Optimizing decomposition of software architecture for local recovery

Cataloged from PDF version of article.The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software

Optimizing decomposition of software architecture for local recovery

The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software. © 2011 Springer Science+Business Media, LLC

Architecting fault-tolerant software systems

The increasing size and complexity of software systems makes it hard to prevent or remove all possible faults. Faults that remain in the system can eventually lead to a system failure. Fault tolerance techniques are introduced for enabling systems to recover and continue operation when they are subject to faults. Many fault tolerance techniques are available but incorporating them in a system is not always trivial. We consider the following problems in designing a fault-tolerant system. First, existing reliability analysis techniques generally do not prioritize potential failures from the end-user perspective and accordingly do not identify sensitivity points of a system. \ud Second, existing architecture styles are not well-suited for specifying, communicating and analyzing design decisions that are particularly related to the fault-tolerant aspects of a system. Third, there are no adequate analysis techniques that evaluate the impact of fault tolerance techniques on the functional decomposition of software architecture. Fourth, realizing a fault-tolerant design usually requires a substantial development and maintenance effort. \ud To tackle the first problem, we propose a scenario-based software architecture reliability analysis method, called SARAH that benefits from mature reliability engineering techniques (i.e. FMEA, FTA) to provide an early reliability analysis of the software architecture design. SARAH evaluates potential failures from the end-user perspective to identify sensitive points of a system without requiring an implementation. \ud As a new architectural style, we introduce Recovery Style for specifying fault-tolerant aspects of software architecture. Recovery Style is used for communicating and analyzing architectural design decisions and for supporting detailed design with respect to recovery. \ud As a solution for the third problem, we propose a systematic method for optimizing the decomposition of software architecture for local recovery, which is an effective fault tolerance technique to attain high system availability. To support the method, we have developed an integrated set of tools that employ optimization techniques, state-based analytical models (i.e. CTMCs) and dynamic analysis on the system. The method enables the following: i ) modeling the design space of the possible decomposition alternatives, ii ) reducing the design space with respect to domain and stakeholder constraints and iii ) making the desired trade-off between availability and performance metrics. \ud To reduce the development and maintenance effort, we propose a framework, FLORA that supports the decomposition and implementation of software architecture for local recovery. The framework provides reusable abstractions for defining recoverable units and for incorporating the necessary coordination and communication protocols for recovery

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications

Design techniques for low-power systems

Portable products are being used increasingly. Because these systems are battery powered, reducing power consumption is vital. In this report we give the properties of low-power design and techniques to exploit them on the architecture of the system. We focus on: minimizing capacitance, avoiding unnecessary and wasteful activity, and reducing voltage and frequency. We review energy reduction techniques in the architecture and design of a hand-held computer and the wireless communication system including error control, system decomposition, communication and MAC protocols, and low-power short range networks