# Presentation: 355058

## A Unified Relational Approach to Grid Information Services(GWD-GIS-012-1 (Informational))

- Peter A. Dinda, Northwestern
- Beth Plale, Georgia Tech

## Claim
Applications need common compositional queries over information of varying dynamicity

Approach
Build down from an RDBMS world-view
Relational = relational data model and queries
Unified = tables and streams

Research Questions
How “far down” must we go?
What extensions are needed?  

- Applications need *common* *compositional* queries over information of *varying dynamicity*
- _Approach_
- Build down from an RDBMS world-view
- Relational = relational data model and queries
- Unified = tables and streams
- _Research Questions_
- How “far down” must we go?
- What extensions are needed?

- 1

- 2

- 3

## Specific Components

- Extensible type hierarchy
- Extensible schemas and indices
- Data streams as relations
- High update rates and freshness
- Compositional queries -> joins
- Time-bounded non-deterministic queries
- Friendly interfaces for non-experts
- Decentralized administration and data

## Outline

- Needs of Grid applications
- Limitations of current models
- Our approach (and research)
- Prototype system
- Schema and indices (including example)
- Fast updates and streaming
- Data stream support with dQUOB
- Time-bounded non-deterministic queries

## Needs of Grid Applications

- Compositional queries
- Application-specific information aggregration
- Support for information of varying dynamicity
- Varying update rates and freshness requirements
- Seamless inclusion of streaming data
- A common data and query model
- Powerful, high level, declarative, easy-to-optimize

## Data Parallel SOR				

- Startup: “Find 4 hosts which all have the same architecture and have a combined memory of at least 2 GB and whose network path bandwidths to each other are comparable”
- 		Compositional Query Over Static Information
- Adaptation: “Tell me about instances in which the predicted load on any one of those 4 hosts exceeds the average of their predicted loads by 50%”
- 		Compositional Query Over Dynamic Information

**?**

**?**

**?**

**?**

## ResourcePredictionSystem 					                

- Software Configuration Management: “For each of those hosts, find an RPS prediction stream corresponding to a measurement stream from a load sensor on the host”
-                Compositional Query Over Semistatic Information
- Performance Monitoring Streams: “Tell me about instances in which the predicted load on any one of those 4 hosts exceeds the average of their predicted loads by 50%”
-                Compositional Query Over Dynamic Streams

## Dv(and traditional workflow)

- Startup: “Find a pool of five hosts each of which have at least a GB of memory for interpolation, a second pool of five different hosts with at least 1 GFLOP/s performance for isosurface extraction, and a third pool of five different hosts with special scene synthesis hardware, where the inter-pool bandwidth is at least 10 MB/s.”
- 		Compositional Query Over Static Information
- Adaptation: “What is the host within the isosurface extraction pool which is expected to have the minimum load over the next 10 seconds?” 		Compositional Query Over Dynamic Streams

## Dv as aQuery

- “Show me the results of rendering the scene synthesized by combining the results of isosurface extraction and morphology reconstruction over regularly grided data resulting from interpolation of this region of the simulation database”
- Compositional Query Describing An Application
- No Specific Query Plan is Implied

## Grid Schedulers

- Similar needs, more flexibility
- But these abstractions are important
- GridSearcher [Schopf]
- Compositional Queries over MDS

## Supporting Compositional Queries

- Set operations -> Relational Algebra -> RDBMS
- ANSI SQL
- Time-bounded Non-deterministic queries

## Type Hierarchies

## Query Example (RPSDB)

## Schemas and Indices

## Non-deterministic Time-bounded Queries

## Data Stream Support

## Distributed Operation

## Interaction with other GIS and Grid Performance Systems

## Fast Updates and Freshness

## Performance Evaluation

## Tensions to explore

- RDBMS versus distributed data and decentralized administration and multiple security domains
- RDBMS versus expensive queries
- Power versus usability (SQL)

## Prototype System(s)

- RPSDB
- dQUOB

## Unification

## ACID?

## Conclusions

- Come join us