Aggregate scheduling enhancing throughput in collective tasking systems
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Aggregate Scheduling – Enhancing Throughput in Collective Tasking Systems . L. Subramanian Randy H.Katz Michael J. Franklin. Collective Tasking Systems. Properties :- Services requests of a predefined set of types Every request has an associated type

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Aggregate scheduling enhancing throughput in collective tasking systems

Aggregate Scheduling – Enhancing Throughput in Collective Tasking Systems

L. Subramanian

Randy H.Katz

Michael J. Franklin


Collective tasking systems
Collective Tasking Systems Tasking Systems

  • Properties :-

    • Services requests of a predefined set of types

    • Every request has an associated type

    • All requests of a particular type can be aggregated into a single request

    • Bottleneck operation of every type is performed only once for all requests of that type

  • Examples:-

    • Broadcast disks – application of broadcast scheduling.

    • Reservation systems – access to the reservation database

    • Network Provisioning systems – bandwidth brokers

    • Front-end Database monitors –access point for multiple databases

    • Disk scheduling systems –locality based access in disks

    • Caching Systems

    • Gang Scheduling – Multiprocessor systems


Aggregate Scheduling Tasking Systems

Scheduler

application

bottleneck

List of Queues

OPT

Door

Maintainer

Aggregator

List of Queues: A queue of requests for every type

OPT: Aggregate Statistics of requests of every type

Doorkeeper: Triggers event when a new request arrives


Components in an aggregate scheduling system
Components in an Aggregate Scheduling System Tasking Systems

  • Aggregator:

  • Aggregates requests into types

  • Updates OPT data structure

  • Informs Maintainer about new event

  • Scheduler:

  • Computes the type with maximum value of OPT function

  • Computes Aggregate request for all requests of that type

  • Schedules that type to the application

  • Maintainer:

  • Uses an optimization function for types

  • Maintains the invariant property of OPT for new events

  • OPT:

  • Data Structure optimized for the optimization metric

  • Every optimization metric induces an invariant in OPT


Optimization metrics
Optimization Metrics Tasking Systems

  • RxW scheduling

    • (#of Requests) * (Max Waiting Time)

  • Approximate RxW

    • Apply RxW for reduced set of types

  • Kinetic Tournaments

    • Total waiting time for requests in a queue

  • Gang Scheduling

    • Associate distance metric between processes (frequency of IPC)

    • Schedule group of processes with min value of max distance

  • The Cost Dimension

    • Cost associated with every type (cost of bottleneck operation)

    • Costs can be dynamic (eg. disk scheduling)

    • Fagin’s work on fuzzy systems

  • Other variants

    • Bounded queue size (admission control)

    • Bounded response time (earliest deadline)


Network Provisioning System Tasking Systems

  • 12 basic domains in AT&T’s backbone

  • 10% of bandwidth reserved(statistically) for VoIP and VPNs.

  • A provisioning system accepts inter-domain requests and reserves along a path.

  • All requests between a pair of domains are aggregated into a single request.

  • Regulate traffic for the reserved portion.




Conclusions
Conclusions Tasking Systems

  • RxW and Kinetic tournaments give much better performance than FIFO

  • RxW vs Kinetic Tournaments(KT)

    • RxW has slightly higher throughput than KT

    • KT has much lesser response time at operating range

    • Variation of response time in KT is restricted

    • Max response time of KT is very low (6 times)

    • RxW has starvation problem

  • Experiment aggregate scheduling for other collective tasking systems


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