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 recent years, an evolutionary change occurred in the digital world when various social networking sites became popular. This led to a big increase of users who share their activities in digital form. A huge amount of digital information has become available, providing researchers with a unique insight into the behavior and activities of entire population. The use of Geo-tagged social media data became an emerging trend to represent user actions and behaviour at specific geographical locations. However, the inaccuracies of Geo-tagged data from social media often limits the utility of this data source in micro scale analysis (at the street or place of interest level (POI)). Our study supports how social media data could be matched accurately with specific physical locations. More specifically, our investigation includes Geo-tweet data and image data from flickr to map accurately physical location using machine leaning and deep learning techniques. In this paper, a preliminary discussion of this work is provided, using Geo-tagged data from Twitter and Open StreetMap in cities such as San Francisco and London as well as for image mapping using Flickr data in cities such as San Francisco and Kyoto.

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).

In today's big data era, there is a tremendously huge amount of data available. Layman users lack not only the knowledge and experience in data analytics to make sense of these data but also the computational resources for executing the analytics. In this paper, we propose and develop a layman analytics system (LAS), which provides the layman users with a scalable and ready-to-use analytics tool to automatically generate analytics workflows for classification tasks. The LAS is designed to benefit from existing open-source data analytics tools using generic ontological modeling of analytics operators from these tools as well as adaptive constraint refinement for metadata learning. Moreover, the LAS can be deployed on both public and private clouds to cater to the need of scalable computing and easy maintenance. To demonstrate the performance of the LAS, we conducted experiments with 114 data sets obtained from the University of California Irvine Machine Learning Repository. The workflows generated by the LAS were benchmarked against the OpenML whereby each data set has a range of classification accuracy obtained using classifiers designed and fine-tuned by data experts. The comparisons showed that 87 out of 114 data sets have exceeded the 50th percentile of the benchmark data. Among these 87 data sets, the LAS outperforms the 90th percentile of the benchmarks on 49 data sets.

The Internet has become the most vulnerable part of critical civil infrastructures. Proactive measures such as early warnings are required to reduce the risk of disasters that can be created using it. With the continuous growth in scale, complexity and variety of networked systems the quality of data is continuously decreasing. This paper investigates the ability to employ Bayesian inference for network scenario analysis with low quality data to produce early warnings. Theoretical account of the approach and experimental results using a real world attack scenario and a real network traffic capture is presented.

The increasing number of cloud-based Internet applications has led to the demand for efficient resource and cost management. This paper proposes a real-time group auction system for the cloud instance market. The system is designed based on a combinatorial double auction, and its applicability and effectiveness are evaluated in terms of resource efficiency and monetary benefits to auction participants (e.g., cloud users and providers). The proposed auction system helps them decide when and how providers will allocate their resources and to which users. Furthermore, we propose a distributed algorithm using a group formation game that determines which users and providers will trade resources by their cooperative decisions. To find how to allocate the resources, the utility optimization problem is formulated as a binary integer programming problem and the nearly optimal solution is obtained by a heuristic algorithm with quadratic time complexity. In comparison studies, the proposed real-time group auction system with cooperation outperforms an individual auction in terms of the resource efficiency (e.g., the request acceptance rate for users and resource utilization for providers) and monetary benefits (e.g., average payments for users and total profits for providers).

This paper investigates network traffic before and after a vulnerability called Heart bleed becomes a public issue around March to May, 2014. To detect anomalies and potential threats due to the vulnerability, a wavelet entropy-based change-point detection method is proposed and compared with three other methods: prediction-based, clustering-based and Fourier transform-based. We show that the proposed wavelet entropy-based method outperforms the others in terms of ease of parameter setting, false alarm and detection accuracy. Using the proposed method and a visualization tool, we have studied Heart bleed vulnerability and successfully captured changes in packet volume and flow.

Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance of PD patients using data mining techniques to reveal the differences between dyskinesia and non-dyskinesia patients. We found that K-means clustering of the root mean square (RMS) tracking error at faster tracking speeds and with ambiguous visual stimuli was able to effectively discriminate between the two groups with 77.8% accuracy. Decision tree classification was less accurate (68.4%) and determined that years since diagnosis was the best feature to distinguish between groups. Our results suggest that data mining methodologies may provide novel insights into features of the neurovegetative disease.

ABSTRACT Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

This paper reports on the development of the Cloud Oriented Data Analytics (CODA) framework which has functions for composing, managing, and processing workflows for data analytics in cloud computing. The framework provides a number of reusable software components for data analytics to users which can be composed as workflows through well-known workflow composers, e.g., RapidMiner, Taverna, and JOpera. In particular, workflow scheduling, workflow recommendation, resource provisioning, resource monitoring, data locality, and security for the workflow computation are addressed by the framework. By using the framework, we demonstrate that workflows can be easily composed and processed in cloud computing. By coordinating the submitted workflows, we can obtain a significant improvement in performance.

This paper proposes a multi-tenant workflow framework that allows users to create data analytic workflows whose tasks are efficiently scheduled and distributed in cloud computing environment. We provide a demo of an event room assignment (ERA) as a test application of the framework. The ERA dynamically and automatically assigns registered events (e.g., meetings, classes, conferences, etc.) to available rooms meeting the user requirements such as the event size, purpose, reservation period, etc. The assignment will lead to the energy efficiency with respect to the power usage (e.g., lighting, ventilation, devices, etc.), and the energy savings can be achieved without affecting people's comfort. We run the ERA with power consumption data (whose size is approximately 50GB) collected from each of over 200 rooms in a building at Dept. of Engineering, Tokyo University. Through the demonstration, we will show that the proposed framework accelerates the speed of data analysis by providing user-friendly workflow composition and parallel processing features utilizing cloud computing technologies.

SUMMARY This paper describes an architecture to build self‐optimizable and self‐stabilizable cloud applications. The design of the proposed architecture, SymbioticSphere, is inspired by key biological principles such as decentralization, evolution, and symbiosis. In SymbioticSphere, each cloud application consists of application services and middleware platforms. Each service and platform is designed as a biological entity and implements biological behaviors such as energy exchange, migration, reproduction, and death. Each service/platform possesses behavior policies, as genes, each of which governs when to and how to invoke a particular behavior. SymbioticSphere allows services and platforms to autonomously adapt to dynamic network conditions by optimizing their behavior policies with a multiobjective genetic algorithm. Moreover, SymbioticSphere allows services and platforms to autonomously seek stable adaptation decisions as equilibria (or symbiosis) between them with a game theoretic algorithm. This symbiosis augments evolutionary optimization to expedite the adaptation of agents and platforms. It also contributes to stable performance that contains very limited amounts of fluctuations. Simulation results demonstrate that agents and platforms successfully attain self‐optimization and self‐stabilization properties in their adaptation process. Copyright © 2012 John Wiley & Sons, Ltd.

Network applications are increasingly required to be autonomous, scalable, adaptive to dynamic changes in the network, and survivable against partial system failures. Based on the observation that various biological systems have already satisfied these requirements, this article proposes and evaluates a biologically-inspired framework that makes network applications to be autonomous, scalable, adaptive, and survivable. With the proposed framework, called iNet, each network application is designed as a decentralized group of software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as reproduction, migration, energy exchange, and death. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces specific antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (an antigen) to evaluate whether it adapts well to the sensed environment, and if it does not, adaptively perform a behavior (an antibody) suitable for the environment conditions. In iNet, a configuration of antibodies is encoded as a set of genes, and antibodies evolve via genetic operations such as crossover and mutation. Empirical measurement results show that iNet is lightweight enough. Simulation results show that agents adapt to dynamic and heterogeneous network environments by evolving their antibodies across generations. The results also show that iNet allows agents to scale to workload volume and network size and to survive partial link failures in the network.

Abstract—Large-scale network applications are expected to be more autonomous and adaptive to dynamic changes in the network to improve user experience, expand applications ’ operational longevity and reduce maintenance cost. Based on the observation that various biological systems have already met the requirements (i.e., autonomy and adaptability), this paper describes a biologically-inspired framework, called iNet, to design autonomous and adaptive network applications. iNet is designed after the mechanisms behind how the immune system works. iNet models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and a behavior of network applications (e.g., migration and reproduction) as an antibody. iNet allows network applications to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. The configuration of antibodies evolves via genetic operations (e.g., mutation and crossover). Simulation results show that iNet allows agents to autonomously adapt to changing environment conditions by invoking their behaviors suitable for the current environment condition and evolving their antibody configurations. I.

This paper describes and empirically evaluates a biologically-inspired adaptation mechanism that allows grid network services to autonomously adapt to dynamic environment changes in the network. Based on the observation that the immune system has elegantly achieved autonomous adaptation, the proposed mechanism, the iNet artificial immune system, is designed after the mechanisms behind how the immune system detects antigens (e.g. viruses) and specifically reacts to them. iNet models a set of environment conditions (e.g. network traffic and resource availability) as an antigen and a behavior of grid services (e.g. migration and replication) as an antibody. iNet allows each grid service to autonomously sense its surrounding environment conditions (i.e. an antigen) to evaluate whether it adapts well to the sensed conditions, and if it does not, adaptively perform a behavior (i.e. an antibody) suitable for the sensed conditions. Empirical evaluation results show that iNet works efficiently in acceptable degree of accuracy and makes grid services adaptive to dynamic network environment. 1.

This paper describes SymbioticSphere, a novel biologically-inspired architecture that allows grid systems (application services and middleware platforms) to be scalable and adaptive to dynamic network environments. In SymbioticSphere, each service and platform is designed as a biological entity, analogous to an individual bee in a bee colony. Services and platforms implement biological concepts and mechanisms such as decentralization, emergence, energy exchange, migration, replication and death. Like in biological systems, desirable systems characteristics such as scalability and adaptability emerge from collective actions and interactions of services and platforms

Summary form only given. This poster presentation describes and empirically evaluates a biologically-inspired adaptation mechanism that allows grid network services to autonomously adapt to dynamic environment changes in the network (e.g. changes in network traffic and resource availability). Based on the observation that the natural immune system has elegantly achieved autonomous adaptation, the proposed mechanism, called the iNet artificial immune system, is designed after the mechanisms behind how the natural immune system detects antigens (e.g. viruses) and specifically reacts to them. iNet models a behavior of grid network services (e.g. migration and replication) as an antibody, and an environment condition (e.g. network traffic and resource availability) as an antigen. iNet allows each grid network service to (1) autonomously sense its surrounding environment conditions (i.e. antigens) to evaluate whether it adapts well to the current conditions, and if it does not, (2) adaptively perform a behavior (i.e. antibody) suitable for the conditions (i.e. antigens). This poster presents the iNet architecture and its algorithm design. It also shows several empirical experimental results. They show that iNet works efficiently at small memory footprint and it makes grid network services adaptive by dynamically changing their population and location against environmental changes in the network