App Engine Research Articles

ServBGP: BGP-inspired autonomic service routing for multi-provider collaborative architectures in the cloud

In this paper we propose the design and implementation of ServBGP, a service routing protocol for managing service collaboration among cloud providers in cloud computing. ServBGP is based on the policy-driven design of the well-known BGP Internet routing protocol to support the different service interaction models currently employed in the cloud particularly the monolithic, composite, and broker-based service models. The main contribution of this work lies in devising a system that autonomously manages the different aspects of service interaction and collaboration among service providers from service discovery and advertisement to service consumption and revocation. The ServBGP routing decision engine is designed to operate by (1) processing cost-bidding service advertisement and revocation messages from the different cloud providers and (2) extracting objective and trustworthy feedback which is maintained by certified network entities and which consists of a set of reputation scores related to quality of service, security, and pricing. The BGP-inspired design choice allows the proposed system to “stand upon the shoulders of giants” by relying on a time-tested protocol that currently supports scalable inter-domain routing services among over 120,000 IPv4 prefixes in the Internet. Moreover, reusing the decision logic of the standard BGP protocol aids in reducing the development and operational costs, hence shortening the time to market, and utilizing a large set of BGP extensions, particularly, in the security domain, that have been incorporated into BGP since its inception in 1989. These qualities are of a chief importance in the cloud community today to ensure the efficient and secure service collaboration among cloud providers and to support the evolution, scalability, and economic feasibility of the overall cloud computing infrastructure. A proof of concept implementation of the ServBGP design is realized on the NetKit network emulation and virtualization platform using the standard BGPv4 protocol. Moreover, the devised ServBGP design is developed from the ground up and deployed in a real cloud computing environment using Amazon EC2, Microsoft Azure, and Google App Engine cloud platforms.

Special issue: software maintenance and reengineering (CSMR 2011)

Special issue: software maintenance and reengineering (CSMR 2011)

Essential app engine

Essential app engine

Evaluating High-Performance Computing on Google App Engine

An experimental approach employs the Google App Engine (GAE) for high-performance parallel computing. A generic master-slave framework enables fast prototyping and integration of parallel algorithms that are transparently scheduled and executed on the Google cloud infrastructure. Compared to Amazon Elastic Compute Cloud (EC2), GAE offers lower resource-provisioning overhead and is cheaper for jobs shorter than one hour. Experiments demonstrated good scalability of a Monte Carlo simulation algorithm. Although this approach produced important speedup, two main obstacles limited its performance: middleware overhead and resource quotas.

ICanPlot: Visual Exploration of High-Throughput Omics Data Using Interactive Canvas Plotting

Increasing use of high throughput genomic scale assays requires effective visualization and analysis techniques to facilitate data interpretation. Moreover, existing tools often require programming skills, which discourages bench scientists from examining their own data. We have created iCanPlot, a compelling platform for visual data exploration based on the latest technologies. Using the recently adopted HTML5 Canvas element, we have developed a highly interactive tool to visualize tabular data and identify interesting patterns in an intuitive fashion without the need of any specialized computing skills. A module for geneset overlap analysis has been implemented on the Google App Engine platform: when the user selects a region of interest in the plot, the genes in the region are analyzed on the fly. The visualization and analysis are amalgamated for a seamless experience. Further, users can easily upload their data for analysis—which also makes it simple to share the analysis with collaborators. We illustrate the power of iCanPlot by showing an example of how it can be used to interpret histone modifications in the context of gene expression.

Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback

We describe a mobile health application that collects data relevant to the treatment of insomnia and other sleep-related problems. The application is based on the principles from neuroergonomics, which emphasizes assessment of the brain's alertness system in everyday, naturalistic environments, and ubiquitous computing. Application benefits include the ability to incorporate both embedded data collection and retrospective manual data input--thus providing the user with a rewarding data access process. The retrospective data input feature was evaluated by comparing an older version of the retrospective editing interface with a newly developed one. The time course of user interactions was precisely measured by exporting time stamps of user interactions using the Google App Engine. We also developed models that closely fit the time course of user interactions using the Goals, Operators, Methods, and Selection rules (GOMS) modeling method. The user data and GOMS models demonstrated that the retrospective sleep tracking feature of the new interface was faster to use but that the retrospective habit tracking feature was slower. Survey results indicated that participants enjoyed using the newly developed interface more than the old interface for the assessment of both sleep and habits. These findings indicate that a mobile application should be designed not only to reduce the amount of time it takes a user to input data, but also to conform to the user's mental models of its behavior.

Automatic conformance checking for migrating software systems to cloud infrastructures and platforms

SUMMARYThe migration of software systems to IaaS (infrastructure as a service)‐ or PaaS (platform as a service)‐based cloud environments enables SaaS providers to benefit from the cloud's merits, such as smoothly scaling up and down existing applications. Our approach, CloudMIG, aims at supporting SaaS providers to perform those migrations. Here, validating the specific constraints that are imposed by a cloud environment constitutes an important early‐phase activity. For example, the access to the file system, number of files, or calls to specific methods may be restricted by cloud providers. Those constraints have to be considered when evaluating the suitability of competing cloud environment candidates. In this paper, we describe CloudMIG's corresponding parts: a generic cloud environment model that incorporates these constraints and appropriate violation detection mechanisms. A software system's conformance can be examined with the assistance of constraint validators. They operate on extracted Knowledge Discovery Meta‐Model‐based system models and can, among others, apply metrics formulated with the Software Metrics Meta‐Model through our metrics execution engine. Additional constraint validators can be plugged into the validation process as needed. In this context, we implemented a prototype and modeled the PaaS environment Google App Engine for Java. We report on a quantitative evaluation regarding the detected constraint violations of five open‐source systems. Copyright © 2012 John Wiley & Sons, Ltd.

Privacy preserving collaborative filtering for SaaS enabling PaaS clouds

Recommender systems use, amongst others, a mechanism called collaborative filtering (CF) to predict the rating that a user will give to an item given the ratings of other items provided by other users. While reasonably accurate CF can be achieved with various well-known techniques, preserving the privacy of rating data from individual users poses a significant challenge. Several privacy preserving schemes have, so far, been proposed in prior work. However, while these schemes are theoretically feasible, there are many practical implementation difficulties on real world public cloud computing platforms. In this paper, we present our implementation experience and experimental results on two public Software-as-a-Service (SaaS) enabling Platform-as-a-Service (PaaS) clouds: the Google App Engine for Java (GAE/J) and the Amazon Web Services Elastic Beanstalk (AWS EBS).a

Reengineering from Tradition to Cloud: A Case Study

In China more and more universities are updating their online course system to Cloud-based Education Service, which actually is a re-engineering of online course system. While “Cloud-based Education Service” is not fullfledged, universities have lots of difficulties to deal with in the process of this transition. But what is for sure is that “Cloud-based Education Service” is based on “Could Computing Service.” To port the frame and refactor the data access layer of the existing system, we use Google App Engine, Google Web Toolkit and Ext-GWT with serviceoriented design. As a result, we successfully port the traditional online course system to Google Cloud Computing Platform. And it is proved that service-oriented developing style Google Cloud Computing Service provided is a scientific way to develop “Cloud-based Education Service”. It has the key functions that future “Cloud-based Education Service” requires. Our conclusion is that as Cloud Computing platform supports more and more traditional frameworks, less and less effort will be made to reengineer the online course system.

Towards Cloud to Device Push Messaging on Android: Technologies, Possibilities and Challenges

In this paper we look at different push messaging alternatives available for Android. Push messaging provides an important aspect of server to device communication, and we specifically focus on the integration of cloud computing with mobile devices through the use of push-based technologies. By conducting a benchmarking test, we investigate the performance of four relevant push technologies for the Android platform, namely C2DM, XMPP, Xtify and Urban Airship. The comparison focuses on three aspects of the libraries: 1) The stability; 2) Response times; and 3) Energy consumption. The test is conducted on both WLAN and 3G, and includes several mobile device types. Additionally, we also integrate with the Google App Engine to provide the cloud integration server that is responsible for sending push messages to the mobile devices.

Cloud computing: Web-based dynamic IT services

Cloud computing is a buzz-word in todays information technology (IT) that nobody can escape. But what is really behind it? There are many interpretations of this term, but no standardized or even uniform definition. Instead, as a result of the multi-faceted viewpoints and the diverse interests expressed by the various stakeholders, cloud computing is perceived as a rather fuzzy concept.With this book, the authors deliver an overview of cloud computing architecture, services, and applications. Their aim is to bring readers up to date on this technology and thus to provide a common basis for discussion, new research, and novel application scenarios. They first introduce the foundation of cloud computing with its basic technologies, such as virtualization and Web services. After that they discuss the cloud architecture and its service modules. The following chapters then cover selected commercial cloud offerings (including Amazon Web Services and Google App Engine) and management tools, and present current related open-source developments (including Hadoop, Eucalyptus, and Open CirrusTM). Next, economic considerations (cost and business models) are discussed, and an evaluation of the cloud market situation is given. Finally, the appendix contains some practical examples of how to use cloud resources or cloud applications, and a glossary provides concise definitions of key terms.The authors presentation does not require in-depth technical knowledge. It is equally intended as an introduction for students in software engineering, web technologies, or business development, for professional software developers or system architects, and for future-oriented decision-makers like top executives and managers.

From Experience to Expertise: Digesting Cumulative Information for Informational Web Search

Web searching has become a necessary activity for most Internet users. People can reach a specific website or collect desired information by using search engines. Nevertheless, when a user is unfamiliar with his or her search target, e.g., learning a previously unknown topic, browsing and understanding the web pages in the corresponding search result requires extra effort. In such an informational web search, the user accumulates information cues by reading web pages to approach the search target. For example, a user can gradually understand ”cloud computing” after obtaining some information about relevant terms, e.g., distributed computing, Google App Engine, and Amazon EC2. In this work, we propose an ”immediacy of interest” indicator, measured in terms of information freshness, to evaluate the possible user interest in a web page contained in a search result. Moreover, to address relevance in search sessions, the topic map is also utilized in our proposed scheme. Empirical studies show that informational web searching tasks can be completed efficiently and effectively with our approach.

Cloud Computing: Case Studies and Total Cost of Ownership

This paper consists of four major sections: The first section is a literature review of cloud computing and a cost model. The next section focuses on detailed overviews of cloud computing and its levels of services: SaaS, PaaS, and IaaS. Major cloud computing providers are introduced, including Amazon Web Services (AWS),Microsoft Azure, and Google App Engine. Finally, case studies of implementing web applications on IaaS and PaaS using AWS, Linode and Google AppEngine are demonstrated. Justifications of running on an IaaS provider (AWS) and running on a PaaS provider (Google AppEngine) are described. The last section discusses costs and technology analysis comparing cloud computing with local managed storage and servers. The total costs of ownership (TCO) of an AWS small instance are significantlylower, but the TCO of a typical 10TB space in Amazon S3 aresignificantly higher. Since Amazon offers lower storage pricing for huge amounts of data, the TCO might be lower. Readers should do their own analysis on the TCOs.

The Design and Implementation of Reusable Data Reduction Tool in Cloud Computing Platform

Cloud computing model makes software reuse from the simple code reuse to the service-oriented software reuse .This paper based on the extended Google App Engine cloud computing platform, designed a reusable data reduction tool, which mainly to solve the access problem of heterogeneous data and an un-existed data type. In order to achieve data reuse, a layered data structure definition based on the reuse is provided. Finally, a data-mining system built on top of cloud computing platform is designed and implemented to verify the efficiency and the validity of data reduction module.

Building Intelligent Virtual Learning Community Based on GAE

Virtual learning community has become the main online learning place for learners, and cloud computing provides new tools for the development of virtual learning community. Based on the Google APP Engine (GAE) cloud computing platform provided by Google Company, the article explained the construction of the virtual learning community in cloud computing environment.

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

AbstractToday, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.

Web 2.0-based SaaS for Community Resource Sharing

Cloud computing is an emerging paradigm where computing resources are offered over the Internet. Web 2.0 is also the mainstream technology in the existing Internet. How to combine the two domains is the main purpose of this paper. Software as a Service (SaaS) is one of the important service models of cloud computing. This study presents a Web 2.0-based Software as a Service (Web 2.0-based SaaS) model based on Web 2.0 Mashups for integrating Web 2.0 technologies into SaaS. Based on the proposed Web-based SaaS model, a RSS-based Community Resource Sharing System, called RCRSS, is developed, and then is deployed in the Google App Engine platform. To illustrate the RCRSS applications, we demonstrate resource sharing of community can be acquired from heterogeneous and distributed resources, including Blog, Facebook, Social Bookmark, and Web Multimedia.

OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript

BackgroundOntologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups.ResultsOntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application.ConclusionsOntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases.Availabilityhttp://www.ontocat.org

Risk Assessment for Cloud-Based IT Systems

The use of Cloud Computing services is an attractive option to improve IT systems to achieve rapidly and elastically provisioned capability, and also to offer economic benefits. However, companies see security as a major concern in migrating to the Cloud. To bring clarity in Cloud security, this paper presents a systematic approach to manage the risks and analyzes the full range of risk in Cloud Computing solutions. Furthermore, as a study case, Google App Engine Platform is assessed based on ISO/IEC 27002 and OWASP Top 10 Risk List in this paper. Knowing the risks of Cloud solutions, companies can execute well-informed decisions on going into the Cloud and build their Cloud solutions in a secure way, relying on a robust e-trust relationship.

A Comprehensive Approach to Ensure Secure Data Communication in Cloud Environment

computing is an Internet based development, in concept, it is a paradigm shift whereby details are abstracted from the users who no longer need knowledge of, expertise in, or control over the technology infrastructure that supports them. According to a 2008 IEEE paper, ―Cloud Computing is a paradigm in which information is permanently stored in servers on the internet and cached temporarily on clients that include desktops, entertainment centers, table computers and notebooks etc.‖ Some examples of emerging Cloud computing infrastructures are Microsoft Azure, Amazon EC2, Google App Engine, and Aneka. Cloud service providers enable users to access and use the necessary resources via the internet. To provide these resources, providers often fall back upon other providers in the cloud, hence this raises security issues in Cloud Environment as Clouds have no borders and the data can be physically located anywhere in the world. So this phenomenon raises serious issues regarding user authentication and data confidentiality. Hence it is proposed to implement a simple Data Protection framework which performs authentication, verification and encrypted data transfer, thus maintaining data confidentiality. Programming is performed using JAVA platform, Cloud environment is created using wired and wireless LAN networks. And Advanced Encryption Standard security algorithm is implemented for ensuring security framework.