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## Details

Document Type: | Book |
---|---|

All Authors / Contributors: |
Sumit Ganguly; Rafael Alonso |

OCLC Number: | 33027968 |

Notes: | "December, 1993." |

Description: | 29 leaves : illustrations ; 28 cm. |

Series Title: | Rutgers University.; Department of Computer Science.; Laboratory for Computer Science Research.; Technical report |

Responsibility: | S. Ganguly, R. Alonso. |

### Abstract:

Abstract: "We consider the issue of optimizing queries for a distributed processing in mobile environment. An interesting characteristic of mobile machines is that they depend on battery as a source of energy which may not be substantial enough. Hence, the appropriate optimization criterion in a mobile environment considers both resource utilization and energy consumption at the mobile client. In this scenario, the optimal plan for a query depends on the residual battery level of the mobile client and the load at the server. We approach this problem by compiling a query into a sequence of candidate plans, such that for any state of the client-server system, the optimal plan is one of the candidate plans. A general solution is proposed by adapting the partial order dynamic programming search algorithm [17,18] (p.o dp) such that the coverset of the query is the set of candidate plans. We propose two novel algorithms, namely, the linear combinations algorithm and the linearset algorithm (referred to as the linear algorithms) that compute the linearset of a query. The linearset of a query is an approximation to the coverset returned by p.o. dp. We show, by means of simulation, that (1) the linearset is an excellent approximation of the coverset, (2) query compilation using the linear algorithms outperform query compilation using p.o dp by factors ranging from 2 to 9, (3) the time taken to compile queries using the linear algorithms for the general optimization criterion is at most twice the time taken by a System R* like standard query optimizer search algorithm, and (4) the run time overhead incurred by the linear algorithms technique is minimal. The techniques presented in the paper are of general applicability to multi-criterion optimization problems in distributed databases, where each criterion is an additive metric."

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