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This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO), we call it ALPSO-II. In order to improve the performance of ALPSO on multi-modal problems, we introduce several new major features in ALPSO-II: (i) Adding particle's status monitoring mechanism, (ii) controlling the number of particles that learn from the global best position, and (iii) updating two of...
The problem of dynamic service composition of SOA systems based on QoS requirements may be framed as one of automatic generation of policies according to desirable level of QoS characteristics. In this paper, we provide a model driven approach to develop an optimal service composition policies for SOA systems with defined SLAs metrics and QoS constraints. Our work utilizes ideas from Dynamic Programming...
Monitoring plays a significant role in improving the quality of service in cloud computing. It helps clouds to scale resource utilization adaptively, to identify defects in services for service developers, and to discover usage patterns of numerous end users. However, due to the heterogeneity of components in clouds and the complexity arising from the wealth of runtime information, monitoring in clouds...
As the intensification of global competition and the complexity of manufacturing products, enterprise manufacturing information change more and more complex. To effective manage complex manufacturing information in product manufacturing process, based on holonic theory, the manufacturing information integration management framework based on holonic is established. By means of the characteristic analysis...
Service-oriented collective intelligence, which creates new value by combining services provided by various organizations via services computing technologies, has been gaining in importance with the development of services computing technologies. Because collective intelligence needs many participants, it is crucial to build a framework where a wide variety of policies of service providers are satisfied...
Service compositions need to continuously self- adapt to cope with unexpected failures. In this context adaptation becomes a fundamental requirement that must be elicited along with the other functional and non functional requirements. Beside modelling, effective adaptation also demands means to trigger it at runtime as soon as the actual behavior of the composition deviates from stated requirements...
We observe two of the recent trends in information technology. Cloud Computing (CC) is widely accepted as an effective reuse paradigm. Mobile Computing with Mobile Internet Device (MID) such as iPhones and Android devices becomes a convenient alternative to personal computers by integrating mobility, communication, software functionality, and entertainment. Due to the resource limitations of MIDs,...
Cloud computing paradigm contains many shared resources, such as infrastructures, data storage, various platforms and software. Resource monitoring involves collecting information of system resources to facilitate decision making by other components in Cloud environment. It is the foundation of many major Cloud computing operations. In this paper, we extend the prevailing monitoring methods in Grid...
Service-Oriented Architectures address the development of distributed and dynamic service-based applications. Due to the dynamics of their environments, services should be self-adaptable in order to maintain agreed resource-level qualities. To support building such services, this work proposes the Self-Adaptable Service Execution Manager (SASEM), responsible for monitoring and controlling the service...
A new on-line predictive monitoring methodology is introduced, based on a combined adaptive principal component analysis (PCA) and an adaptive predictor filter in an autoregressive (AR) formulation. An adaptive PCA-based monitoring scheme is developed utilizing the process historical data to recursively track the dynamic behavior of the industrial process plant based on statistical Hotelling and squared...
Many heterogeneous embedded systems, for example industrial automation and automotive applications, require hard-real time constraints to be exhaustively verified - which is a challenging task for the verification engineer. To cope with complexity, verification techniques working on different abstraction levels are best practice. SystemC is a versatile C++ based design and verification language, offering...
Web services and service-oriented architecture (SOA) have become the de facto standard for designing distributed and loosely coupled applications. Many service-based applications demand for a mix of interactions between humans and Software-Based Services (SBS). An example is a process model comprising SBS and services provided by human actors. Such applications are difficult to manage due to changing...
The widespread of embedded computer networks as part of everyday peoples' lives is leading the current research towards smart environments and Ambient Intelligence (AmI). AmI is a new information paradigm where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive and responsive to their needs. In this paper, we describe the intelligent...
Low simulation speeds have a critical impact on the design process by limiting the number of design options which can be explored. Sampling is a popular fast simulation technique because it can achieve high simulation speed and high accuracy. However state-of-the-art sampling techniques either consider warm-up as an orthogonal issue and leave the choice of a warm-up technique to the end user, or require...
In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify...
The emerging production paradigms, potentiated by the advances in Information Technologies (IT), especially in web related standards and technologies as well as the progressive acceptance of the multiagent systems (MAS) concept and related technologies, envision collections of modules whose individual and collective function adapts and evolves ensuring the fitness and adequacy of the shop floor in...
The present paper proposes a tool based on fuzzy Petry Nets for modeling the recurrent of the decision functions. The analysed system- a producer-consumer distributed energy production systems is structured on hierarchical levels and is synchronized in relation with successive external requests. Thus, each hierarchical level is capable to repeatedly carry out a fixed decision scheme and executes this...
Several activities in service oriented computing can benefit from knowing properties of a given service composition ahead of time. We will focus here on properties related to computational cost and resource usage, in a wide sense, as they can be linked to QoS characteristics. In order to attain more accuracy, we formulate computational cost / resource usage as functions on input data (or appropriate...
We propose the PREvent framework, which is a system that integrates event-based monitoring, prediction of SLA violations using machine learning techniques, and automated runtime prevention of those violations by triggering adaptation actions in service compositions. PREvent improves on related work in that it can be used to prevent violations ex ante, before they have negatively impacted the provider's...
This paper presents an approach to the runtime management of decentralized service compositions. The goal of the approach is to transparently observe the behavior of peer-to-peer service interaction in order to permit the introduction of adaptive behavior. The design includes a monitoring component that utilizes a choreography model to formulate a global view of the application, and introduce adaptive...
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