Web Services Essentials
ACDS: Adapting Computational Data Streams for High Performance
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Staying FIT: efficient load shedding techniques for distributed stream processing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards a streaming SQL standard
Proceedings of the VLDB Endowment
A middleware for context-aware agents in ubiquitous computing environments
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
SystemT: a system for declarative information extraction
ACM SIGMOD Record
Hadoop: The Definitive Guide
Developing context-aware pervasive computing applications: Models and approach
Pervasive and Mobile Computing
Context-aware publish subscribe in mobile ad hoc networks
COORDINATION'07 Proceedings of the 9th international conference on Coordination models and languages
COLA: optimizing stream processing applications via graph partitioning
Middleware'09 Proceedings of the ACM/IFIP/USENIX 10th international conference on Middleware
Fault injection-based assessment of partial fault tolerance in stream processing applications
Proceedings of the 5th ACM international conference on Distributed event-based system
Advanced Topics in Exception Handling Techniques
Advanced Topics in Exception Handling Techniques
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Stream processing applications are deployed as continuous queries that run from the time of their submission until their cancellation. This deployment mode limits developers who need their applications to perform runtime adaptation, such as algorithmic adjustments, incremental job deployment, and application-specific failure recovery. Currently, developers do runtime adaptation by using external scripts and/or by inserting operators into the stream processing graph that are unrelated to the data processing logic. In this paper, we describe a component called orchestrator that allows users to write routines for automatically adapting the application to runtime conditions. Developers build an orchestrator by registering and handling events as well as specifying actuations. Events can be generated due to changes in the system state (e.g., application component failures), built-in system metrics (e.g., throughput of a connection), or custom application metrics (e.g., quality score). Once the orchestrator receives an event, users can take adaptation actions by using the orchestrator actuation APIs. We demonstrate the use of the orchestrator in IBM's System S in the context of three different applications, illustrating application adaptation to changes on the incoming data distribution, to application failures, and on-demand dynamic composition.