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Highlights • Thymic basaloid carcinoma is one of the rarest subtypes of thymic carcinoma. • To our knowledge, this is the first case report of thymic basaloid carcinoma concurrent another carcinoma. • Aggressive treatment including surgery should be considered aiming at radical cure., Introduction Thymic basaloid carcinoma is rare, as only about 40 reports have described it since the initial report. Thymoma and thymic carcinomas increase the risk of other malignancies, but concurrent thymic basaloid carcinoma and another malignancy has not been reported. We presented a rare case of thymic basaloid carcinoma with rectal carcinoma. Case presentation Computed tomography revealed an anterior mediastinal mass and rectal wall thickening, and colonoscopy identified a rectal type 2 tumor in a 68-year-old man. Total thymectomy via a median sternotomy was performed, and the thymic tumor was histopathologically confirmed as stage II thymic basaloid carcinoma. Subsequent laparoscopic low anterior resection indicated stage IIIa rectal carcinoma. Adjuvant chemotherapy was administered for the rectal cancer. Discussion Concurrent thymic and extrathymic tumors is rare condition. There are few reports of thymic basaloid carcinoma, and it is unclear whether this tumor, like common thymoma, increase the risk of extrathymic malignancies. Further studies in more patients are needed to elucidate the nature of this tumor. Conclusion To our knowledge, this is the first case report of thymic basaloid carcinoma concurrent another carcinoma. Aggressive treatment including surgery should be considered aiming at radical cure.

Multi-path TCP (MPTCP) is a network protocol that has the potential to enhance network performance, which is crucial in grid computing systems to improve the overall system's performance. It utilizes multiple network paths between machines to achieve a high network performance. This protocol can also improve the connectivity of multiple servers that may be in different data centers by combining multiple network paths. One of the main issues of MPTCP is the path for ACK (acknowledgment) packets, which take the same path where original packets come from. When networks have different round trip times (RTTs), MPTCP may dispatch the ACK packet through a longer path, which is inefficient. HayACK solves this issue by selecting the path with the least RTTs. However, HayACK may suffer from inconsistent packets because of middleboxes, which are intermediary devices between the source and the destination that process network packets. These middleboxes may discard MPTCP packets because of inconsistent sequence numbers of HayACK packets, which is a product of HayACK's path selection. This study aims to investigate the usefulness of HayACK for high-performance data transfer when using multiple paths in real network environments and the influence of middleboxes on HayACK. Our investigation was conducted using various network paths by preparing many client hosts with different IP addresses and one server. The influence of middleboxes is determined by checking the probe packets passed through, changed, or discarded. Assuming the HayACK response, we crafted packets such as TCP SYN packets and TCP ACK packets.

Conventional TCP congestion control algorithms, such as newReno and CUBIC, were designed to avoid bandwidth collapse at times of network congestion. However, the different network scenarios and various network applications associated with congestion would make it difficult to maintain TCP throughput during this time. Thus, we focus on how a recurrent neural network (RNN)-based TCP throughput prediction method can improve throughput under the conditions of congestion to solve this problem. Our proposed method involves training an RNN on the metrics of a TCP connection to predict the TCP throughput. We prepared eight groups of metrics for training, and applied three types of RNNs (i.e., simple-RNN, gated recurrent unit (GRU), and long short-term memory (LSTM)) in our method design. Under our congestion scenarios, a combination of metrics can contribute to improve the throughput. The throughput predicted via our method indicates that it performs better than the TCP CUBIC algorithm. Furthermore, the GRU-based predictive model demonstrated the best performance, achieving 35% higher throughput than the TCP CUBIC algorithm.

The selection of data reduction schemes, which is crucial for reduced data footprints on a distributed file system (DFS) and transferring big data, is usually limited to the schemes supported by the underlying platforms. If the source code of the platform is available, it might be possible to add user-favorite reduction schemes, but it requires an expensive implementation cost or is virtually impossible. In this paper, we propose a system design that links a DFS to reduction schemes and enables them transparently to data processing applications. To verify the feasibility of this approach, we implemented a framework within Hadoop DFS (HDFS) named Hadoop Data Reduction Framework (HDRF). The features of HDRF is threefold. First, the application programmers can easily incorporate their favorite reduction schemes with reasonably restrained implementation costs. Second, the selection is transparent to data processing applications. Lastly, experimental results show that HDRF can halve the vanilla HDFS transfer time by using a more optimized application without compromising the compression ratio. When running the same application, HDRF has minimal runtime and storage overheads compared to vanilla HDFS.

Fast-growing cloud computing has a mass impact on power consumption in datacenters. In our previous study, we presented a power-saving method for cloud storage systems, where the stored data were periodically rearranged in a disk array in the order of access frequency. The disk containing unpopular files can often be switched to power-saving mode, enabling power conservation. However, if such unpopular files became popular at some point, the disk containing those files spin up that leads to increase power consumption. To remedy this drawback, in this paper, we present a multi-tier power-saving method for cloud storage systems. The idea behind our method is to divide the disk array into multiple tiers. The first tier containing popular files is always active for fast response, while lower tiers pack unpopular files for power conservation. To maintain such a hierarchical structure, files are periodically migrated to the neighboring tiers according to the access frequency. To evaluate the effectiveness of our proposed method, we measured the performance in simulations and a prototype implementation using real access traces of approximately 60,000 time-series images with a duration of 3,000 h. In the experiments, we observed that our method consumed approximately 22% less energy than the system without any file migration among disks. At the same time, our method maintained a preferred response time with an overall average of 86 ms based on our prototype implementation.

// Shoki Sato 1 , Toru Nakamura 1 , Toyomasa Katagiri 2 , Takahiro Tsuchikawa 1 , Toshihiro Kushibiki 1 , Kouji Hontani 1 , Mizuna Takahashi 1 , Kazuho Inoko 1 , Hironobu Takano 1 , Hirotake Abe 1 , Shintaro Takeuchi 1 , Masato Ono 1 , Shota Kuwabara 1 , Kazufumi Umemoto 1 , Tomohiro Suzuki 1 , Osamu Sato 1 , Yusuke Nakamura 3 and Satoshi Hirano 1 1 Department of Gastroenterological Surgery II, Hokkaido University Graduate School of Medicine, Sapporo, Japan 2 Division of Genome Medicine, Institute for Genome Research, Tokushima University, Tokushima, Japan 3 Department of Medicine and Surgery, The University of Chicago, Chicago, IL, USA Correspondence to: Toru Nakamura, email: torunakamura@med.hokudai.ac.jp Keywords: C16orf74; pancreatic ductal adenocarcinoma; cell-permeable peptide; molecular target therapy Received: May 04, 2017 Accepted: July 26, 2017 Published: October 19, 2017 ABSTRACT Background: Chromosome 16 open reading frame 74 ( C16orf74 ) is highly expressed in pancreatic ductal adenocarcinoma (PDAC) and is involved in cancer cell proliferation and invasion through binding to calcineurin (CN). Therefore, C16orf74 is a good target for the development of a PDAC treatment. A cell-permeable dominant-negative (DN) peptide that can inhibit the C16orf74/CN interaction was designed to examine whether this peptide can inhibit PDAC cell proliferation in vitro and in vivo . Method: TheDN-C16orf74 peptide, which corresponds to the portion of C16orf74 that interacts with CN, was synthesized, and we assessed its anti-tumor activity in proliferation assays with human PDAC cells and the underlying molecular signaling pathway. Using an orthotopic xenograft model of PDAC, we treated mice intraperitoneally with phosphate-buffered saline (PBS), control peptide, or DN-C16orf74 and analyzed the tumor-suppressive effects. Result: DN-C16orf74 inhibited the binding of C16orf74 to CN in an immunoprecipitation assay. DN-C16orf74 suppressed PDAC cell proliferation, and the level of suppression depended on the expression levels of C16orf74 in vitro . DN-C16orf74 also exhibited anti-tumor effects in orthotopic xenograft model. Furthermore, the tumor-suppressive effect was associated with inhibition of the phosphorylation of Akt and mTOR. Conclusion: The cell-permeable peptide DN-C16orf74 has a strong anti-tumor effect against PDAC in vitro and in vivo .

To empower scientists who are engaged in nation-wide or global-scale collaborative projects for scientific discovery, a large amount of scientific data needs to be visualized and then shared among the scientists. Tiled Display Wall?(TDW) has been widely accepted and used for visualization of large-scale scientific data. Scalable Adaptive Graphics Environment?(SAGE) has received attention from scientists as a middleware that organizes multiple display monitors into a network-aware large display monitor. Using a SAGE TDW, scientists can display multiple visualized contents on a single display monitor, each of which can be located at geographically distant site managed by other organizations. However, SAGE does not have a mechanism for managing multiple visualized data streams heading to a single TDW. In a conventional network, data flows for a same destination tend to share a same link, resulting in drop of packets and therefore poor visual quality. Moreover, because of the flexible nature of SAGE, rate of each visual data flow may change dynamically as a result of user interaction on a TDW, such as moving and resizing an application window. For the reason above, we propose and develop a dynamic route allocation method that switches packet flows onto network links where better performance is expected, in response to user interaction such as window movement and resizing. Technically, we have leveraged OpenFlow, an implementation of Software Defined Networking?(SDN), to integrate network programmability into SAGE. In this paper, we show how SAGE enhanced with the proposed method succeeded in avoiding network congestion and improving the quality of visualization on the TDW over the wide area OpenFlow network on the Internet. SAGE is a TDW middleware that can receive multiple streams from multi site.Dynamic changes of network traffic caused by user-interaction in SAGE.We modified SAGE with dynamic routing functionality triggered by user interaction.Dynamic routing functionality is implemented using OpenFlow.It aims to avoid network congestion for keeping high quality visualization.

Prompt acknowledgment (ACK) of received packets is key to achieving high throughputs in the Transmission Control Protocol (TCP) data flows. This also applies to Multipath TCP (MPTCP), a variant of TCP that bundles multiple paths into a single logical flow. However, the original MPTCP takes advantage of multiple paths only when sending data packets, not ACK packets. In the original MPTCP, an ACK packet goes through the same path that its originating packet has come through, even if a better path is available. The path that the originating packet came through is not always an appropriate path because the characteristics of the path (RTT, bandwidth, etc.) can be diverse. However, it is possible to improve the promptness of ACKing if we can select the appropriate path that an ACK packet goes through, regardless of what path the original data packets came through. In this study, we propose a method for improving MPTCP throughputs by improving the promptness of receiving ACK. The method is referred to as HayACK. HayACK returns an ACK packet via a path with the lowest round-trip time (RTT), even if it is not a path that received a data packet. To examine the effectiveness of our proposal, we conducted experiments in Linux using our modified version of a MPTCP protocol stack. The evaluation results show that HayACK can improve MPTCP throughputs by a maximum of 1.74x, and by 1.52x on average.

Ephrin receptor A4 (EphA4) is overexpressed in human pancreatic adenocarcinoma (PDAC) and activate cell growth. Recent studies have identified small molecules that block EphA4. In this study, we investigated the correlation between EphA4 expression and the prognosis of patients with PDAC. We also examined the cytostatic efficacy of 2,5-dimethylpyrrolyl benzoic acid (compound 1), a small molecule that blocks EphA4, in PDAC cells. Overall survival of patients with EphA4 positivity was significantly shorter than that of patients with EphA4 negativity (P = 0.029). In addition, multivariate analysis revealed that EphA4 expression was an independent prognostic factor in PDAC patients (P = 0.039). Compound 1 showed a cytostatic efficacy in PDAC cells expressing EphA4 in vitro and in vivo. Our study indicated that compound 1 suppressed both EphA4 and Akt phosphorylations, and induced apoptosis in PDAC cells expressing EphA4. In conclusion, compound 1 has a high potential as a therapeutic agent for patients with PDAC.

Supplementary data for this article are available at Cancer Research Online, Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic malignancies. PDAC builds a tumor microenvironment that plays critical roles in tumor progression and metastasis. However, the relationship between chemotherapy and modulation of PDAC-induced tumor microenvironment remains poorly understood. In this study, we report a role of chemotherapy-derived inflammatory response in the enrichment of PDAC microenvironment with immunosuppressive myeloid cells. Granulocyte macrophage colony-stimulating factor (GM-CSF) is a major cytokine associated with oncogenic KRAS in PDAC cells. GM-CSF production was significantly enhanced in various PDAC cell lines or PDAC tumor tissues from patients after treatment with chemotherapy, which induced the differentiation of monocytes into myeloid-derived suppressor cells (MDSC). Furthermore, blockade of GM-CSF with monoclonal antibodies helped to restore T-cell proliferation when cocultured with monocytes stimulated with tumor supernatants. GM-CSF expression was also observed in primary tumors and correlated with poor prognosis in PDAC patients. Together, these results describe a role of GM-CSF in the modification of chemotherapy-treated PDAC microenvironment and suggest that the targeting of GM-CSF may benefit PDAC patients' refractory to current anticancer regimens by defeating MDSC-mediated immune escape., http://cancerres.aacrjournals.org/content/75/13/2629.figures-only

In the field of social science, a variety of high-performance computing simulations such as the Monte Carlo simulation and the Multi-agent simulation must be efficiently performed to deal with social scientific big data. To facilitate social scientists in performing their own analysis against such big data, the information infrastructure for social science must be equipped with a core technology that efficiently and effectively leverages limited resources available on the information infrastructure. From such a perspective, a new type of job management technology, which treats not only computational resources such as the Central Processing Unit (CPU) and memory, but also network resources unlike traditional job management, is investigated in this paper. A cluster system with a fat-tree topology interconnect is conventional cluster architecture these days. For this investigation, the National Aeronautics Space Administration Advanced Supercomputing, USA (NAS) Parallel Benchmarks, which contain computation patterns often observed in social scientific simulations, are used to assess the efficacy of the resource allocation by our proposed job management technology on a cluster system with a fat-tree topology interconnect.

We introduce our research project aimed at developing a last-mile public transportation system with autonomous vehicles. In particular, we focus attention on traffic control techniques. Our vehicles can form fleets of autonomous vehicles, each of which consists of a lead vehicle driven manually and some driverless vehicles running behind another vehicle. In our system, the transport requests of all passengers are aggregated in real time to a central management server, and according to the information received, an optimized operation schedule is dynamically determined. Based on this system, we present a technique that by reorganizing vehicles does not require passengers to transfer to other fleets. In addition, to ensure fault tolerance of the system against failure of the central server, we propose a method to automatically switch to a provisional operation schedule for continuing transportation service. We also describe a scheme to implement our vehicle operation management adopting techniques of parallel computing and virtualization.

A 59-year-old woman was noted to have an anterior mediastinal mass on computed tomography at a regular follow-up 13 years after initial surgery for left breast cancer. Magnetic resonance imaging showed an anterior mediastinal mass. A total thymectomy with excision of surrounding lymphoid tissue was performed. Pathological examination of the resected tumor confirmed the diagnosis of thymic metastasis from breast cancer. The patient has been alive for 6 years after thymectomy. Metastases to the thymus are rare, but long-term survival can be expected with appropriate treatment.

The ability of tumor cells to escape immune destruction and their acquired resistance to chemotherapy are major obstacles to effective cancer therapy. Although immune checkpoint therapies such as anti-PD-1 address these issues in part, clinical responses remain limited to a subpopulation of patients. In this report, we identified IL34 produced by cancer cells as a driver of chemoresistance. In particular, we found that IL34 modulated the functions of tumor-associated macrophages to enhance local immunosuppression and to promote the survival of chemoresistant cancer cells by activating AKT signaling. Targeting IL34 in chemoresistant tumors resulted in a remarkable inhibition of tumor growth when accompanied with chemotherapy. Our results define a pathogenic role for IL34 in mediating immunosuppression and chemoresistance and identify it as a tractable target for anticancer therapy. Cancer Res; 76(20); 6030–42. ©2016 AACR.

In this paper, we propose a method for the dynamic migration of the IP routing points using binary particle swarm optimization (PSO) in distributed Virtual LAN environments. Virtual LAN (VLAN) is a virtualization technique of datalink layer and can construct arbitrary logical networks on top of a physical network. However, VLAN often causes much redundant traffic due to mismatch between the topology of the logical network and that of the underlying physical network. We will show that the proposed method can adaptively select the routing points dynamically according to the observed traffic patterns and thus reduce the redundant traffic. Finally, we will evaluate the proposed method using the simulation environment.

Network testbeds have been used for network measurement and experiments. In such testbeds, resources, such as CPU, memory, and I/O interfaces, are shared and virtualized to maximize node utility for many users. A few studies have investigated the impact of virtualization on precise network measurement and understood Internet traffic characteristics on virtualized testbeds. Although scheduling latency and heavy loads are reportedly affected in precise network measurement, no clear conditions or criteria have been established. Moreover, empirical-statistical criteria and methods that pick out anomalous cases for precise network experiments are required on userland because virtualization technology used in the provided testbeds is hardly replaceable. In this paper, we show that ‘oversize packet spacing’, which can be caused by CPU scheduling latency, is a major cause of throughput instability on a virtualized network testbed even when no significant changes occur in well-known network metrics. These are unusual anomalies on virtualized network environment. Empirical-statistical analysis results accord with results at previous work. If network throughput is decreased by the anomalies, we should carefully review measurement results. Our empirical approach enables anomalous cases to be identified. We present CPU availability as an important criterion for estimating the anomalies.

Cancer vaccines serve as a promising clinical immunotherapeutic strategy that help to trigger an effective and specific antitumor immune response compared to conventional therapies. However, poor immunogenicity of tumor cells remains a major obstacle for clinical application, and developing new methods to modify the immunogenicity of tumor cells may help to improve the clinical outcome of cancer vaccines. 4T1 mouse breast cancer cell line has been known as poorly immunogenic and highly metastatic cell line. Using this model, we identified a sub cell line of 4T1-designated as 4T1-Sapporo (4T1-S)-which shows immunogenic properties when used as a vaccine against the same line. In 4T1-S-vaccinated mice, subcutaneous injection of 4T1-S resulted in an antitumor inflammatory response represented by significant enlargement of draining lymph nodes, accompanied with increased frequencies of activated CD8 T cells and a subpopulation of myeloid cells. Additionally, 4T1-S vaccine was ineffective to induce tumor rejection in nude mice, which importantly indicate that 4T1-S vaccine rely on T cell response to induce tumor rejection. Further analysis to identify mechanisms that control tumor immunogenicity in this model may help to develop new methods for improving the efficacies of clinical cancer vaccines.

High performance computing is required for Big Science application because the proliferation and huge amount of scientific data that needs to be analyzed is a serious problem. Traditionally, network resources were generally assumed as a static resource users cannot control on demand. By integrating network programmability to every stage of a scientific workflow, this study explores a next-generation high performance computing infrastructure where both computational and network resources are flexibly sliced and efficiently leveraged based on the resource requirements of the scientific applications. Technically, Software Defined Networking has been adopted as a key technology for this purpose. In this paper the concept and goals of a next-generation high performance computing infrastructure is introduced and the current status of our research is discussed.

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.

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.

Collective communication is of great importance in MPI because the execution time of an MPI program is affected by the communication performance it can gain. Particularly these days, when a cluster system composed of multiple computing nodes has become dominant as a large-scale computing system, the execution time of collective communication affects the total execution time of the MPI program. However, in many implementations of MPI, collective communication is developed to make use of unicast-based communication in a repeated and combined way, which may result in inefficient communication. In this paper, we explore the use of a Software-Defined Network, which was originally expected to help network administrators operate networks through central control in a software-programming manner, to accelerate MPI_Bcast, a basic collective communication used in MPI. The evaluation in this paper indicates that our prototyped SDN_MPI_Bcast is superior to MPI_Bcast in OpenMPI in communication performance. Also, the evaluation implies that SDN_MPI_Bcast is feasible.

Network performance in high-performance computing environments such as supercomputers and Grid systems takes a role of great importance in deciding the overall performance of computation. However, most Job Management Systems (JMSs) available today, which are responsible for managing multiple computing resources for distribution and balancing of a computational workload, do not consider network awareness for resource management and allocation. In this paper, the authors briefly overview our proposed and prototyped network-aware JMS that can allocate an appropriate set of computing and network resources to a job request. Also, we evaluate the usefulness and effectiveness of our proposal. Experiments conducted with the prototype implementation imply that our proposed network-aware JMS could reduce job execution time by 23.4 percent.

Despite the development of virtualization technologies in computing resources, how best to build virtual networks is still an open issue. Virtual network is an important technology that can hide physical network topologies and policies and provide a dedicated virtual computing environment for each user. However, it is difficult to figure out the actual mapping between a physical network and a virtual network from a logical point of view; the difficulty will cause performance degradation in the virtual network and inefficient network routing. We have therefore proposed a network performance-aware routing mechanism using SDN technologies. By using SDN technology, we can control the network in a centralized manner. Our proposed system automatically aggregates the information on network performance, and then defines an appropriate routing policy by solving optimal path problems based on the observed network performance. We have improved the performance of the inter-domain network for a multisite virtual cluster with the proposed system. We have also started to deploy the proposed system into an actual environment consisting of different multiple organizations.

This paper describes a framework for developing mobile software robots by using the PLANET mobile object system, which is characterized by a language-neutral layered architecture, the native code execution of mobile objects, and asynchronous object passing. We propose an approach to implementing mobile Web search robots that takes full advantage of these characteristics, and we base our discussion of its effectiveness on experiments conducted in the Internet environment. The results show that the PLANET approach to mobile Web search robots significantly reduces the amount of data transferred via the Internet and that it enables the robots to work more efficiently than the robots in the conventional stationary scheme whenever nontrivial amounts of HTML files are processed.

Whether to be able to efficiently utilize the interconnect in a cluster system is a key factor in deciding the computational performance especially for a class of jobs that require intensive communications between processes. However, most of Job Management Systems (JMSs), which are deployed on cluster systems for load-balancing, do not have any mechanism that takes the pattern and requirements of communication occurred in a job into consideration, regardless of potential large impact on its performance. In this research, we explore a novel JMS that can assign computing resources to jobs submitted to the JMS from a standpoint of efficient use of both network and processor resource. More technically, in this paper, a JMS that integrates the network programming functionality of OpenFlow as Software Defined Network is proposed and discussed towards better and more efficient allocation of computing and network resources.

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.

The use of virtual cluster for High Performance Computing (HPC) work has a great benefit of hiding the complexity of physical infrastructure while providing a familiar HPC environment for users. Nevertheless, setting up one is still a challenging task for most users. In addition, the ability to span a virtual cluster across a multi-site cloud can even create a great potential for users to harness a much higher processing power from the cloud. In this paper, the development of a multi-site virtual cluster cloud is presented. This is done by providing the cloud infrastructures tailored specifically for virtual cluster deployment along with related utilities such as web-interface monitor and command line tools. To realize this infrastructure, an overlay network is created using Virtual Private Network (VPN) technology to merge every site into a single broadcast domain. Moreover, a Multi-tier VPN network topology is proposed to enhance the communication performance among the multi-site cloud. A smart virtual machine scheduling mechanism is introduced to reduce performance degradation caused by network delays. The experimental results show that the virtual cluster works well with highly parallel task with low communication among task. Thus, this technology can be used to support many classes of important HPC applications.

In today's datacenter networks, TCP protocol is still used in most of the network traffic. On the other hand, TCP protocol can also result in severe goodput collapse in a high bandwidth and low latency datacenter environment. One of the problems is called incast problem. In order to efficiently utilize the bandwidth of distributed systems such as datacenter environments, some traffic control mechanisms should be very necessary. In this paper, we discussed about an application layer control mechanism to improve the performance of certain traffic pattern which may cause incast problem. The main idea is to make the data flows be staggered and transferred in serialized manner. We conducted both simulation-based and real-machine-based experiments. We find out that staggered flows are able to avoid incast problem and to make the performance better in most cases.

A tiled display wall (TDW), which is a single large display device composed of multiple sets of computers and displays, has recently gained the attention of scientists. In particular, SAGE, which is a middleware for building TDW, allows scientists to browse a multiple series of visualized results of computer simulation and analysis through the use of network streaming. Each visualized result can be generated on a different remote computer. For this reason SAGE has been increasingly hailed as a promising visualization technology that will solve the geographical distribution problem of computational and data resources. SAGE depends heavily on a network streaming technique in its architecture, but does not have any recovery mechanism against cases of network problems. This research, therefore, aims at realizing a network failure avoidance functionality for SAGE, focusing on OpenFlow against SAGE vulnerability to network failure. Specifically, the functionality is designed and developed as a composition of the following three functions: network failure detection, network topology understanding, and a packet forwarding control function. The key concept behind our design is that the network control function from OpenFlow should be built into SAGE. The evaluation in the paper confirms that the proposed and prototyped network failure avoidance functionality can detect failures on network routes and then reroute network streaming for visualization on TDW.

Grid applications are increasingly becoming dependent on network resources. Predicted network throughput is a useful parameter for network-aware scheduling for such applications. Although throughput prediction methods have been proposed, many of these methods are suffering from the fact that the probability distribution of traffic is unclear and the scale and bandwidth of networks are constantly changing. Furthermore, a virtual machine has been used as a platform for grid computing, and it can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer that predicts the throughput of a large data transfer. We propose a throughput prediction method based on the connection pair that uses v-support vector regression (SVR) and polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction accuracy of our method compared to that of a previous prediction method is higher. Moreover, the drop in the accuracy is also smaller than that of the previous method under an unstable network state. We clarify the prediction accuracy with other probe sizes for the connection pair. The accuracy is decreased by a small-sized probe, and there are no changes with a large-sized probe. These results show that our method is accurate, robust, and suitable for its purpose.

Visualization has been taking a role of much importance in both science and business areas. For scientists, visualization is a method to gain the meaning of and insight from scientific data and computational results; for business marketing area, computer visualization is popular as a digital signage technology. Recently, tiled display wall (TDW) has gathered users' attention and concern as a promising large-scale visualization technique. SAGE (Scalable Adaptive Graphics Environment) is a representative middleware for realizing such a TDW and experimentally used in an inquisitive way at research institutions and universities. However, this SAGE has an undesirable problem that existent X-window based visualization applications are not displayed without source code modification, although it provides a suite of APIs with which developers can write visualization applications displayed on the SAGE-enabled TDW. In this paper, we present an adapter solution that allows users to display X-window application on the SAGE-based TDW without source code modification. In our evaluation, the developed adapter solution incurred a small overhead in displaying an X application onto a SAGE-enabled TDW, in comparison with the case of modifying the source code of it.

Network test beds based on virtualization technology have been used in network research. Network measurement is affected by shared and virtualized resources on a node. Oversized packet spacings, which can occur due to CPU scheduling latency, are a major cause of both throughput instability and imprecise network measurement on a virtualized network test bed. These spacings are anomalies in virtualized network environment. Although CPU availability is an important criterion for estimating these anomalies, a naive method for measuring it may over consume CPU resources and affect the performance of other tasks. We observed the resource states of a system during throughput measurement and applied principal component analysis to an approximately 7 × 8000 matrix of the resource states to obtain criteria for estimating the anomalies instead of using the naive method. We show that the top two principal components account for 84% of the original dataset and can describe the original resource state. Component loadings and a scatter plot of the first and second component scores can provide a simple but descriptive enough view of the resource state for anomaly estimation. The first component is workloads on the node, and the second one is lack of resources, leading to anomalies. We determined the appropriate component boundary scores for an anomaly area by using Bayes' approach and used the boundaries to evaluate the anomalies with an input dataset. The results showed that our approach can be used to estimate anomalies.

We generally utilize high-speed transport protocols to transfer and share large-scale data in long-distance broadband networks. However, the difference in performance between protocols when some kinds of protocols share the same link increases. Consequently, we propose a control method of discarding packets corresponding to protocol types that takes into consideration high-speed performance and fairness of flows. We evaluated the efficiency of our method in simulations. As a result, we found that it could allocate bandwidth equally while retaining high link utilization.

Advanced Layer-3 (L3) switches achieve high-speed IP packet forwarding by storing parts of the header information from transmitted packets into the flow cache in the switch fabric when relaying the IP packets between subnets. When IP traffic is overloaded on an L3 switch, the flow cache is easily exhausted, decreasing the IP packet forwarding performance. Virtual LAN (VLAN), a virtualization technology at the data-link layer, is widely used for the internal networks of many organizations because it allows network configurations to be changed easily and provides design flexibility. In a VLAN-based local area network with multiple L3 switches, the relaying point for each VLAN can be placed on any of the L3 switches. We developed a new network control scheme called distributed virtual routing, which dynamically controls the packet exchange points for each VLAN to suppress the consumption of flow caches. We describe the basic concept and then evaluate the reduction of the relaying flows through the simulation using the real network data.

This paper proposes an autonomous energy saving method that works on wireless sensor networks. Wireless sensor nodes are equipment for gathering information about the user's surrounding environment. An energy-efficient data gathering system is required because each node is battery powered. Our method autonomously reduces the sampling frequency for monitoring environments whose condition may vary rapidly or slowly. Its simple lightweight computation scheme also helps to reduce battery consumption. We conducted experiments using real sensor data and found that our method performed well in terms of both power consumption and quality of sampled data.

A new anomaly detection system for mobile handsets has been proposed. In this system, software behavior that deviates from a model representing normal behavior is considered to be an anomaly. It is generally impossible to cover software behavior exhaustively by the model, which could adversely affect accuracy. In order to resolve this problem, the proposed system assesses the anomalousness of behavior not covered by the model. Moreover, this system needs to have a low overhead in order to be used in mobile handsets, which have less computational resource than a PC. The proposed system adopts an efficient feature for behavior assessment to achieve a high accuracy with a low overhead. This system is implemented on the ARM architecture, which is widely used in mobile handsets. Experimental results clarify that the performance overhead is reasonable and anomalous behavior can be detected accurately.

Running a program under surveillance by a reference monitor system is a promising approach to secure use of programs. However, with the existing reference monitor systems, it is difficult to make a security policy description that adequately conforms to the Least Privilege Principle. In this paper, we propose a novel mechanism of implementing reference monitor systems. Our mechanism utilizes information obtained by analyzing a target program before it is used. Our mechanism enables one to make security policy descriptions in a fine-grained manner, allowing persistence of the Least Privilege Principle. Our proposal includes a graphical user interface (GUI) tool that helps the user to describe the security policy. We describe implementation of a reference monitor system using our mechanism and implementation of the GUI tool. We measured the performance of our mechanism on a micro benchmark and macro benchmark.

Anomaly detection is a promising approach to detecting intruders masquerading as valid users (called masqueraders). It creates a user profile and labels any behavior that deviates from the profile as anomalous. In anomaly detection, a challenging task is modeling a user’s dynamic behavior based on sequential data collected from computer systems. In this paper, we propose a novel method, called Eigen co-occurrence matrix (ECM), that models sequences such as UNIX commands and extracts their principal features. We applied the ECM method to a masquerade detection experiment with data from Schonlau et al. We report the results and compare them with results obtained from several conventional methods.

Wear tests in dry rolling contact were carried out at room temperature on five ceramic materials, such as silicon nitride, silicon carbide, cermet, titania, and alumina. The results showed that wear rate of silicon nitride was smaller than any of the other ceramic materials and bearing steel. Observations of worn surface and wear debris revealed that ceramic materials have two types of wear, one related to real contact area and another related to Hertzian contact area. It was also found that brittle fracture dominates the wear process of ceramic materials in dry rolling contact. Based on the experimental results, wear rate of ceramic materials was expressed with a new nondimensional parameter.

It was shown that the transition from severe to mild wear and the wear rate of 0.45 wt.% C steel pairs in dry friction were sensitive functions of the normal damping coefficient of the loading system. The transition from severe to mild wear appeared above a damping coefficient of 20 N s m −1 at a sliding velocity of 0.26 m s −1 , but it appeared below 2 N s m −1 at 1.57 m s −1 . The severe wear rate at 0.26 m s −1 decreased with increase in damping coefficient, but it increased at 1.57 m s −1 with increase in damping coefficient. These complicated wear properties under the effect of damping were explained using the new parameters, effective sliding distance and dynamic normal load.