries

• Cluster Finder Find N hosts, each running
  Linux, with total memory at least N512 MB, all
  connected to the same router, the bisection width
  is at least 100Mbits.
• Our running example
• Non network query Find N hosts with total
  memory at least N512 MB.
• No joins needed at all

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Performance of Scoped Approximate Queries (2)

• Scalability with database size.
• Scalability with the complexity of queries.
• Scalability with concurrent users and update load.

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Performance of Scoped Approximate Query (9.8K
hosts, Cluster Finder)
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Performance of Scoped Approximate Query (101K
hosts , Cluster Finder)
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Performance of Scoped Approximate Query (980K
hosts , Cluster Finder)
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Performance of Scoped Approximate Query (9.8K
hosts, Non-network query)
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Performance of Scoped Approximate Query (101K
hosts , Non-network query)
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Performance of Scoped Approximate Query (980K
hosts , Non-network query)
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Scalability with multiple concurrent users and
background load

• Other research has shown that GIS servers will
  undertake frequent updating while serving the
  requests.
• GIS servers serve multiple concurrent users.
• Evaluate scoped approximate queries with
  concurrent users and update load.
• Concurrent users execute queries repeatedly
• The update load execute transactional updates on
  randomly selected hosts as fast as possible.
• About 200 updates/second

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Performance of Scoped Approximate Query (9.8K
hosts , Cluster Finder, with Concurrent Users,
looking for 64 nodes)
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Performance of Scoped Approximate Query (9.8K
hosts , Non network query, with Concurrent Users,
looking for 64 nodes)
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Conclusions

• Described and evaluated two query techniques to
  trade off query time with the size of result set
  Scoped and Approximate query.
• Combination of Scoped and Approximate query can
  dramatically reduce response time and server load.

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For more information

• GridG and Related paper http//www.cs.northwester
  n.edu/urgis/GridG
• Synthesizing Realistic Computational Grids,
  In proceedings of SC03.
• RGIS and Related paper http//www.cs.northwestern
  .edu/urgis/
• Nondeterministic Queries in a Relational Grid
  Information Service, In proceedings of SC03.