Cloud-Edge Continuum Computing Platform hosts real-time applications over edge and cloud resources. The execution time of real-time applications composed of micro-services is increased by the communication latency, which is the time between a packet entering and leaving the network. To keep the execution time small, a latency-guaranteed network, which determines an upper bound of the latency, will be effective. Motivated by this requirement, we have been enhancing Deterministic Networking (DetNet), a protocol suite that builds a latency-guaranteed network over Wide-Area Networks (WAN).

In the context of open science, the management of metadata is essential for promoting research data utilization. Experimental scientists are required to manage a substantial volume of experimental data, including a significant proportion of failed data. This places a considerable burden on the experimental scientists. In this paper, we outline the development of a conceptual image for a data aggregation and management infrastructure for core facilities. The infrastructure enables an automatic assignment of unique identifier and metadata, optimizing research data management of experimental scientists.

Research must be reproducible to be verifiable. Provenance, which describes how data was produced, is one of the metadata that can improve reproducibility. In this paper, we propose a method to construct the provenance of research data produced in high-performance computing (HPC) systems. Our method can construct a high-level and user-perspective provenance by integrating information available in HPC systems, such as a workload manager, with low-level data about running programs' behavior captured in an operating system kernel. The method enables users of HPC systems to collect the provenance without modifying assets such as programs and scripts.

Cloud bursting functionality provides the administrators of on-premise supercomputing systems with an on-demand way of dynamically integrating the computing resources on the cloud into them when the computing demands from the users dramatically increase. In the Cybermedia Center at Osaka University, we have deployed a cloud bursting environment between a supercomputing system named SQUID and Microsoft Azure by respecting the transparency in usage rather than performance. Technically, the transparency allows users to execute their jobs on the cloud computing resources without having to be aware of the cloud in terms of usage. However, how we encourage users of our supercomputing system to use the cloud computing resources is a realistic and important problem to be solved from a practical operational perspective. In this paper, we investigate the cloud bursting environment on SQUID in terms of performance, monetary cost, and transparency on usage. Finally, we discuss how we as the administrators of on-premise supercomputing systems can provide a better way to encourage users to execute their jobs on the cloud computing resource instead of on the on-premise computing resource under our service policy.

Today, scientific research heavily depends on the digital world. Almost the entire process of scientific research including data acquisition, data analysis, and visualization is now being conducted in the digital world. Also, today' scientific research essentially requires the global collaboration by scientists and IT researchers, each of who works on a different organization. In this paper the authors describe the future vision of eScience as well as challenges and expectations to eScience community.

Recent rapid scale out of high performance computing systems has rapidly and continuously increased the scale and complexity of the interconnects. As a result, current static and over-provisioned interconnects are becoming cost-ineffective. Against this background, we have been working on the integration of network programmability into the interconnect control, based on the idea that dynamically controlling the packet flow in the interconnect according to the communication pattern of applications can increase the utilization of interconnects and improve application performance. Interconnect simulators come in handy especially when investigating the performance characteristics of interconnects with different topologies and parameters. However, little effort has been put towards the simulation of packet flow in dynamically controlled interconnects, while simulators for static interconnects have been extensively researched and developed. To facilitate analysis on the performance characteristics of dynamic interconnects, we have developed PFAnalyzer. PFAnalyzer is a toolset composed of PFSim, an interconnect simulator specialized for dynamic interconnects, and PFProf, a profiler. PFSim allows interconnect researchers and designers to investigate congestion in the interconnect for an arbitrary cluster configuration and a set of communication patterns collected by PFProf. PFAnalyzer is used to demonstrate how dynamically controlling the interconnects can reduce congestion and potentially improve the performance of applications.

Similar to Virtualization, Linux Containers (LXC) provides high-performance, lightweight computing resource allocation and isolation. Each LXC container has a resource overhead smaller than that of a virtual machine, leading to significantly lower container migration time and making frequent container placement modification a viable optimization technique. Traditional container scheduling mechanisms do not leverage this property of LXC. Generally, a scheduler tries to find the most optimal placement for a new container, the allocated host then executes the scheduled container until the end of the container's life cycle. This strategy works fine for short-lived containers. With a long-lived container such as a server process becoming more and more common, and the container placement calculated at the beginning of the execution may not remain optimal during the container's lifetime, since the other containers are moving in and out of the cluster. This research proposes container rebalancing, a novel scheduling mechanism with a rebalancing process working alongside a scheduling process. The container rebalancing method increases LXC cluster utilization while maintaining minimal interference with the scheduling process. This is done by continuously modifying container placement, by using the rebalancing process, in order to load-balance utilization of each host in the LXC cluster. LXC cluster simulation driven by Google's cluster data is used to verify the feasibility of container rebalancing. Simulation results show an observable increase in container scheduled rate and cluster utilization with no drawback, suggesting that container rebalancing is a promising method.

A deep learning technique has emerged as a successful approach for diagnostic imaging. Along with the increasing demands for dental healthcare, the automation of diagnostic imaging is increasingly desired in the field of orthodontics for many reasons (e.g., remote assessment, cost reduction, etc.). However, orthodontic diagnoses generally require dental and medical scientists to diagnose a patient from a comprehensive perspective, by looking at the mouth and face from different angles and assessing various features. This assessment process takes a great deal of time even for a single patient, and tends to generate variation in the diagnosis among dental and medical scientists. In this paper, the authors propose a deep learning model to automate diagnostic imaging, which provides an objective morphological assessment of facial features for orthodontic treatment. The automated diagnostic imaging system dramatically reduces the time needed for the assessment process. It also helps provide objective diagnosis that is important for dental and medical scientists as well as their patients because the diagnosis directly affects to the treatment plan, treatment priorities, and even insurance coverage. The proposed deep learning model outperforms a conventional convolutional neural network model in its assessment accuracy. Additionally, the authors present a work-in-progress development of a data science platform with a secure data staging mechanism, which supports computation for training our proposed deep learning model. The platform is expected to allow users (e.g., dental and medical scientists) to securely share data and flexibly conduct their data analytics by running advanced machine learning algorithms (e.g., deep learning) on high performance computing resources (e.g., a GPU cluster).

Summary The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international community of researchers that actively collaborate to address problems and challenges of common interest in eScience. The PRAGMA Experimental Network Testbed (PRAGMA‐ENT) was established with the goal of constructing an international software‐defined network (SDN) testbed to offer the necessary networking support to the PRAGMA cyberinfrastructure. PRAGMA‐ENT is isolated, and PRAGMA researchers have complete freedom to access network resources to develop, experiment, and evaluate new ideas without the concerns of interfering with production networks. In the first phase, PRAGMA‐ENT focused on establishing an international L2 backbone. With support from the Florida Lambda Rail, Internet2, PacificWave, Japan Gigabit Network, and TaiWan Advanced Research and Education Network, PRAGMA‐ENT backbone connects openflow‐enabled switches at University of Florida, University of California, San Diego, Nara Institute of Science and Technology (Japan), Osaka University (Japan), National Institute of Advanced Industrial Science and Technology (Japan), and National Applied Research Laboratories (Taiwan). The second phase of PRAGMA‐ENT consisted of an evaluation of technologies for the control plane that enables multiple experiments (ie, OpenFlow controllers) to coexist. Preliminary experiments with FlowVisor revealed some limitations leading to the development of a new approach, called AutoVFlow. This paper describes our experience in the establishment of PRAGMA‐ENT backbone (with international L2 links), its current status, and plans for the control plane. Discussion of preliminary application ideas, including optimization of routing control; multipath routing control; extending the backbone using overlay network; and remote visualization are also discussed.

On a cluster system running behind the Cloud computing, multiple processes are generated from most applications and then executed on multiple computing nodes. Their processes communicate with each other during their execution. The communication performance among multiple processes plays an important role in the total execution performance of an application. The SDN-enhanced JMS, which we have developed in the previous work, offers the framework that allows administrators to prescribe resource provisioning way using the information on both computing and network resources on a cluster system. However, an advanced and effective method for providing an appropriate set of computing and network resources to jobs is still an issue to be tackled. In this paper, we aim to design and implement an adaptive network resource allocation method with which network resources can be reallocated to jobs in execution, in response to the change of process placement occurred at job dispatch and termination events. Our evaluation shows that the proposed method can reallocate network resources of jobs in execution, and suppress the degradation of network throughput of jobs.

Recently, researchers' and scientists' interest and concern to Internet of Things (IoT) have been remarkably increasing. A diversity of IoT devices such as mobile phones, sensors and even scientific measurement facilities have been connected to the Internet and then generating an enormous amount of data. From the demands on computational resources enough to analyze such data, the utilization of the cloud has been a major trend in these days. Taking aggregation and distribution of data from and to IoT devices on the cloud into consideration, however, access control to such data gives rise to an important problem. Each of IoT devices may have a security policy and each user may have a different attribute. For achieving safe access control to data, a fully-controlled infrastructure where access to network resources is controlled as well as computational resources is required. From such a consideration, this paper proposes an access-controlled networking mechanism that dynamically organizes a flexible and secure network linking IoT devices, computational resources and users on the cloud, based on user's attribute and IoT device security policies. The architecture of FlowSieve, which we have designed and implemented in this preliminary stage of the research, is presented as well as our envisaged fully access-controlled cloud for secure data access.

With the constant increase in the number and variety of small molecule chemical compounds, drug discovery is becoming a very resource intensive endeavor. Performing molecular simulations of ligand-protein binding by virtual screening has become an integral part of the discovery process. Cloud computing is an efficient choice to execute these large-scale screenings, given that large compute allocations are not accessible to many researchers. This research focused on developing a multi-site cloud environment that combines small allocations of virtual machines in multiple locations connected through a virtual networking system (ViNe), and compared two parallelization approaches: Message Passing Interface (MPI) and MapReduce using Hadoop. Virtual screenings were conducted using DOCK, a protein-ligand molecular interaction simulation program. Multiple DOCK test simulations through MPI and Hadoop were run to assess the performance and flexibility of the environment. These tests indicated that MPI and MapReduce offer comparable scalability performance, and that network latency has a significant influence on low accuracy simulations. Furthermore, differences in performance at individual cloud resource sites were reduced on average because of the larger combined pool of resources. This project prototyped and assessed a fully functional multi-site cloud environment for virtual screenings, which can be used to guide small laboratories in deploying their own cloud-based screenings.

Bandwidth and latency are two major factors that contribute the most to network application performance. Between each pair of switches in a network, there may be multiple paths connecting them. Each path has different properties because of multiple factors. Traditional shortest-path routing does not take this knowledge into consideration and may result in sub-optimal performance of applications and underutilization of network. We proposed a concept of "bandwidth and latency aware routing". The idea is that we could improve overall performance of the network by separating application into bandwidth-oriented and latency-oriented application and allocate different route for each type of application accordingly. We also proposed a design of this network system implemented using Open Flow. Routes are calculated from monitored information using Dijkstra algorithm and its variation. To support our design, we show a use case in which our design performs better than traditional routing as well as evaluation results.

In the era of cloud computing, data centers that accommodate a series of user-requested jobs with a diversity of resource usage pattern need to have the capability of efficiently distributing resources to each user job, based on individual resource usage patterns. In particular, for high-performance computing as a cloud service which allows many users to benefit from a large-scale computing system, a new framework for resource management that treats not only the CPU resources, but also the network resources in the data center is essential. In this paper, an SDN-enhanced JMS that efficiently handles both network and CPU resources and as a result accelerates the execution time of user jobs is introduced as a building block technology for such a HPC cloud. Our evaluation shows that the SDN-enhanced JMS efficiently leverages the fat-tree interconnect of cluster systems running behind the cloud to suppress the collision of communications generated by different jobs.

Recently, the concept of Software-Defined Network (SDN), which allows us to administer and configure a network in a centralized and software-programming manner, has gathered network engineers' and researchers' attention rapidly. In particular, the expectation and concern to OpenFlow as an implementation of the SDN is remarkable. As a result, research activities, which include prototyping, implementation, demonstration and experiments, conducted over OpenFlow networks have been a worldwide tendency. In such research activities, however, the difficulty in understanding network topology, traffic amount and an actual path of a network flow on the OpenFlow network, and the intricacies in debugging software designed for OpenFlow are serious problems in the development process of OpenFlow controller. This research aims to realize a visualization software that facilitates researchers to perform OpenFlow controller development and demonstration experiments performed on an actual OpenFlow network. In this paper, the authors summarize the achievement of their research work in progress as well as the future direction.

Advances in sequencing technology have resulted in an exponential increase in the availability of protein sequence information. In order to fully utilize information, it is important to translate the primary sequences into high-resolution tertiary protein structures. MODELLER is a leading homology modeling method that produces high quality protein structures. In this study, the function of MODELLER was expanded by configuring and deploying it on a parallel grid computing platform using a custom four-step workflow. The workflow consisted of template selection through a protein BLAST algorithm, target-template protein sequence alignment, distribution of model generation jobs among the compute clusters, and final protein model optimization. To test the validity of this workflow, we used the Dual Specificity Phosphatase (DSP) protein family, which shares high homology among each other. Comparison of the DSP member SSH-2 with its model counterpart revealed a minimal 1.3% difference in output energy scores. Furthermore, the Dali Pair wise Comparison Program demonstrated a 98% match among amino acid features and a Z-score of 26.6 indicating very significant similarities between the model and actual protein structure. After confirming the accuracy of our workflow, we generated 23 previously unknown DSP family protein structure models. Over 40,000 models were generated 30 times faster than conventional computing. Virtual receptor-ligand screening results of modeled protein DSP21 were compared with two known structures that had either higher or lower structural homology to DSP21. There was a significant difference (p!0.001) between the average ligand ranking discrepancy of a more homologous protein pair and a less homologous protein pair, suggesting that the protein models generated were sufficiently accurate for virtual screening. These results demonstrate the accuracy and usability of a grid-enabled MODELLER program and the increased efficiency of processing protein structure models. This workflow will help increase the speed of future drug development pipelines.

SUMMARY Hadoop, based on the popular MapReduce framework, is an open‐source distributed computing framework that has been gaining much popularity and usage. It aims to allow programmers to focus on building applications that deals with processing large amount of data, without having to handle other issues when performing parallel computations. However, tuning the performance of Hadoop applications is not an easy task due to the level of abstraction of the framework. In this paper, we present three case studies and some of the challenges and issues that are to be considered in performance tuning when running applications in Hadoop. The focus is mainly on the impact of input data on Hadoop's performance and how they can be tuned. Copyright © 2011 John Wiley & Sons, Ltd.

Universities are constantly searching for ways that prepare students as effective global professionals. At the same time, cyber infrastructure leverages computing, information, and communication technology to perform research, often in an international context. In this paper we discuss a novel model, called the Cyber infrastructure Internship Program (CIP), which serves both of these goals. Specifically, students apply or develop cyber infrastructure to solve challenging research problems, but they do this via summer internships abroad. CIP has been implemented at three different Universities: The University of California San Diego in the US, Osaka University in Japan and and Monash University in Australia. We discuss details of the schemes and provide some initial evaluations of their success.

Domain Name Systems (DNS) servers provide a critical service to users and application on the internet. At the top of the DNS hierarchy is the Root DNS servers providing pointers to Top Level Domain servers on the Internet. Over the past years the number of Root DNS servers has grown to cope with the exponential growth of the Internet. This paper reports on the status and performance of the Root DNS servers gathered by the Gulliver's project SEIL probes which are deployed globally. This is the first effort to carry out long term study, from 2007 till 2009, on the performance of Root DNS servers. The data gathered shows the performance of anycast Root DNS servers in terms of Loss Rate and Query Response Time (QRT). Anycast servers' performance on the whole has been improving with more deployment of its instances, while unicast servers' performance has been roughly the same. We have correlated changes in the QRT to events that has occurred, eg. Cable breaks and deployment of a new anycast instance.

SUMMARY: ViewDock TDW is a modification of the pre-existing ViewDock Chimera extension (http://www.cgl.ucsf.edu/chimera/) used to visualize results of virtual screening experiments. By combing TDW hardware and an enhanced ViewDock interface, dozens of ligand-protein complexes are rendered simultaneously to parallelize the analysis of candidate ligands. The ViewDock TDW GUI allows the user to easily and interactively manipulate the molecules on the TDW as an entire set, a selected subset or a single ligand-protein complex and preserves all Chimera functionality. AVAILABILITY AND IMPLEMENTATION: ViewDock TDW is an open source software; freely available on the web at http://www.tdw-prime.webs.com. Chimera UCSF is also available, free of charge, at http://www.cgl.ucsf.edu/chimera/

Industry widely employs the two-dimensional videoconferencing system as a long distance communication tool, but current limitations such as its tendency to misrepresent eye contact prevent it from becoming more widely adopted. We are exploring the possibility of a three-dimensional videoconferencing system for future interactive streaming of point cloud data, and present the preliminary research results in this paper. We have tested thus far with one sender and one receiver, using pre-recorded data for the sender. The sender, encircled by high-definition cameras, stands and speaks in a room. A cluster of computers reconstructs each frame of the camera images into a 3D point cloud and streams it across a high-speed, low-latency network. On the receiving end, a splat-based renderer employs a new algorithm to efficiently resample the points in real-time, maintaining a user-specified frame rate. Parallel hardware projects onto multiple screens while head tracking equipment records the viewer's movements, allowing the receiver to view a stereoscopic 3D representation of the sender from multiple angles. We can combine these visuals with appropriate use of multiple audio channels to forge an unparalleled virtual experience. This next step towards immersive 3D videoconferencing brings us closer to empowering worldwide collaboration between research departments.

In 2005, Osaka University, in Japan, started an international educational program called, Pacific Rim International University (PRIUS), on top of the Pacific Rim Application and Grid Middleware Assembly (PRAGMA) research framework. The PRIUS framework is based on and similar to that of the PRIME program at the University of California San Diego. Through the PRIUS program, Osaka University has explored a new structure of higher education for graduate students by combining lectures given by PRAGMA researchers and scientists as well as internship abroad opportunities to PRAGMA member institutions and universities. In this paper, we describe the goals and framework of the PRIUS program and discuss issues for the improvement of PRIUS. We also present two examples of interns' achievements as well as other educational effects brought through collaboration with PRAGMA.

SHP-2 is a protein tyrosine phosphatase (PTP) that plays an important role in many cellular functions such as development, growth, and death; thus SHP-2 has been hypothesized to play an important role in various diseases such as diabetes, neurodegeneration, and cancer. The importance of the individual roles of different PTPs is not well understood and this is complicated by the lack of specific inhibitors. In this study, we have utilized the multi-institutional PRAGMA Grid computation resources to virtually screen the ZINC 7 database using virtual docking software DOCK 6.2. Preliminary results suggest several SHP-2 specific inhibitors that can be further tested and validated under laboratory conditions. Complications during these multiple, virtual screenings on the grid as well as potential improvements are also discussed. These findings have future clinical significance in the creation of new drug therapies for the treatment of different diseases.

The slingshot-2 (SSH-2) protein plays a significant role in different cell functions such as growth and movement. SSH-2 is a phosphatase protein that belongs to a unique class of enzymes called dual specificity phosphatases (DSP) that target the phosphothreonine and phosphotyrosine residues of mitogen-activated protein (MAP) kinases, which regulate cell growth. Because of this, it is of great interest to find specific inhibitors of DSPs such as SSH-2. Implementing an in silico platform to screen a sizable pool of chemical compounds against SSH-2 on the grid environment with the molecular docking software DOCK 6, several chemical compounds have been identified as potential inhibitors of SSH-2 activity. The issues of performing routine virtual screenings on the grid and possible improvements are also presented. The most promising inhibitor determined from standard and AMBER DOCK screenings was 2-amino-3-phosphonooxy-propanoic acid, which will be verified with wet bench testing.

Parkinson's disease is a brain disease whose medical treatment and remedy have not been established yet, although a cause of this disease is considered to be the insufficient formation and action of dopamine, which plays a role of great importance in brain activity. For this reason, there is an increasing demand on the clinical database pertaining to Parkinson's disease which multiple hospitals and research institutions can share to accumulate knowledge and know-how on the disease. In this paper, we present a clinical database for Parkinson's disease research and diagnosis which we have preliminary built, especially focusing on the access control and data filtering mechanisms built in hope that medical Grid services are seamlessly integrated to the database in future.

Recent advances in grid infrastructure and middleware development have enabled various types of applications in science and engineering to be deployed on the grid. The characteristics of these applications and the diverse infrastructure and middleware solutions developed, utilized or adapted by PRAGMA member institutes are summarized. The applications include those for climate modeling, computational chemistry, bioinformatics and computational genomics, remote control of instruments, and distributed databases. Many of the applications are deployed to the PRAGMA grid testbed in routine basis experiments. Strategies for deploying applications without modifications, and those taking advantage of new programming models on the grid are explored and valuable lessons learned are reported. Comprehensive end to end solutions from PRAGMA member institutes that provide important grid middleware components and generalized models of integrating applications and instruments on the grid are also described.

We demonstrate an oral scientific simulation and its visualization based on E-science. This oral scientific application will become an essential key component for medical and dental clinic in the near future because Bio-Medical simulations will provide a clinical index considering a prognostic of a disease. In this case, it was shown that the physical theory of sound production of speech sound, sibilant. However, it is difficult to acquire the computational and storage resources in the hospitals. Our E-science infrastructure enables scientists and clinicians to achieve the advanced information produced by simulations. As the result of this phase implementation for Bio-Medical simulation on E-science infrastructure, we could extract the scientific findings about the oral science. Moreover, this infrastructure can be used more generally because of the divided architecture between applications and E-science infrastructure.

The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testbed resources are presented along with their graphical visualization. Copyright © 2005 John Wiley & Sons, Ltd.

For the effective and early diagnosis of brain diseases, we have developed an evaluation system for brain function using an Independent Component Analysis (ICA) method. This evaluation system benefits greatly from the newly emerged Grid. To embody a Grid environment, a Globus grid toolkit has been utilized as a building block. In this research we have distributed the computational workload for the ICA on a Globus based Grid environment composed of two Alpha cluster systems and a personal computer. In addition, in order to allow scientists and application developers to easily build the Grid-enabled system, we also have adopted a Grid-enabled-Message Passing Interface, called MPICH-G. An introduction of MPICH-G to the medical analysis system on a Grid environment makes it possible for a naïve user to realize rapid analysis without special knowledge of the Grid. Magnetoencephalography (MEG), a highly sophisticated medical technology, is used for the measurement of brain function. The proposed method has the ability to integrate various geographically distributed resources and to analyze functional brain data from MEG

A method of evaluating brain function using the metacomputer concept of the Globus system combined with a message-passing interface is described. The proposed method has the ability to exploit various geographically distributed resources and parallel computing linked to a high-technology medical instrumentation system, magnetoencephalography, to analyze the functional state of the brain. It is envisaged that the method will lead to the realization of an efficient telemedicine system for health care.