Abstract
Shared-nothing clusters are a well known and cost-effective approach to database server scalability, in particular, with highly intensive read-only workloads typical of many 3-tier web-based applications. The common reliance oil a centralized component and a simplistic propagation strategy employed by mainstream solutions however conduct to poor scalability with traditional on-line transaction processing (OLTP), where the Update ratio is high. Such approaches also pose in additional obstacle to high availability while introducing a single point Of failure. More recently, database replication protocols based on group communication have been shown to overcome such limitations, expanding the applicability of shared-nothing Clusters to more demanding transactional workloads. These take simultaneous advantage of total order multicast and transactional semantics to improve oil mainstream solutions. However, none has already been widely deployed in a general purpose database management system. In this paper, we argue that it major hurdle for their acceptance is that these proposals have disappointing performance with specific subsets of real-world workloads. Such limitations are deep-rooted and working around them requires in-depth understanding of protocols and changes to applications. We address this issue with a novel protocol that combines multiple transaction execution mechanisms and replication techniques and then show how it avoids the identified pitfalls. Experimental results are obtained with it workload based oil the industry standard TPC-C benchmark.

Abstract

Abstract
Adaptation of system parameters is acknowledged as a requirement to scalable and dependable distributed systems. Unfortunately, adaptation cannot be effective when provided solely by individual system components as the correct decision is often tied to the composition itself and the system as a whole. In fact, proper adaption is a cross-cutting issue: Diagnostic and feedback operations must target multiple components and do it at different abstraction levels. We address this problem with the SERPENTINE middleware platform. By relying on the industry standard JMX as a service interface, it can monitor and operate on a wide range of distributed middleware and application components. By building on a JMX-enabled OSGi runtime, SERPENTINE is able to control the life-cycle of components themselves. The scriptable stateless server and cascading architecture allow for increased dependability and flexibility.

Abstract

Abstract

Abstract
This work present and demonstrated an applications of artificial neural network approach in optical sensing. The conventional matrix method used in simultaneous measurement of strain and temperature with optical Bragg gratings is compared with artificial neural network approach. The alternative method is proposed for reduced the error.