Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds

2014 ◽  
Author(s):  
In Kee Kim ◽  
Jacob Steele ◽  
Yanjun Qi ◽  
Marty Humphrey
Keyword(s):  
Resource Management ◽  
Scientific Applications

Effective Distributed Supercomputing Resource Management for Large Scale Scientific Applications

2015 ◽  
Vol 42 (5) ◽  
pp. 573-579
Author(s):  
Seungwoo Rho ◽  
Jik-Soo Kim ◽  
Sangwan Kim ◽  
Seoyoung Kim ◽  
Soonwook Hwang
Keyword(s):  
Resource Management ◽  
Large Scale ◽  
Scientific Applications

scholarly journals Optimized Scheduling Approach for Scientific Applications Based on Clustering in Cloud Computing Environment

2019 ◽  
Vol 20 (3) ◽  
pp. 527-540
Author(s):  
Walid Kadri ◽  
Belabbas Yagoubi
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Clustering Algorithm ◽  
Virtual Machines ◽  
Directed Acyclic Graphs ◽  
Resource Provisioning ◽  
Scientific Applications ◽  
Scheduling Policies ◽  
Use Of Resources ◽  
The Impact

Cloud Computing refers to the use of the computing capabilities of remote computers, where the user has considerable computing power without having powerful units. Scientific applications, usually represented as Directed Acyclic Graphs (DAGs), are an important class of applications that lead to challenging problems for resource management in distributed computing. With the advent of Cloud Computing, particularly the IaaS offers for on demand virtual machines leasing, multiple jobs execution, consisting of a large number of DAGs, needs an elaborated scheduling and resource provisioning policies, for efficient use of resources. Only few works exists that consider this problem in the context of clouds environment. In goal of optimization and fault tolerance, DAGs applications are generally partitioned into multiple parallel DAGs using clustering algorithm and assigned to VM (Virtual Machine) resources independently. In this work, we investigate through simulation, the impact of clustering for both provisioning and scheduling policies in the total makespan and financial costs for execution of user's application. We implemented four scheduling policies well-known in grid computing systems, and adapted clustering algorithm to our resource management policy that leases and destroys dynamically VMs. We show that dynamic policies can achieve equal or even better performance than static management policies.

scholarly journals Porting of Scientific Applications to Grid Computing on GridWay

2005 ◽  
Vol 13 (4) ◽  
pp. 317-331 ◽  
Author(s):  
J. Herrera ◽  
E. Huedo ◽  
R.S. Montero ◽  
I.M. Llorente
Keyword(s):  
Genetic Algorithm ◽  
Resource Management ◽  
Rapid Development ◽  
Management Systems ◽  
Scientific Applications ◽  
Grid Environment ◽  
Distributed Resource Management ◽  
Programming Paradigm ◽  
Bioinformatics Application ◽  
Management Application

The expansion and adoption of Grid technologies is prevented by the lack of a standard programming paradigm to port existing applications among different environments. The Distributed Resource Management Application API has been proposed to aid the rapid development and distribution of these applications across different Distributed Resource Management Systems. In this paper we describe an implementation of the DRMAA standard on a Globus-based testbed, and show its suitability to express typical scientific applications, like High-Throughput and Master-Worker applications. The DRMAA routines are supported by the functionality offered by the GridWay2framework, which provides the runtime mechanisms needed for transparently executing jobs on a dynamic Grid environment based on Globus. As cases of study, we consider the implementation with DRMAA of a bioinformatics application, a genetic algorithm and the NAS Grid Benchmarks.

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