Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
ACM Computing Surveys (CSUR)
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Computation offloading to save energy on handheld devices: a partition scheme
CASES '01 Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for embedded systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Computation hierarchy for in-network processing
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Automatic Partitioning: Prototyping Ubiquitous-Computing Applications
IEEE Pervasive Computing
An Adaptive Multi-Constraint Partitioning Algorithm for Offloading in Pervasive Systems
PERCOM '06 Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications
Scalable, Distributed, Real-Time Map Generation
IEEE Pervasive Computing
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Task partitioning for multi-core network processors
CC'05 Proceedings of the 14th international conference on Compiler Construction
Language-based optimisation of sensor-driven distributed computing applications
FASE'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Fundamental approaches to software engineering
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Many pervasive inter-vehicular applications involve the collation, processing and summarisation of sensor data originating from vehicles. When and where such processing takes place is an explicit design-stage decision. Often some processing occurs on vehicles, and some on backend servers, but it is hard for the programmer to optimise this distribution for feasibility or performance. This paper investigates automated task assignment: we define a computational model which captures data aggregation and summarisation explicitly, allowing a compiler to automatically optimise the assignment of processing tasks to particular vehicles and servers. Our model allows a compiler to apply program transformations to data processing, which can further improve task assignment.