Extended Fault Taxonomy of SOA-Based Systems

Service Oriented Architecture (SOA) is considered as a standard for enterprise software development. The main characteristics of SOA are dynamic discovery and composition of software services in a heterogeneous environment. These properties pose newer challenges in fault management of SOA-based systems (SBS). A proper understanding of different faults in an SBS is very necessary for effective fault handling. A comprehensive three-fold fault taxonomy is presented here that covers distributed, SOA specific and non-functional faults in a holistic manner. A comprehensive fault taxonomy is a key starting point for providing techniques and methods for accessing the quality of a given system. In this paper, an attempt has been made to outline several SBSs faults into a well-structured taxonomy that may assist developers to plan suitable fault repairing strategies. Some commonly emphasized fault recovery strategies are also discussed. Some challenges that may occur during fault handling of SBSs are also mentioned

Dependability analysis of web services

Web Services form the basis of the web based eCommerce eScience applications so it is vital that robust services are developed. Traditional validation and verification techniques are centred around the concept of removing all faults to guarantee correct operation whereas Dependability gives an assessment of how dependably a system can deliver the required functionality by assessing attributes, and by eliminating threats via means attempts to improve dependability. Fault injection is a well-proven dependability assessment method. Although much work has been done in the area of fault injection and distributed systems in general, there appears to have been little research carried out on applying this to middleware systems and Web Services in particular. There are additional problems associated with applying existing fault injection technologies to Web Services running in a virtual machine environment since most are either invasive or work at a machine level. The Fault Injection Technology (FIT) method has been devised to address these problems for middleware systems. The Web Service-Fault Injection Technology (WS-FIT) implementation applies the FIT method, based on network level fault injection, to Web Services to create a non-invasive dependability assessment method. It allows targeted perturbation of Web Service RFC parameters as well as more traditional network level fault injection operations. The WS-FIT tool includes taxonomies that define a system under test, fault models to apply and failure modes to be detected, and uses these taxonomies to generate fault injection campaigns. WS-FIT has been applied to a number of case studies and has successfully demonstrated its effectiveness. It has also been successfully applied to a third-party system to evaluate dependability means. It performed this dependability assessment as well as allowing debugging of the means to be undertaken uncovering unknown faults

A survey on fault-models for QoS studies of service-oriented systems

This survey paper presents an overview of the fault-models available to the researcher who wants to parameterise system-models in order to study Quality- of-Service (QoS) properties of systems with service-oriented architecture. The concept of a system-model subsumes the whole spectrum between abstract mathematical models and testbeds based on actual implementations. Fault- models, on the other hand, are parameters to system-models. They introduce faults and disturbances into the system-model, thereby allowing the study of QoS under realistic conditions. In addition to a survey of existing fault- models, the paper also provides a discussion of available fault-classification schemes

Cloud computing services: taxonomy and comparison

Cloud computing is a highly discussed topic in the technical and economic world, and many of the big players of the software industry have entered the development of cloud services. Several companies what to explore the possibilities and benefits of incorporating such cloud computing services in their business, as well as the possibilities to offer own cloud services. However, with the amount of cloud computing services increasing quickly, the need for a taxonomy framework rises. This paper examines the available cloud computing services and identifies and explains their main characteristics. Next, this paper organizes these characteristics and proposes a tree-structured taxonomy. This taxonomy allows quick classifications of the different cloud computing services and makes it easier to compare them. Based on existing taxonomies, this taxonomy provides more detailed characteristics and hierarchies. Additionally, the taxonomy offers a common terminology and baseline information for easy communication. Finally, the taxonomy is explained and verified using existing cloud services as examples

An SOA-Based Framework of Computational Offloading for Mobile Cloud Computing

Mobile Computing is a technology that allows transmission of audio, video, and other types of data via a computer or any other wireless-enabled device without having to be connected to a fixed physical link. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, connection instability, and limited computational power. In particular, the advent of connecting mobile devices to the internet offers the possibility of offloading computation and data intensive tasks from mobile devices to remote cloud servers for efficient execution. This proposed thesis develops an algorithm that uses an objective function to adaptively decide strategies for computational offloading according to changing context information. By following the style of Service-Oriented Architecture (SOA), the proposed framework brings cloud computing to mobile devices for mobile applications to benefit from remote execution of tasks in the cloud. This research discusses the algorithm and framework, along with the results of the experiments with a newly developed system for self-driving vehicles and points out the anticipated advantages of Adaptive Computational Offloading