Hybrid cloud resource provisioning policy in the presence of resource failures

Resource provisiomng is an important and challenging problem in the large-scale distributed systems such as Cloud computing environments. Resource management issues such as Quality of Service (QoS) further exacerbate the resource provisioning problem. Furthermore, with the increasing functionality and complexity of Cloud computing, resource failures are inevitable. Therefore, the question we address in this paper is how to provision resources to applications in the presence of resource failures in a hybrid Cloud computing environment. To this end, we propose three Cloud resource provisioning policies where we utilize workflow applications to drive the system workload. The proposed strategies take into account the workload model and the failure correlations to redirect requests to appropriate Cloud providers. Using real failure traces and workload models, we evaluated the performance and monetary cost of the proposed policies. The results of our experiments show that we can decrease the deadline violation rate of users' requests to as low as 20% with a limited cost on Amazon public Cloud.

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

Transcendental pragmatics? Pragmatism, Deleuze, and metaphilosophy

In this chapter I juxtapose the methodological commitments of Gilles Deleuze with some different forms of contemporary neo-pragmatism developed by Nicholas Rescher, Sami Pihlstrom and Joseph Margolis. Focusing upon their respective conceptions of transcendental reasoning, naturalism, and common sense, I conclude that Deleuze’s philosophy challenges some core aspects of contemporary neo-pragmatism, and hence also the prospects for a rapprochement that might warrant the name of "transcendental pragmatics".

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Exposing HPC and sequential applications as services through the development and deployment of a SaaS cloud

Cloud and service computing has started to change the way research in science, in particular biology and medicine, is being carried out. Researchers that have taken advantage of this technology (making use of public and private cloud compute resources) can process large amounts of data (big data) and speed up discovery. However, this requires researchers to acquire a solid knowledge and skills in the development of sequential and high performance computing (HPC), and cloud development and deployment background. In response a technology exposing HPC applications as services through the development and deployment of a SaaS cloud, and its proof of concept in the form of implementation of a cloud environment, Uncinus, has been developed and implemented to allow researchers easy access to cloud computing resources. The new technology offers and Uncinus supports the development of applications as services and the sharing of compute resources to speed up applications' execution. Users access these cloud resources and services through web interfaces. Using the Uncinus platform, a bio-informatics workflow was executed on a private (HPC) cloud, server and public cloud (Amazon EC2) resources, performance results showing a 3 fold improvement compared to local resources' performance. Biology and medicine specialists with no programming and application deployment on clouds background could run the case study applications with ease.

O Sublime Nanotecnológico

The nano convergence is a historic event, similar to the advent of cyberspace, which has equal potential disruption. As well as the aesthetics of communication Mario Costa has a technological sublime mediated through imaging techniques, we propose in this work the emergence of an aesthetic of nanocomunicasio, which, in turn, proposes a nanotechnological sublime mediated through technological individuation. While the technological sublime Mario Costa is entirely based on the Kantian sublime, the sublime nanotechnology is inspired by the sublime transimanente Schopenhauer. If the technological sublime Mario Costa is connected to virtuality and reproducibility of digital imaging techniques, we have the sublime nanotechnology refers instead to the materiality and autorreprodutibilidade the principle of individuation.

A general communication cost optimization framework for big data stream processing in geo-distributed data centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.