Cost-Minimizing Scheduling of Workflows on a Cloud of Memory Managed Multicore Machines

Authors:
Nicolas G. Grounds;John K. Antonio;Jeff Muehring
Affiliations:
RiskMetrics Group, Norman, USA;School of Computer Science, University of Oklahoma, Norman, USA;RiskMetrics Group, Norman, USA
Venue:
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Year:
2009

Citing 12
Cited 2

Garbage collection can be faster than stack allocation

Information Processing Letters
Multiprocessor Online Scheduling of Hard-Real-Time Tasks

IEEE Transactions on Software Engineering
Garbage collection: algorithms for automatic dynamic memory management

Garbage collection: algorithms for automatic dynamic memory management
A Modified Least-Laxity-First Scheduling Algorithm for Real-Time Tasks

RTCSA '98 Proceedings of the 5th International Conference on Real-Time Computing Systems and Applications
Dynamic Mapping in a Heterogeneous Environment with Tasks Having Priorities and Multiple Deadlines

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Quantifying the performance of garbage collection vs. explicit memory management

OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A new task scheduling method for distributed programs that require memory management: Research Articles

Concurrency and Computation: Practice & Experience
Dynamic Load Balancing in Distributed Systems in the Presence of Delays: A Regeneration-Theory Approach

IEEE Transactions on Parallel and Distributed Systems
Garbage collection-aware utility accrual scheduling

Real-Time Systems
Quantifying and improving the performance of garbage collection

Quantifying and improving the performance of garbage collection
A New CPU Availability Prediction Model for Time-Shared Systems

IEEE Transactions on Computers
Predicting Running Time of Grid Tasks based on CPU Load Predictions

GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing

CAD: an efficient data management and migration scheme across clouds for data-intensive scientific applications

Globe'11 Proceedings of the 4th international conference on Data management in grid and peer-to-peer systems
Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments

The Journal of Supercomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Workflows are modeled as hierarchically structured directed acyclic graphs in which vertices represent computational tasks, referred to as requests, and edges represent precedent constraints among requests. Associated with each workflow is a deadline that defines the time by which all computations of a workflow should be complete. Workflows are submitted by numerous clients to a scheduler that assigns workflow requests to a cloud of memory managed multicore machines for execution. A cost function is assumed to be associated with each workflow, which maps values of relative workflow tardiness to corresponding cost function values. A novel cost-minimizing scheduling framework is introduced to schedule requests of workflows so as to minimize the sum of cost function values for all workflows. The utility of the proposed scheduler is compared to another previously known scheduling policy.