Enhancing Throughput with Aggregate Scheduling in Collective Tasking Systems

1. Aggregate Scheduling – Enhancing Throughput in Collective Tasking Systems L. Subramanian Randy H.Katz Michael J. Franklin

2. Collective 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

3. Aggregate Scheduling 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

4. Components in an Aggregate Scheduling 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

5. 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)

6. Network 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.

7. Throughput & Block Rate Characteristics

8. Response Time Characteristics

9. Conclusions • 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