@misc{oai:ir.soken.ac.jp:00003580,
 author = {河井, 裕 and カワイ, ユタカ and KAWAI, Yutaka},
 month = {2016-02-17, 2016-02-17},
 note = {This research seeks to seamlessly support the infrastructure of distributed computing
and storage through the development and study of a software-abstraction
layer that interfaces to multiple Grid middleware and to new Cloud environments.
Through this abstraction it is possible to sustain uninterrupted access to resources
that is robust to the dynamic nature of those resources (compute nodes may fail,
storage resources may go offline while a computation is being performed). We
studied the software-abstraction layer and provided our Universal Grid User Interface
(UGI) architecture for multiple kinds of Grid and Cloud middleware to
support end users and application engineers. UGI is implemented based on A
Simple API for Grid Applications (SAGA) and provides supplemental and extended
functions that are not included in SAGA.
We demonstrated that job submissions can be executed in the UGI-based user
environment with different Grid resources. We provided and verified a simple way
to execute the jobs based on High Energy and Nuclear Physics (HENP) libraries.
For file manipulation, we demonstrated that an application can access the different
file-system middleware in the Data Grids. The application enables to handle
pieces as completed files, even if a large file is cut up and the separated parts are
stored on different Data Grids. We managed the files distributed in heterogeneous
Data Grids by using a catalog service. The example demonstrated that an application
can obtain the location information about the pieces of files distributed among
different kinds of Data Grids, and then access the distributed files.
For applied tools and applications, we demonstrated a method to reliably manage
files with Resource Namespace Service (RNS), a UGI-based Web application
for Particle Therapy Simulation (PTSim), and an approach inspired by Ant Colony
Optimization (ACO). Our method for reliably managing large files works on different
kinds of Data Grids using RNS. The volume of digital data and the size of
an individual file are increasing due to the introduction of high-resolution images,
high-definition audiovisual files, etc. The reliable storage of such large files is becoming
problematic with whole file replication as a failure in the integrity of the
file is difficult to localize. Our method involves managing large files in Data Grids
by splitting them into smaller units in a traceable manner and then managing the
smaller units. The RNS catalog service contains EPR (Endpoint Reference) and
metadata that describe the original locations as well as the checksum values. The
example we shows how our Grid application can retrieve the actual file locations
and the checksum values from the RNS service.
Our second tool is a UGI-based Web application for PTSim. PTSim is a simulation
system for particle therapy. The application of particle physics to the
medical environment is one of the application areas that have a direct benefit to
mankind. PTSim makes use of the Geant4 toolkit to simulate the passage of particles
through the human body. It includes a Web interface that can be used by
several collaborating medical particle therapy centers. The Web interface allows
a non-Grid environment to be easily ported to Grid to take advantage of the additional
resources.
Our last tool is for an approach inspired by swarm intelligence, ACO. Swarm
intelligence is one of approaches to provide a fault tolerant and efficient means of
transferring data in a dynamic environment. Swarm intelligence is inspired primarily
by observations of the collective behavior of social insects in addressing
complex distributed problems. The basic idea is that each member of the swarm
has simple rules that govern its behavior, but the interaction among the members
of the swarm can be used to tackle problems that are difficult to solve with complicated
numeric methods. We investigate the problem of data distribution among
a client and servers in a dynamic environment. We regard each download from a
server to the client as a single member in a swarm. The member’s behavior is simply
to reliably download a data file. Each member can communicate with other
members to allow the swarm to settle on the best set of servers to download the
data from based on the current status of the environment. ACO is one of Swarm
intelligence methods. We created a simulator following the ACO based approach
and showed that our approach works well, providing a fault tolerant and efficient
means of transferring data in a dynamic environment.
We can utilize the computing and storage resources with our implementation
and solution. The challenges of today’s researchers who need to collaborate with
geographically distributed colleagues with distributed computing and storage resources
can be overcome., 総研大甲第1548号},
 title = {A study in resource federation for e-Science},
 year = {}
}