2,233 research outputs found
Modeling Adaptation with Klaim
In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present an investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and use of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some well-known adaptation techniques: the IBM MAPE-K loop, the Accord component-based framework for architectural adaptation, and the aspect- and context-oriented programming paradigms. We illustrate our approach through a simple example concerning a data repository equipped with an automated cache mechanism
Microservice Transition and its Granularity Problem: A Systematic Mapping Study
Microservices have gained wide recognition and acceptance in software
industries as an emerging architectural style for autonomic, scalable, and more
reliable computing. The transition to microservices has been highly motivated
by the need for better alignment of technical design decisions with improving
value potentials of architectures. Despite microservices' popularity, research
still lacks disciplined understanding of transition and consensus on the
principles and activities underlying "micro-ing" architectures. In this paper,
we report on a systematic mapping study that consolidates various views,
approaches and activities that commonly assist in the transition to
microservices. The study aims to provide a better understanding of the
transition; it also contributes a working definition of the transition and
technical activities underlying it. We term the transition and technical
activities leading to microservice architectures as microservitization. We then
shed light on a fundamental problem of microservitization: microservice
granularity and reasoning about its adaptation as first-class entities. This
study reviews state-of-the-art and -practice related to reasoning about
microservice granularity; it reviews modelling approaches, aspects considered,
guidelines and processes used to reason about microservice granularity. This
study identifies opportunities for future research and development related to
reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table
Performance-oriented Cloud Provisioning: Taxonomy and Survey
Cloud computing is being viewed as the technology of today and the future.
Through this paradigm, the customers gain access to shared computing resources
located in remote data centers that are hosted by cloud providers (CP). This
technology allows for provisioning of various resources such as virtual
machines (VM), physical machines, processors, memory, network, storage and
software as per the needs of customers. Application providers (AP), who are
customers of the CP, deploy applications on the cloud infrastructure and then
these applications are used by the end-users. To meet the fluctuating
application workload demands, dynamic provisioning is essential and this
article provides a detailed literature survey of dynamic provisioning within
cloud systems with focus on application performance. The well-known types of
provisioning and the associated problems are clearly and pictorially explained
and the provisioning terminology is clarified. A very detailed and general
cloud provisioning classification is presented, which views provisioning from
different perspectives, aiding in understanding the process inside-out. Cloud
dynamic provisioning is explained by considering resources, stakeholders,
techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table
ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING
Service-Oriented Architecture (SOA) enables composition of large and complex
computational units out of the available atomic services. However, implementation of
SOA, for its dynamic nature, could bring about challenges in terms of service
discovery, service interaction, service composition, robustness, etc. In the near future,
SOA will often need to dynamically re-configuring and re-organizing its topologies of
interactions between the web services because of some unpredictable events, such as
crashes or network problems, which will cause service unavailability. Complexity and
dynamism of the current and future global network system require service architecture
that is capable of autonomously changing its structure and functionality to meet
dynamic changes in the requirements and environment with little human intervention.
This then needs to motivate the research described throughout this thesis.
In this thesis, the idea of introducing autonomy and adapting case-based reasoning
into SOA in order to extend the intelligence and capability of SOA is contributed and
elaborated. It is conducted by proposing architecture of an autonomic SOA
framework based on case-based reasoning and the architectural considerations of
autonomic computing paradigm. It is then followed by developing and analyzing
formal models of the proposed architecture using Petri Net. The framework is also
tested and analyzed through case studies, simulation, and prototype development. The
case studies show feasibility to employing case-based reasoning and autonomic
computing into SOA domain and the simulation results show believability that it
would increase the intelligence, capability, usability and robustness of SOA. It was
shown that SOA can be improved to cope with dynamic environment and services
unavailability by incorporating case-based reasoning and autonomic computing
paradigm to monitor and analyze events and service requests, then to plan and execute
the appropriate actions using the knowledge stored in knowledge database
Trust Management in the Internet of Everything
Digitalization is leading us towards a future where people, processes, data
and things are not only interacting with each other, but might start forming
societies on their own. In these dynamic systems enhanced by artificial
intelligence, trust management on the level of human-to-machine as well as
machine-to-machine interaction becomes an essential ingredient in supervising
safe and secure progress of our digitalized future. This tutorial paper
discusses the essential elements of trust management in complex digital
ecosystems, guiding the reader through the definitions and core concepts of
trust management. Furthermore, it explains how trust-building can be leveraged
to support people in safe interaction with other (possibly autonomous) digital
agents, as trust governance may allow the ecosystem to trigger an auto-immune
response towards untrusted digital agents, protecting human safety.Comment: Proceedings of the 16th European Conference on Software
Architecture-Companion Volum
An autonomic traffic analysis proposal using Machine Learning techniques
International audienceNetwork analysis has recently become in one of the most challenging tasks to handle due to the rapid growth of communication technologies. For network management, accurate identification and classification of network traffic is a key task. For example, identifying traffic from different applications is critical to manage bandwidth resources and to ensure Quality of Service objectives. Machine learning emerges as a suitable tool for traffic classification; however, it requires several steps that must be followed adequately in order to achieve the goals. In this paper, we proposed an architecture to perform traffic analysis based on Machine Learning techniques and autonomic computing. We analyze the procedures to perform Machine Learning over traffic network classification, and at the same time we give guidelines to introduce all these procedures into the architecture proposed. The main contribution of our proposal is the reconfiguration of the traffic classifier that will change according to the knowledge adquired from the traffic analysis process
- âŠ