Medusa: an experiment in distributed operating system structure
Communications of the ACM
Computer Communications Network Design and Analysis
Computer Communications Network Design and Analysis
The MuNet: A scalable decentralized architecture for parallel computation
ISCA '80 Proceedings of the 7th annual symposium on Computer Architecture
X-Tree: A tree structured multi-processor computer architecture
ISCA '78 Proceedings of the 5th annual symposium on Computer architecture
The Roscoe distributed operating system
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
StarOS, a multiprocessor operating system for the support of task forces
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
A Large Scale, Homogenous, Fully Distributed Parallel Machine, II
ISCA '77 Proceedings of the 4th annual symposium on Computer architecture
The switching structure and addressing architecture of an extensible multiprocessor: cm*.
The switching structure and addressing architecture of an extensible multiprocessor: cm*.
A framework for problem-solving in a distributed processing environment.
A framework for problem-solving in a distributed processing environment.
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Micros, A Distributed Operating System for Micronet, A Reconfigurable Network Computer
IEEE Transactions on Computers
Cm*: a modular, multi-microprocessor
AFIPS '77 Proceedings of the June 13-16, 1977, national computer conference
Wave Scheduling Decentralized Scheduling of Task Forces in Multicomputers
IEEE Transactions on Computers
Star: A Local Network System for Real-Time Management of Imagery Data
IEEE Transactions on Computers
A portable modula-2 operating system: SAM2S
AFIPS '84 Proceedings of the July 9-12, 1984, national computer conference and exposition
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Efficient task scheduling techniques are needed for microcomputer networks to be used as general purpose computers. The Wave Scheduling technique, developed for the MICRO-NET network computer, co-schedules groups of related tasks onto available network nodes. Scheduling managers are distributed over a logical control hierarchy. They subdivide requests for groups of free worker nodes and send waves of requests towards the leaves of the control hierarchy, where all workers are located. Because requests from different managers compete for workers, a manager may have to try a few times to schedule a task force. Each task force manager actually requests slightly more workers than it really needs. It computes a request size which minimizes expected scheduling overhead, as measured by total idle time in worker nodes. Using a Markov queueing model, it is shown that Wave Scheduling in a network of microcomputers is almost as efficient as centralized scheduling.