Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Encapsulation of parallelism in the Volcano query processing system
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
ObjectGlobe: Ubiquitous query processing on the Internet
The VLDB Journal — The International Journal on Very Large Data Bases
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Adapting to source properties in processing data integration queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
An adaptable distributed query processing architecture
Data & Knowledge Engineering
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Parallel querying with non-dedicated computers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
QoS-based data access and placement for federated systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Query optimization in distributed networks of autonomous database systems
ACM Transactions on Database Systems (TODS)
QoS management in service-oriented architectures
Performance Evaluation
Achieving Self-Management via Utility Functions
IEEE Internet Computing
The design and implementation of OGSA-DQP: A service-based distributed query processor
Future Generation Computer Systems
Adaptive workload allocation in query processing in autonomous heterogeneous environments
Distributed and Parallel Databases
Autonomic query parallelization using non-dedicated computers: an evaluation of adaptivity options
The VLDB Journal — The International Journal on Very Large Data Bases
Control Theory: a Foundational Technique for Self Managing Databases
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Performance model driven QoS guarantees and optimization in clouds
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Future Generation Computer Systems
Utility functions for adaptively executing concurrent workflows
Concurrency and Computation: Practice & Experience
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Workload management coordinates access to and use of shared computational resources; adaptive workload execution revises resource allocation decisions dynamically in response to feedback about the progress of the workload or the behavior of the resources. Where the workload contains or consists of database queries, adaptive query processing (AQP) changes the way in which a query is being evaluated while the query is running. In parallel environments, available adaptations may change the allocation of query fragments to a machine, for example to remove load imbalance or change the parallelism level. Most AQP strategies act on individual queries with the objective of reducing response times. However, where adaptations affect the usage of shared resources, or the principal goal is to meet quality of service targets rather than to minimize overall response times, locally beneficial decisions may have globally detrimental effects. This paper describes the use of utility functions to coordinate adaptations that assign resources to query fragments from multiple queries, and demonstrates how a common framework can be used to support different objectives, specifically to minimize overall query response times and to maximize the number of queries meeting quality of service goals. Experiments using simulation compare the use of utility functions with the more common heuristic control strategies, demonstrating situations in which significant benefits can be obtained.