Building an Expert System for Evaluation of Commercial Cloud Services

Commercial Cloud services have been increasingly supplied to customers in industry. To facilitate customers' decision makings like cost-benefit analysis or Cloud provider selection, evaluation of those Cloud services are becoming more and more crucial. However, compared with evaluation of traditional computing systems, more challenges will inevitably appear when evaluating rapidly-changing and user-uncontrollable commercial Cloud services. This paper proposes an expert system for Cloud evaluation that addresses emerging evaluation challenges in the context of Cloud Computing. Based on the knowledge and data accumulated by exploring the existing evaluation work, this expert system has been conceptually validated to be able to give suggestions and guidelines for implementing new evaluation experiments. As such, users can conveniently obtain evaluation experiences by using this expert system, which is essentially able to make existing efforts in Cloud services evaluation reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24, 201

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Real-time Spatial Detection and Tracking of Resources in a Construction Environment

Construction accidents with heavy equipment and bad decision making can be based on poor knowledge of the site environment and in both cases may lead to work interruptions and costly delays. Supporting the construction environment with real-time generated three-dimensional (3D) models can help preventing accidents as well as support management by modeling infrastructure assets in 3D. Such models can be integrated in the path planning of construction equipment operations for obstacle avoidance or in a 4D model that simulates construction processes. Detecting and guiding resources, such as personnel, machines and materials in and to the right place on time requires methods and technologies supplying information in real-time. This paper presents research in real-time 3D laser scanning and modeling using high range frame update rate scanning technology. Existing and emerging sensors and techniques in three-dimensional modeling are explained. The presented research successfully developed computational models and algorithms for the real-time detection, tracking, and three-dimensional modeling of static and dynamic construction resources, such as workforce, machines, equipment, and materials based on a 3D video range camera. In particular, the proposed algorithm for rapidly modeling three-dimensional scenes is explained. Laboratory and outdoor field experiments that were conducted to validate the algorithm’s performance and results are discussed

Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.Comment: 8 pages, Proceedings of the 5th International Conference on Cloud Computing (IEEE CLOUD 2012), pp. 344-351, Honolulu, Hawaii, USA, June 24-29, 201