A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

A Survey on Service Composition Middleware in Pervasive Environments

The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments

Context Oriented Software Middleware

Our middleware approach, Context-Oriented Software Middleware (COSM), supports context-dependent software with self-adaptability and dependability in a mobile computing environment. The COSM-middleware is a generic and platform-independent adaptation engine, which performs a runtime composition of the software's context-dependent behaviours based on the execution contexts. Our middleware distinguishes between the context-dependent and context-independent functionality of software systems. This enables the COSM-middleware to adapt the application behaviour by composing a set of context-oriented components, that implement the context-dependent functionality of the software. Accordingly, the software dependability is achieved by considering the functionality of the COSM-middleware and the adaptation impact/costs. The COSM-middleware uses a dynamic policy-based engine to evaluate the adaptation outputs and verify the fitness of the adaptation output with the application's objectives, goals and the architecture quality attributes. These capabilities are demonstrated through an empirical evaluation of a case study implementation

Testing self-adaptive applications with simulation of context events

Modern trends in mobile computing have raised the expectations of users in terms of such features such as context-awareness and self-adaptiveness. With such capabilities, applications can autonomously sense their context and automate a number of tasks, effectively reducing the attention required by the end users. This paper presents a custom simulation engine, designed to support the testing of applications developed using the MUSIC platform. The simulation tool consists of a platform-independent server module, deployed along with the application, and a client module which is responsible for interpreting and executing the simulation script. The use of the tool is demonstrated in the scope of the SatMotion application, which is designed to assist satellite antenna installers with specialized functionality

08031 Abstracts Collection -- Software Engineering for Self-Adaptive Systems

From 13.01. to 18.01.2008, the Dagstuhl Seminar 08031 ``Software Engineering for Self-Adaptive Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Self Healing Microservices Architecture: A Case Study in Docker Swarm Cluster

One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behaviour as well as implement a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. Often the behaviour of a microservices architecture continuously changes over time and the identification of both normal and abnormal behaviours of running services becomes a challenging task. This paper proposes a self-healing Microservice architecture that continuously monitors the operational environment, detects and observes anomalous behaviours, and provides a reasonable adaptation policy using a multi-dimensional utility-based model. This model preserves the cluster state and prevents multiple actions to taking place at the same time. It also guarantees that the executed adaptation action fits the current execution context and achieves the adaptation goals. The results show the ability of this model to dynamically scale the architecture horizontally or vertically in response to the context changes

Feature-based generation of pervasive systems architectures utilizing software product line concepts

As the need for pervasive systems tends to increase and to dominate the computing discipline, software engineering approaches must evolve at a similar pace to facilitate the construction of such systems in an efficient manner. In this thesis, we provide a vision of a framework that will help in the construction of software product lines for pervasive systems by devising an approach to automatically generate architectures for this domain. Using this framework, designers of pervasive systems will be able to select a set of desired system features, and the framework will automatically generate architectures that support the presence of these features. Our approach will not compromise the quality of the architecture especially as we have verified that by comparing the generated architectures to those manually designed by human architects. As an initial step, and in order to determine the most commonly required features that comprise the widely most known pervasive systems, we surveyed more than fifty existing architectures for pervasive systems in various domains. We captured the most essential features along with the commonalities and variabilities between them. The features were categorized according to the domain and the environment that they target. Those categories are: General pervasive systems, domain-specific, privacy, bridging, fault-tolerance and context-awareness. We coupled the identified features with well-designed components, and connected the components based on the initial features selected by a system designer to generate an architecture. We evaluated our generated architectures against architectures designed by human architects. When metrics such as coupling, cohesion, complexity, reusability, adaptability, modularity, modifiability, packing density, and average interaction density were used to test our framework, our generated architectures were found comparable, if not better than the human generated architectures

Building self-adaptive systems by adaptation patterns integrated into agent methodologies

Adopting patterns, i.e. reusable solutions to generic problems, turns out to be useful to rely on tested solutions and to avoid reinventing the wheel. To this aim, we proposed to use adaptation patterns to build systems that exhibit self-adaptive features. However, these patterns would be more usable if integrated in a methodology exploited to develop a system. In this paper we show how our Catalogue of adaptation patterns can be integrated into methodologies for adaptive systems; more in detail, we consider methodologies which support the development of multi-agent systems that can be considered good examples of adaptive systems. The paper, in particular, shows the integration of our Catalogue of adaptive patterns into the PASSI methodology, together with the graphical tool that we developed to support it

ADJUSTMENT STRATEGIES FOR MANAGING UNANTICIPATED CHANGES IN SOFTWARE DEVELOPMENT PROCESSES

Software engineers face multiple challenges of managing unanticipated changes, dependencies, uncertainty, emerging demand patterns. In this contribution we focus on the process of software development and its design to especially cover unforeseen changes. The article presents a structural view on the (distributed) software engineering process introducing three domains that trigger adjustment opportunities of the engineering process. Hereafter the solution approach imposing the process model PEPMAD is outlined

A Deep Recurrent Q Network Towards Self-adapting Distributed Microservices Architecture (in press)

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resources over-provisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms including: i) deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The DRQN implementation in this paper manages to outperform the above mentioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training times. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms