ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Educational Technology and Related Education Conferences for January to June 2011 - November 11, 2010

If you attend the same conferences each year, you don’t need to scan this list. This list is your opportunity to “push the envelope” by trying something new. There are hundreds of professional development events that may give you a different perspective or help you learn a new skill. Rather than attend the same event you always do, scan this list and investigate conferences, symposiums, or workshops you have never attended. The list below covers selected events focused primarily on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until June 2011 are complete as dates, locations, or URLs are not available for a number of events held after June 2011. A Word 2003 format is used to enable people who do not have access to Word 2007 or higher version and those with limited or high-cost Internet access to find a conference that is congruent with their interests or obtain conference proceedings. (If you are seeking a more interactive listing, refer to online conference sites.) Consider using the “Find” tool under Microsoft Word’s “Edit” tab or similar tab in OpenOffice to locate the name of a particular conference, association, city, or country. If you enter the country “United Kingdom” in the “Find” tool, all conferences that occur in the United Kingdom will be highlighted. Then, “cut and paste” a list of suitable events for yourself and your colleagues. Please note that events, dates, titles, and locations may change; thus, CHECK the specific conference website. Note also that some events will be cancelled at a later date. All Internet addresses were verified at the time of publication. No liability is assumed for any errors that may have been introduced inadvertently during the assembly of this conference list. If possible, please do not remove the contact information when you re-distribute the list as that is how I receive updates and corrections. If you publish the list on the web, please note its source

EvLog: Evolving Log Analyzer for Anomalous Logs Identification

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving log events, and unstable log sequences. In this paper, we propose a novel unsupervised approach named Evolving Log analyzer (EvLog) to mitigate these challenges. We first build a multi-level representation extractor to process logs without parsing to prevent errors from the parser. The multi-level representations preserve the essential semantics of logs while leaving out insignificant changes in evolving events. EvLog then implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively, which outperforms other state-of-the-art approaches by a wide margin. To our best knowledge, this is the first study on tackling anomalous logs over software evolution. We believe our work sheds new light on the impact of software evolution with the corresponding solutions for the log analysis community

Educational Technology and Related Education Conferences for June to December 2011

This potpourri of educational technology conferences includes gems such as “Saving Your Organisation from Boring eLearning” and “Lessons and Insights from Ten eLearning Masters”. And, if you wish, you can “Be an Open Learning Hero”. You will also find that the number of mobile learning conferences (and conferences that have a mobile learning component) have increased significantly. Countries such as China, Indonesia, Japan, and Thailand have shown a keen interest in mobile learning. It would be impossible for you to be present at all the conferences that you would like to attend. But, you could go to the conference website/url during and after the conference. Many conference organizers post abstracts, full papers, and/or videos of conference presentations. Thus, you can visit the conference virtually and may encounter information and contacts that would be useful in your work. The list below covers selected events focused primarily on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2011 are complete as dates, locations, or URLs are not available for a number of events held after December 2011. But, take a look at the conference organizers who planned ahead in 2012. A Word 2003 format is used to enable people who do not have access to Word 2007 or higher version and those with limited or high-cost Internet access to find a conference that is congruent with their interests or obtain conference proceedings. (If you are seeking a more interactive listing, refer to online conference sites.) Consider using the “Find” tool under Microsoft Word’s “Edit” tab or similar tab in OpenOffice to locate the name of a particular conference, association, city, or country. If you enter the country “Australia” or “Singapore” in the “Find” tool, all conferences that occur in Australia or Singapore will be highlighted. Or, enter the word “research”. Then, “cut and paste” a list of suitable events for yourself and your colleagues. Please note that events, dates, titles, and locations may change; thus, CHECK the specific conference website. Note also that some events will be cancelled at a later date. All Internet addresses were verified at the time of publication. No liability is assumed for any errors that may have been introduced inadvertently during the assembly of this conference list. If possible, do not remove the contact information when you re-distribute the list as that is how I receive updates and corrections. If you mount the list on the web, please note its source

Automated Realistic Test Input Generation and Cost Reduction in Service-centric System Testing

Service-centric System Testing (ScST) is more challenging than testing traditional software due to the complexity of service technologies and the limitations that are imposed by the SOA environment. One of the most important problems in ScST is the problem of realistic test data generation. Realistic test data is often generated manually or using an existing source, thus it is hard to automate and laborious to generate. One of the limitations that makes ScST challenging is the cost associated with invoking services during testing process. This thesis aims to provide solutions to the aforementioned problems, automated realistic input generation and cost reduction in ScST. To address automation in realistic test data generation, the concept of Service-centric Test Data Generation (ScTDG) is presented, in which existing services used as realistic data sources. ScTDG minimises the need for tester input and dependence on existing data sources by automatically generating service compositions that can generate the required test data. In experimental analysis, our approach achieved between 93% and 100% success rates in generating realistic data while state-of-the-art automated test data generation achieved only between 2% and 34%. The thesis addresses cost concerns at test data generation level by enabling data source selection in ScTDG. Source selection in ScTDG has many dimensions such as cost, reliability and availability. This thesis formulates this problem as an optimisation problem and presents a multi-objective characterisation of service selection in ScTDG, aiming to reduce the cost of test data generation. A cost-aware pareto optimal test suite minimisation approach addressing testing cost concerns during test execution is also presented. The approach adapts traditional multi-objective minimisation approaches to ScST domain by formulating ScST concerns, such as invocation cost and test case reliability. In experimental analysis, the approach achieved reductions between 69% and 98.6% in monetary cost of service invocations during testin

Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich system runtime information they contain. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on intelligent log analytics powered by AI (artificial intelligence) techniques. However, only a small fraction of these techniques have reached successful deployment in industry because of the lack of public log datasets and necessary benchmarking upon them. To fill this significant gap between academia and industry and also facilitate more research on AI-powered log analytics, we have collected and organized loghub, a large collection of log datasets. In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. Up to the time of this paper writing, loghub datasets have been downloaded over 15,000 times by more than 380 organizations from both industry and academia.Comment: Dateset available at https://zenodo.org/record/322717

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment. Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit. Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of our scheduling and that it tolerates temporal failures