2,019 research outputs found
Recommended from our members
PGGA: A predictable and grouped genetic algorithm for job scheduling
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods such as first-come-first-serve (FCFS) and random scheduling, heuristics such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions
Enhanced Cluster Computing Performance Through Proportional Fairness
The performance of cluster computing depends on how concurrent jobs share
multiple data center resource types like CPU, RAM and disk storage. Recent
research has discussed efficiency and fairness requirements and identified a
number of desirable scheduling objectives including so-called dominant resource
fairness (DRF). We argue here that proportional fairness (PF), long recognized
as a desirable objective in sharing network bandwidth between ongoing flows, is
preferable to DRF. The superiority of PF is manifest under the realistic
modelling assumption that the population of jobs in progress is a stochastic
process. In random traffic the strategy-proof property of DRF proves
unimportant while PF is shown by analysis and simulation to offer a
significantly better efficiency-fairness tradeoff.Comment: Submitted to Performance 201
Multi-resource fairness: Objectives, algorithms and performance
Designing efficient and fair algorithms for sharing multiple resources
between heterogeneous demands is becoming increasingly important. Applications
include compute clusters shared by multi-task jobs and routers equipped with
middleboxes shared by flows of different types. We show that the currently
preferred objective of Dominant Resource Fairness has a significantly less
favorable efficiency-fairness tradeoff than alternatives like Proportional
Fairness and our proposal, Bottleneck Max Fairness. In addition to other
desirable properties, these objectives are equally strategyproof in any
realistic scenario with dynamic demand
- …