Aggregate Scheduling – Aggregate Scheduling – Enhancing Throughput in Enhancing Throughput in

Collective Tasking SystemsCollective Tasking Systems

L. Subramanian

Randy H.Katz

Michael J. Franklin

Collective Tasking SystemsCollective 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 SchedulingAggregate Scheduling

Scheduler

List of Queues

Aggregator

OPTDoor

application

bottleneck

Maintainer

List of Queues: A queue of requests for every typeOPT: Aggregate Statistics of requests of every typeDoorkeeper: Triggers event when a new request arrives

Components in an Aggregate Components in an Aggregate Scheduling SystemScheduling System

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 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 SystemNetwork Provisioning System

• 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.

Throughput & Block Rate Throughput & Block Rate CharacteristicsCharacteristics

Response Time CharacteristicsResponse Time Characteristics

ConclusionsConclusions

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