Introduction to the special section on dependable network computing

Dependable network computing is becoming a key part of our daily economic and social life. Every day, millions of users and businesses are utilizing the Internet infrastructure for real-time electronic commerce transactions, scheduling important events, and building relationships. While network traffic and the number of users are rapidly growing, the mean-time between failures (MTTF) is surprisingly short; according to recent studies, in the majority of Internet backbone paths, the MTTF is 28 days. This leads to a strong requirement for highly dependable networks, servers, and software systems. The challenge is to build interconnected systems, based on available technology, that are inexpensive, accessible, scalable, and dependable. This special section provides insights into a number of these exciting challenges

Evaluation of Software Diversity for Detecting Hardware Faults

In this paper, we describe how software diversity can be evaluated on the basis of fault coverage by analysing functionality. A diverse function will spread checks throughout the considered hardware. To examine the fault detection behavior of such checks, we investigate a pure functional language and describe data flow through this diverse function by using its computation tree. Then, we evaluate the difference in data flow for each diverse function by using a graph-metrics called distance. Based on this distance, we present a definition for measuring diversity. Furthermore, we illustrate the application of this approach on a floating-point-adder and give examples of diverse functions. Finally, we estimate the fault coverage and compare our results with those of a fault simulation program for single stuck-at faults. Index Terms - software-based fault tolerance, data flow analysis, software diversity, software measure, fault simulation 1 Introduction Recently, many authors have propo..

Machine learning-based management of cloud applications in hybrid clouds: A Hadoop case study

This paper illustrates the effort to integrate a machine learning-based framework which can predict the remaining time to failure of computing nodes with Hadoop applications. This work is part of a larger effort targeting the development of a cloud-oriented autonomic framework to increase the availability of applications subject to software anomalies, and to jointly improve their performance. The framework uses machine-learning, software rejuvenation, and load distribution techniques to proactively prevent failures. We believe that this work allows to set a possible path towards the definition of best practices for the development of systems to support autonomic management of cloud applications, illustrating what are the issues that should be addressed by the research community. Indeed, given the scale and the complexity of modern computing infrastructures, effective autonomic management approaches of cloud applications are becoming mandatory

A performance analysis of EC2 cloud computing services for scientific computing

Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks and kernels. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community