Optimization for QoS on Systems with Tasks Deadlines Luis Fernando Orleans Pedro Nuno Furtado

Introduction  QoS in information systems is all about providing guarantees that submitted tasks will execute within an specified amount of time (deadline)  A simple solution to reduce the response time is to add more servers Load-balancing Problem: no concerns about time- constraints

Introduction (2)  Unpredictable behaviour of stressed parallel systems  Possible causes: Load-balancing algorithms without concerns on response time (best-effort) Workload variability

Introduction (3)  Objective: Optimize the performance of stressed QoS parallel systems.  Methodology: Simulation

Some considerations  Hard deadlines x soft deadlines  Generation of tasks duration Exponential distribution Pareto’s distribution (a more real modelling)  Tasks arrival rate: 10 tasks per sec. Exponential distribution

Load-balancing algorithms  Round-Robin Requests are dispatched round-the-table among the servers  Least-Work-Remaining Requests are dispatched to the server with the least outstanding work.

Algorithms with best-effort policy

Proposed solution  Limit the number of concurrent executing (CE) tasks in the system Admission control  Rejection of incoming tasks when the max CE had been reached

Results  Treating the system as an M/M/3/CE Processor Sharing system

Results  Treating the system as an M/P/3/CE Processor Sharing system, where P is a Pareto distribution

Conclusions  In QoS system, the number of CE is a crucial variable  Limiting the value of CE can drastically reduce the number of killed tasks  Task size’s variability has an enormous impact on the system’s performance

Questions?

Thank you!