Tuning adaptive computations for the performance improvement of applications in JEE server

With the increasing use of autonomic computing technologies, a Java Enterprise Edition (JEE) application server is implemented with more and more adaptive computations for self-managing the Middleware as well as its hosted applications. However, these adaptive computations consume resources such as CPU and memory, and can interfere with the normal business processing of applications at runtime due to resource competition, especially when the whole system is under heavy load. Tuning these adaptive computations from the perspective of resource management becomes necessary. In this article, we propose a tuning model for adaptive computations. Based on the model, tuning is carried out dynamically by upgrading or degrading the autonomic level of an adaptive computation so as to control its resource consumption. We implement the RSpring tuner and use it to optimize autonomic JEE servers such as PkuAS and JOnAS. RSpring is evaluated on ECperf and RUBiS benchmark applications. The results show that it can effectively improve the application performance by 13.6 % in PkuAS and 19.2 % in JOnAS with the same amount of resources. ? 2012 The Brazilian Computer Society.EI02143-158

A Model-Based Monitoring Architecture for Heterogeneous Enterprise Services and Information Systems

Runtime management of distributed information systems is a complex and costly activity. One of the main challenges that must be addressed is obtaining a complete and updated view of all the managed runtime resources. This article presents a monitoring architecture for heterogeneous and distributed information systems. It is composed of two elements: an information model and an agent infrastructure. The model negates the complexity and variability of these systems and enables the abstraction over non-relevant details. The infrastructure uses this information model to monitor and manage the modeled environment, performing and detecting changes in execution time. The agents infrastructure is further detailed and its components and the relationships between them are explained. Moreover, the proposal is validated through a set of agents that instrument the JEE Glassfish application server, paying special attention to support distributed configuration scenarios

Adaptable Service Oriented Infrastructure Provisioning with Lightweight Containers Virtualization Technology

Modern computing infrastructures should enable realization of converged provisioning and governance operations on virtualized computing, storage and network resources used on behalf of users' workloads. These workloads must have ensured sufficient access to the resources to satisfy required QoS. This requires flexible platforms providing functionality for construction, activation and governance of Runtime Infrastructure which can be realized according to Service Oriented Infrastructure (SOI) paradigm. Implementation of the SOI management framework requires definition of flexible architecture and utilization of advanced software engineering and policy-based techniques. The paper presents an Adaptable SOI Provisioning Platform which supports adaptable SOI provisioning with lightweight virtualization, compliant with the structured process model suitable for construction, activation and governance of IT environments. The requirements, architecture and implementation of the platform are all discussed. Practical usage of the platform is presented on the basis of a complex case study for provisioning JEE middleware on top of the Solaris 10 lightweight virtualization platform

Dynamic Model-based Management of Service-Oriented Infrastructure.

Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation

An Autonomous Engine for Services Configuration and Deployment.

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this propos

Roboconf: a Hybrid Cloud Orchestrator to Deploy Complex Applications

International audienceThis paper presents Roboconf, an open-source distributed application orchestration framework for multi-cloud platforms, designed to solve challenges of current Autonomic Computing Systems in the era of Cloud computing. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. Roboconf implements an asynchronous and parallel deployment protocol which accelerates and makes resilient the deployment process. Intensive experiments with different type of applications over different cloud models (e.g. private, hybrid, and multi-cloud) validate the genericity of Roboconf. These experiments also demonstrate its efficiency comparing to existing frameworks such as RightScale, Scalr, and Cloudify

An Adaptable Framework to Deploy Complex Applications onto Multi-cloud Platforms

International audienceCloud computing is nowadays a popular technology for hosting IT services. However, deploying and reconfiguring complex applications involving multiple software components, which are distributed on many virtual machines running on single or multi-cloud platforms, is error-prone and time-consuming for human administrators. Existing deployment frameworks are most of the time either dedicated to a unique type of application (e.g. JEE applications) or address a single cloud platform (e.g. Amazon EC2). This paper presents a novel distributed application management framework for multi-cloud platforms. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. This framework implements an asynchronous and parallel deployment protocol which accelerates and make resilient the deployment process. A prototype has been developed to serve conducting intensive experiments with different type of applications (e.g. OSGi application and ubiquitous big data analytics for IoT) over disparate cloud models (e.g. private, hybrid, and multi-cloud), which validate the genericity of the framework. These experiments also demonstrate its efficiency comparing to existing frameworks such as Cloudify

SCA Platform Specifications - Version 2.0

The SCOrWare project aims at building an open source implementation of the Service Component Architecture (SCA) specifications. This implementation is composed of 1) a runtime platform for deploying, executing, and managing SCAbased applications, 2) a set of development tools for modeling, designing, and implementing SCA-based applications, and 3) a set of demonstrators. This document contains the specifications of the SCOrWare runtime platform. Section 1.1 lists the parts of SCA specifications supported by the SCOrWare platform. Section 1.2 gives an overview of the SCOrWare platform, and a summary of next chapters of this document. Section 1.3 lists the main update from the version 1 to the version 2 of this documen

Evaluating Mission-Critical Self-Adaptive Software Systems: A Testing-Based Approach

Self-adaptive software is a closed-loop system that tries to manage, direct, or regulate its own behavior dynamically. Such a system aims at providing an automated and systematic approach to handling the increasing complexity of operation management. Mission-critical systems (e.g., e-business and telecommunication systems) are usually large, complex, and distributed. These systems must preserve their Quality of Service (QoS) at runtime under highly dynamic and non-deterministic conditions; therefore, they are suitable candidates for being equipped with self-adaptive capabilities. Although significant efforts have been devoted to modeling, designing, developing and deploying self-adaptive software since a decade ago, there is still a lack of well-established concrete processes for evaluating such systems. This dissertation proposes a systematic evaluation process for mission-critical self-adaptive software systems. The process is a well-defined testing approach that needs a post-mortem analysis, takes the quantified QoS requirements as inputs, and comprises two main phases: i) conducting system-level testing, and ii) evaluating QoS requirements satisfaction. The process uses Service Level Agreements (SLAs) as quantified QoS requirements, and consequently as the adaptation requirements of mission-critical systems. Adaptation requirements are specific types of requirements used to engineer self-adaptive software. Moreover, for the first phase, the dissertation discusses the uniqueness and necessity of conducting system-level load and stress testing on a self-adaptive software system, for collecting runtime QoS data. In the second phase, the process makes use of utility functions to generate a single value indicating the QoS satisfaction of the evaluated system. The dissertation mainly focuses on evaluating the performance, availability and reliability characteristics of QoS. An open source service-oriented Voice over IP (VoIP) application was selected as a case study. The VoIP application was transformed into a self-adaptive software system with various types of adaptation mechanisms. A set of empirical experiments was performed on the developed self-adaptive VoIP application, and the proposed process was adopted for evaluating the effectiveness of different adaptation mechanisms. To this end, the dissertation defines a sample SLA for the VoIP application, presents a report on the load and stress testing performed on the self-adaptive VoIP application, and presents a set of utility functions for evaluation. The experiments illustrate the validity, reliability, flexibility, and cost of the proposed evaluation process. In sum, this dissertation introduces a novel evaluation process for mission-critical self-adaptive software systems, and shows that the proposed process can help researchers to systematically evaluate their self-adaptive systems

Model of Adaptive System for Continuous Monitoring and Performance Prediction of Distributed Applications

Stalno praćenje rada softvera je neophodno da bi se utvrdilo da li softver poštuje zadate nivoe kvaliteta. Na osnovu sakupljenih podataka, moguće je da se predvidi i dalje ponašanje aplikacije i da se izvrši izbor daljih akcija da bi se održao zahtevani nivo. Tema ove disertacije je razvoj sistema za kontinualno praćenje performansi softvera, kao i razvoj modela za predviđanje performansi softvera. Za implementaciju sistema potrebljena je JEE tehnologija, ali je sistem razvijen tako da može da se primeni i za praćenje softvera razvijenog za druge platforme. Sistem je modelovan tako minimalno utiče na performanse sistema softvera koji se prati. Linearna regresija je upotrebljena za modelovanje zavisnosti performansi od okruženja u kom se softver izvršava. Sistem je upotrebljen za praćenje izabrane JEE aplikacije.Continuous monitoring of software is necessary to determine whether the software performs within required service perfomance levels. Based on collected data, it is possible to predict the future performance of applications and to plan further actions in order to maintain the required service levels. The theme of this dissertation is the development of systems for continuous performance monitoring software, as well as the development of models for predicting the performance of software. To implement the system was used JEE technologies, but the system was developed so that it can be used for tracking software developed for other platforms. The system is modeled as a minimum impact on system performance software that is monitored. Linear regression was used for modeling the dependence of the performance environment in which the software is running. The system was used to monitor selected JEE applications