The always-online nature and the lack of sufficient built-in protection make Internet of Things (IoT) devices highly susceptible to various cyberthreats. An efficient and effective anomaly detection system is an essential need for IoT device security. However, it’s challenging to apply advanced host-based anomaly detection techniques from conventional systems to IoT devices due to the device resource constraints. This paper introduces a monitoring and anomaly detection framework based on thread-level system call streams for IoT devices. It leverages the execution pattern of IoT applications and detects anomalies by analyzing system call arguments and associated I/O attributes, in addition to the invocation sequence in real-time. The evaluation results highlight the feasibility of the proposed approach in terms of both performance and security.

CoursewareHub, which is developed to use Jupyter Notebook in lectures and exercises, can acquire learning logs related to students’ cell execution, and is being used to improve classes and support classes based on the results of the log analysis. The authors have proposed a cloud-based log analysis environment using Elasticsearch, Fluentd and Kibana, and have extended the scope of application to network devices and LMS. In this paper, we aim to provide an environment for log analysis that enables correlation analysis of multiple logs by further expanding the scope of application of this environment. As an example, we applied CoursewareHub logs to this environment and constructed a dashboard that enables teachers conducting classes to grasp the status of students by analysing CoursewareHub logs in real time.

Internet of Things (IoT) is widely used as a fundamental technology for realizing various services. An IoT-based service system comprising cloud servers and many IoT devices connected via networks may be risky owing to the possibility of the entire system being be affected by cyberattacks on an IoT device. Moreover, new software vulnerabilities are frequently reported. From the perspective of security, IoT device software must be reliable and resilient. Consequently, a secure software update mechanism for IoT, assurance of software reliability, and mitigation mechanisms are required. Overall, this study proposed a zero-trust over-the-air (ZT-OTA) update framework for the reliable and resilient software update management of IoT devices via OTA software updates from remote locations. The ZT-OTA update framework provides a strict software version-management mechanism that enhances the security of software updates. Further, the proposed framework collaborates with a Software Assessment Service (SAS) as an authorized third-party assessment organization and deploys reliable software approved by the service to IoT devices. Upon discovering a software vulnerability following the deployment of the software, the SAS proactively revokes the reliability of the software by revoking its certificates for signing software codes and the vulnerability assessment result. Subsequently, the ZT-OTA server notifies developers and users of IoT devices to arrange for new software that must be fixed and automatically acts on behalf of IoT devices that must be retired. This study introduces the detailed design of the ZT-OTA update framework and describes to demonstrate its feasibility.

Various sensor data from Internet of Things (IoT) devices are expected to be routinely collected, analyzed, and utilized in the cloud. However, the required communication throughput and latency for various services must be maintained when collecting IoT data in mobile environments. IoT communication involves a large amount of small-scale streaming data, necessitating efficient transmission methods tailored to the communication environment. In this study, we investigate the effectiveness of compression processing by varying the publish/subscribe data characteristics and data compression ratios to improve performance. In experiments, the performance of MQTT communication over SINETStream is investigated, varying parameters such as data size, compression algorithm, data characteristics, and data compression ratio. The results show that performance is improved for highly compressible data, and the performance difference becomes more pronounced as data size increases. There is no correlation between compression time and data compression ratio, and the impact of compression time on overall execution time is slight, thereby confirming that selecting appropriate algorithms for each data characteristic and applying compression processing according to the data size effectively improves performance.

The data collected by Internet of Things (IoT) devices equipped with sensors enable smart home services such as monitoring the elderly, pets, and the indoor environment. Building an IoT system to collect data from individual households in the cloud requires measures to reduce communication latency and the amount of data transferred, and protect privacy. When collecting diverse data in an indoor environment, installing sensors in multiple indoor locations is necessary. However, installing numerous sensors increases costs and makes it difficult to relocate the sensors to obtain the necessary information. In this study, we construct an IoT system for a smart home that collects indoor environmental information using a wheeled mobile robot implemented in a Robot Operating System (ROS) and performs analysis processing in a cloud via an edge server. We attempt to demonstrate the effectiveness of sensor data collection using a robot by developing a prototype system for indoor carbon dioxide concentration monitoring application. We also the performance characteristics of ROS communication between the sensor robot and the edge server, and IoT communication between the edge server and the cloud server under different communication environments to identify technical issues in the smart home.

Along with the significant growth in the number of connected Internet of Things (IoT) devices and increasingly aggressive cyberattacks, IoT cybersecurity has been facing more and more challenges. Security monitoring systems as one of the predominant security hardening approaches are often introduced to computer systems for detecting anomaly activities and ongoing intrusion. System auditing is one of the prevalent approaches for realizing such systems. However, most of the existing monitoring techniques for IoT systems heavily rely on network traffic analysis. In this work, we emphasize the device endpoint itself and propose a flexible and extensible monitoring framework for Linux-based IoT systems. We present the feasibility of the framework by implementing a monitoring prototype and an application simulating real-world IoT surveillance scenario, and conducting comprehensive evaluations on an ARM device. The evaluation results showcase the minimal overhead cost of the proposed monitoring framework and demonstrate the practicability of security monitoring on constrained IoT devices.

In this study, we aimed to construct a system to perform statistical analyses for educational improvement while preserving privacy. Various probabilistic statistical algorithms have been developed for data agitation. However, one of the major challenges in using these algorithms is determining appropriate parameters. To address this issue, our previous research proposed the “α-criterion” as a criterion that must be satisfied by appropriate parameters. This paper outlines a system that calculates the parameters that satisfy the α-criterion while performing real-time data agitation on the given aggregate data. To verify that our implementation actually works, we show a use case with sample data on kibana-Elastic log analysis provided by Elastic N. V.

We propose a criterion of reasonable parameters for an algorithm that aggregates target data with anonymized data for safe data analysis in online educational systems, called learning management systems (LMSs). We also statistically investigate parameters that can satisfy the proposed criteria using an anonymization algorithm and real large-scale data. We use an open dataset containing one year's worth of product review data due to the difficulty of collecting LMS data large enough for the evaluation. Furthermore, we discuss an approach to address cases where no parameter satisfies the proposed criteria.

Demands for Internet of Things (IoT) platforms are increasing along with the expansion of data-driven sciences, and academic network infrastructure such as national research and education networks (NRENs) are now being encouraged to aggressively support diverse IoT research projects. In Japan, the National Institute of Informatics (NII) operates an NREN called "SINET5," which is an academic backbone network linking more than 900 universities and research institutions. In addition to SINET5's high-speed 100 Gbps backbone network, SINET5 also provides a mobile network called "Mobile SINET" as IoT infrastructure. To provide crucial support to IoT research projects and to facilitate the development and deployment of IoT applications, we present herein our experience with our software library, "SINETStream." In this paper, we provide an overview of SINETStream and discuss lessons learned from examples of application deployment over Mobile SINET. SINETStream provides a common and simple application program interface (API) for various message brokers, security functions, and performance tuning support features. These functions improve the portability of applications and help application developers to remove hindrances to the development of secure and efficient IoT applications. The experimental results provided herein show that application developers can use SINETStream functions within a reasonable overhead. We also show how the combination of SINETStream and Mobile SINET enables users to develop and deploy highly confidential and efficient IoT applications.

As learning management systems (LMSs) become increasingly popular in many universities, large scale Moodle systems, such as cross-university LMSs, have been implemented, and they accumlate a large amount of LMS log data. To handle such a large amount of data, it is effective to use a cloud computing environment. However, because LMS log data contain much personal information, it is often necessary to anonymise or pseudonymise the data to use them in cloud computing environment. In this study, we implemented a log analysis environment that combines an organisation’s internal server and a cloud computing environment by using the GakuNin-Cloud on-demand configuration service provided by the National Institute of Informatics (NII). In this environment, LMS logs are stored in the cloud with pseudonymisation, and the pseudonymisation is automatically removed by a re-identification proxy when the logs need to be analysed. We applied the environment to the Moodle system at Gunma University. We also applied the environment to an example Moodle created by the GakuNin Cloud on-demand configuration service.

With the development of cameras and sensors, and the spread of cloud computing, life logs can be acquired and stored in general households for various services using the logs. However, it is difficult to analyze moving images acquired by a home sensor in real time using machine learning because the data size and the computational complexity are large. New computing paradigm called edge computing or fog computing, which enables distributed computing over edge and cloud, has the possibility to address this issue. The feature vectors are extracted from moving images by preprocessing on the sensor side and the only small feature vectors are sent to the cloud and used for learning. But, it is not clear how accurately we can recognize actions using only the feature vectors in the learning and inferring. We investigate the accuracies of action recognition with various machine learning methods using feature vector information obtained from moving images. We use the pose estimation library OpenPose for detection of the feature vectors and recognize actions using logistic regression, random forest, support vector machine, and neural network (NN) models, general NN and LSTM, as machine learning methods. The experimental results show that it is possible to recognize an action with 80% accuracy or higher when using random forest and neural network models. We also discuss a method to further improve the accuracy based on the experimental results.

The spread of various sensors and the development of cloud computing technologies enable the accumulation and use of many live logs in ordinary homes. In addition, deep learning technologies have been widely used for image and speech recognition processing. However, a key issue for deep learning is heavy processing loads. To operate a service that utilizes sensor data, those data are transmitted from sensors in ordinary homes to a cloud and analyzed in the cloud. However, services that involve moving image analysis require large amounts of data to be transferred continuously and high computing power for the analysis; hence, it is difficult to process them in real time in the cloud using a conventional stream data processing framework. First, we perform preliminary experiments using Apache Spark [3] (hereinafter called Spark), which is a representative cluster computing platform that is designed to be fast and versatile, and Ray [4] , which is a distributed execution framework. We investigate the characteristics of their distributed recognition processing and demonstrate that Ray enables scalable distributed processing. Next, We implement a prototype system of the proposed distributed stream processing infrastructure using Ray and Apache Kafka [1] (hereinafter called Kafka), which is a distributed messaging system, and demonstrate its performance.

As the use of various sensors and cloud computing technologies has spread, many life-log analysis applications for safety services for the elderly and children have been developed. However, it is difficult to perform real-time large data processing in clouds due to the computational complexity of the analysis because efficient deployment schemes of streaming computing components over cloud resources have not been well-investigated. In this study, we propose a video analysis framework that collects videos from multiple cameras and analyzes them using Apache Kafka and Apache Spark Streaming. We first investigate the data transfer performance of Apache Kafka and examine efficient cluster configuration and parameter settings. We then apply this configuration to the proposed framework and measure the data analysis throughput. The experimental results show that the overall throughput varies depending on the number of broker nodes that store data, the number of topic partitions of data, and the number of nodes that conduct analysis processing. In addition, it is confirmed that the number of cores is needed to consider for the efficient cluster configuration, and that the network bandwidth between the nodes becomes a bottleneck as the amount of data and the number of components increase.

The Spread of cameras and sensors and cloud technologies enable us to obtain life logs at ordinary homes and transmit the captured data to a cloud for life log analysis. However, the amount of processing for video data analysis in a cloud drastically increases when a very large number of homes send data to the cloud. In this research, we aim to improve the efficiency of distributed video data analysis processing by using the parallel deep learning framework Chainer [2] and the distribution processing platform Apache Spark [1] (Spark). In this paper, we construct a Spark cluster and investigate the performance of parallel data processing using Spark varying parameter settings.

High-performance R&E networks and clouds enable the construction of a secure virtual cloud environment over their resources. However, it is not easy for general users to construct such an environment and build an application over the environment, as this would require each user to not only have knowledge about the target application, but also about the cloud APIs and resource optimization. This prompted us to propose a Virtual Cloud Service System (VCSS), which allows general academic users to construct an application-specific virtual cloud environment. The system consists of the Virtual Cloud Provider (VCP) middleware and Application Templates. The VCP provides users with a simple service interface and enables them to construct a virtual cloud. The Application Templates provide Jupyter Notebook-based procedure manuals and a management environment for a virtual cloud constructed by the VCP for typical research and educational applications such as a learning management system. The experimental results show that the VCP can provide a secure and high-performance virtual cloud environment over academic and public clouds and the R&E network between them.

The spread of various sensors and Cloud technologies has made it easy to acquire life-logs and accumulate data. As a result, many life-log analysis applications, which transfer data from sensors, especially cameras to a Cloud and analyze them in the Cloud, have been developed. Cameras with a server function called network cameras have become cheap and readily available for security services and the monitoring of pets and children from remote locations. In these services, raw data from sensors, including cameras, are generally transferred to a Cloud and processed there. However, it is difficult to transfer raw data from sensors to a Cloud because of the limitation of network bandwidth between sensors and a Cloud and privacy issues caused by sending raw sensor data to a Cloud.

Distributed file systems, which manage large amounts of data over multiple commercially available machines, have attracted attention as a management and processing system for big data applications. A distributed file system consists of multiple data nodes and provides reliability and availability by holding multiple replicas of data. Due to system failure or maintenance, a data node may be removed from the system and the data blocks the removed data node held are lost. If data blocks are missing, the access load of the other data nodes that hold the lost data blocks increases, and as a result the performance of data processing over the distributed file system decreases. Therefore, replica reconstruction is an important issue to reallocate the missing data blocks in order to prevent such performance degradation. The Hadoop Distributed File System (HDFS) is a widely used distributed file system. In the HDFS replica reconstruction process, source and destination data nodes for replication are selected randomly. We found that this replica reconstruction scheme is inefficient because data transfer is biased. Therefore, we propose two more effective replica reconstruction schemes that aim to balance the workloads of replication processes. Our proposed replication scheduling strategy assumes that nodes are arranged in a ring and data blocks are transferred based on this one-directional ring structure to minimize the difference of the amount of transfer data of each node. Based on this strategy, we propose two replica reconstruction schemes, an optimization scheme and a heuristic scheme. We have implemented the proposed schemes in HDFS and evaluated them on an actual HDFS cluster. From the experiments, we confirm that the replica reconstruction throughput of the proposed schemes show a 45% improvement compared to that of the default scheme. We also verify that the heuristic scheme is effective because it shows performance comparable to the optimization scheme and can be more scalable than the optimization scheme.

This paper proposes a new cloud computing service model, Hardware as a Service (HaaS), that is based on the idea of implementing ``elastic data centers'' that provide a data center administrator with resources located at different data centers as demand requires. To demonstrate the feasibility of the proposed model, we have developed what we call an Inter-cloud Resource Integration System (Iris) by using nested virtualization and OpenFlow technologies. Iris dynamically configures and provides a virtual infrastructure over inter-cloud resources, on which an IaaS cloud can run. Using Iris, we have confirmed an IaaS cloud can seamlessly extend and manage resources over multiple data centers. The experimental results on an emulated inter-cloud environment show that the overheads of the HaaS layer are acceptable when the network latency is less than 10 msec. We believe these results provide new insight to help establish inter-cloud computing.

Virtual Machine(VM)-based flexible capacity management is an effective scheme to reduce total power consumption in the data centers. However, there remain the following issues, trade-off between power-saving and user experience, decision on VM packing plans within a feasible calculation time, and collision avoidance for multiple VM live migration processes. In order to resolve these issues, we propose two VM packing algorithms, a matching-based (MBA) and a greedy-type heuristic (GREEDY). MBA enables to decide an optimal plan in polynomial time, while GREEDY is an aggressive packing approach faster than MBA. We investigate the basic performance and the feasibility of proposed algorithms under both artificial and realistic simulation scenarios, respectively. The basic performance experiments show that the algorithms reduce total power consumption by between 18% and 50%, and MBA makes suitable VM packing plans within a feasible calculation time. The feasibility experiments show that the proposed algorithms are feasible to make packing plans for an actual supercomputer, and GREEDY has the advantage in power consumption, but MBA shows the better performance in user experience.

We have been developing a middleware suite called GridARS that enables co-allocation of computing and network resources from multiple administration sites. In such middleware, it is important to provide each user application with a slice which is a set of dynamically allocated resources distributed across sites. However, there are the following issues in constructing such a slice automatically: 1) multi-site administration heterogeneity, 2) dynamic determination of application configuration information, 3) distributed resource monitoring, and 4) asymmetric network reachability. We design and implement an application execution system that provides each application with a slice, that mimics a conventional computing cluster system over the dynamically allocated resources. From the demonstration of the proposed system on an emulated wide area network environment, we confirmed that: first, the proposed system can fully automate resource allocation, slice construction, application invocation, and resource monitoring, in coordination with GridARS. Second, the proposed system can setup a slice quickly, even if the allocated resources are widely distributed and their communication latencies are high. This is because the overhead for gathering and distributing contextualization information is small, and OS-level virtualization and stackable file system technologies accelerate the contextualization process at each node.

Intercloud is a promising technology for data intensive applications. However, an important issue for Intercloud applications is orchestration of various virtualized and performance-assured resources, not only computers, but also network and storage, provided from multiple domains. We have been developing an advance reservation-based resource management framework, called Grid ARS, which can integrate heterogeneous resources and construct a performance-assured virtual infrastructure over Intercloud environment. Grid ARS provides four services that address resource management, resource allocation planning, provisioning and monitoring of the constructed virtual infrastructure. Grid ARS has been developed using common Web services technologies and standards. In this paper, we present overview of Grid ARS and its service components and describe Grid ARS demonstration challenges, demonstration at GLIF2010 and SC10 and OGF NSI interoperation in 2011.

Data Grid is a Grid for ubiquitous access and analysis of large-scale data. Because Data Grid is in the early stages of development, the performance of its petabyte-scale models in a realistic data processing setting has not been well investigated. By enhancing our Bricks Grid simulator to accommodated Data Grid scenarios, we investigate and compare the performance of different Data Grid models. These are categorized mainly as either central or tier models; they employ various scheduling and replication strategies under realistic assumptions of job processing for CERN LHC experiments on the Grid Datafarm system. Our results show that the central model is efficient but that the tier model, with its greater resources and its speculative class of background replication policies, are quite effective and achieve higher performance, while each tier is smaller than the central model.

The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. The authors first present recent developments and extensions to the Bricks simulation models. They discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with "Load Correction" and "Fallback" mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadline scheduling algorithms.

While there have been several proposals of high-performance global computing systems, scheduling schemes for the systems have not been well investigated. The reason is difficulties of evaluation by large-scale benchmarks with reproducible results. Our Bricks performance evaluation system allows the analysis and comparison of various scheduling schemes in a typical high-performance global computing setting. Bricks can simulate various behaviors of global computing systems, especially the behavior of networks and resource scheduling algorithms. Moreover, Bricks is partitioned into components such that not only can its constituents be replaced to simulate various different system algorithms, but it also allows the incorporation of existing global computing components via its foreign interface. To test the validity of the latter characteristics, we incorporated the NWS (Network Weather Service) system, which monitors and forecasts global computing systems behavior. Experiments were conducted by running NWS under a real environment versus a Bricks-simulated environment, given the observed parameters of the real environment. We observed that Bricks behaved in the same manner as the real environment, and NWS also behaved similarly, making quite comparative forecasts under both environments.

The paper proposes a performance evaluation model for effective job scheduling in global computing systems. The proposed model represents a global computing system by a queueing network, in which servers and networks are represented by queueing systems. Evaluation of the proposed model showed that the model could simulate behavior of an actual global computing system and job scheduling on the system effectively.

Rapid increase in speed and availability of network of supercomputers is making high-performance global computing possible, including our Ninf system. However, critical issues regarding system performance characteristics in global computing have been little investigated, especially under multi-client, multi-site WAN settings. In order to investigate the feasibility of Ninf and similar systems, we conducted benchmarks under various LAN and WAN environments, and observed the following results: 1) Given sufficient communication bandwidth, Ninf performance quickly overtakes client local performance, 2) current supercomputers are sufficient platforms for supporting Ninf and similar systems in terms of performance and OS fault resiliency, 3) for a vector-parallel machine (Cray J90), employing optimized data-parallel library is a better choice compared to conventional task-parallel execution employed for non-numerical data servers, 4) computationally intensive tasks such as EP can readily be supported under the current Ninf infrastructure, and 5) for communication-intensive applications such as Linpack, server CPU utilization dominates LAN performance, while communication bandwidth dominates WAN performance, and furthermore, aggregate bandwidth could be sustained for multiple clients located at different Internet sites; as a result, distribution of multiple tasks to computing servers on different networks would be essential for achieving higher client-observed performance. Our results are not necessarily restricted to the Ninf system, but rather, would be applicable to other similar global computing systems.