BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

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Search-based software engineering: Trends, techniques and applications

A Comprehensive Survey of Network Function Virtualization

We study the virtual machine live migration (LM) and disaster recovery (DR) from a networking perspective, considering long-distance networks, for example, between data centers. These networks are usually constrained by limited available bandwidth, increased latency and congestion, or high cost of use when dedicated network resources are used, while their exact characteristics cannot be controlled. LM and DR present several challenges due to the large amounts of data that need to be transferred over long-distance networks, which increase with the number of migrated or protected resources. In this context, our work presents the way LM and DR are currently being performed and their operation in long-distance networking environments, discussing related issues and bottlenecks and surveying other works. We also present the way networks are evolving today and the new technologies and protocols (e.g., software-defined networking, or SDN, and flexible optical networks) that can be used to boost the efficiency of LM and DR over long distances. Traffic redirection in a long-distance environment is also an important part of the whole equation, since it directly affects the transparency of LM and DR. Related works and solutions both from academia and the industry are presented.

Survey: Live Migration and Disaster Recovery over Long-Distance Networks

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Cloud is an emerging technology in the world of information technology and is built on the key concept of virtualization. Virtualization separates hardware from software and has benefits of server consolidation and live migration. Live migration is a useful tool for migrating OS instances across distant physical of data centers and clusters. It facilitates load balancing, fault management, low-level system maintenance and reduction in energy consumption. In this paper, we survey the major issues of virtual machine live migration. We discuss how the key performance metrics e.g downtime, total migration time and transferred data are affected when a live virtual machine is migrated over WAN, with heavy workload or when VMs are migrated together. We classify the techniques and compare the various techniques in a particular class.

Live virtual machine migration techniques: Survey and research challenges

Brokering in interconnected cloud computing environments: A survey

A well-rounded, accessible exposition of honeypots in wired and wireless networks, this book addresses honeypots from a variety of perspectives. Case studies enhance the practical understanding of the subject along with strong theoretical foundation. The book covers the latest technology in information security and honeypots, including honeytokens, honeynets and honeyfarms. Topics include denial of service, virus, worms, phishing, and elaborates on virtual honeypots and forensics. Practical implementations as well as current state of research are discussed.

Honeypots: A New Paradigm to Information Security

The growing demand of computation, large data storage needed for running a high performance computing enterprise and high dimensional data based web application increases the energy and power consumed by large infrastructure. Cloud computing is providing a solution as part of the Green IT initiative to reduce the adverse environmental impacts and save energy. Our paper describes important metrics of cloud computing which makes it greener. We discuss the various power and energy models and identify major challenges to build a model for Green Cloud. We also discuss the ways to reduce power and energy in terms of cloud computing services. Our work surveys the various models and helps understand the road map for a greener cloud.

A survey on energy and power consumption models for Greener Cloud

It is common to encounter databases that have up to a half of the entries missing, which is specifically true with medical databases. Most of the statistical and data mining techniques require complete datasets and obviously these techniques do not provide accurate results with missing values. Several methods have been proposed to deal with the missing data. Commonly used method is to delete instances with missing value attribute. These approaches are suitable when there are few missing values. In case of large number of missing values, deleting these instances results in loss of bulk of information. Other method to cope-up with this problem is to complete their imputation (filling in missing attribute). We propose an efficient missing value imputation method based on clustering with weighted distance. We divide the data set into clusters based on user specified value K. Then find a complete valued neighbor which is nearest to the missing valued instance. Then we compute the missing value by taking the average of the centroid value and the centroidal distance of the neighbor. This value is used as impute value. In our proposed approach we use K-means technique with weighted distance and show that our approach results in better performance.

Ramesh C. Joshi

Papers

Live virtual machine migration techniques: Survey and research challenges

Brokering in interconnected cloud computing environments: A survey

Honeypots: A New Paradigm to Information Security

A survey on energy and power consumption models for Greener Cloud

Missing Value Imputation Based on K-Mean Clustering with Weighted Distance