Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

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Software Architecture Optimization Methods: A Systematic Literature Review

Software architecture optimization methods: A systematic literature review

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

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Quality-of-service in cloud computing: modeling techniques and their applications

Service-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last 10 years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today’s service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore, investigating service description, discovery, access, and composition in the Future Internet of services.

https://link.springer.com/content/pdf/10.1007%2Fs13174-011-0021-3.pdf

Service-oriented middleware for the Future Internet: state of the art and research directions

The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAP Cloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard, we propose an efficient simulated annealing based heuristic, called CRAM that is able to achieve about84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation(on Android G2 devices and Amazon EC2, S3 and Cloud Front)and extensive simulation using two rich mobile applications(Video-Content Augmented Reality and Image processing). Our results indicate that MAP Cloud provides improved scalability as compared to local clouds, improved efficiency (power/delay)(about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.

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MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture

QoS-aware service composition is a key requirement in Service Oriented Computing (SOC) since it enables fulfilling complex user tasks while meeting Quality of Service (QoS) constraints. A challenging issue towards this purpose is the selection of the best set of services to compose, meeting global QoS constraints imposed by the user, which is known to be a NP-hard problem. This challenge becomes even more relevant when it is considered in the context of dynamic service environments. Indeed, two specific issues arise. First, required tasks are fulfilled on the fly, thus the time available for services' selection and composition is limited. Second, service compositions have to be adaptive so that they can cope with changing conditions of the environment. In this paper, we present an efficient service selection algorithm that provides the appropriate ground for QoS-aware composition in dynamic service environments. Our algorithm is formed as a guided heuristic. The paper also presents a set of experiments conducted to evaluate the efficiency of our algorithm, which shows its timeliness and optimality.

/pdf/qos-aware-service-composition-in-dynamic-service-oriented-42a1mclgb5.pdf

QoS-aware service composition in dynamic service oriented environments

Service-Oriented Computing is a cornerstone for the realization of user needs through the automatic composition of services from service descriptions and user tasks, i.e., high-level descriptions of the user needs. Yet, automatic service composition processes commonly assume that service descriptions and user tasks share the same abstraction level, and that services have been pre-designed to integrate. To release these strong assumptions and to augment the possibilities of composition, we add adaptation features into the service composition process using semantic descriptions and adaptive extensions to graph planning.

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Automated Service Composition with Adaptive Planning

The essential issue of interoperability in distributed systems is becoming even more pressing in the Future Internet, where complex applications will be composed from extremely heterogeneous systems. Open system integration paradigms, such as service oriented architecture (SOA) and enterprise service bus (ESB), have provided answers to the interoperability requirement. However, when it comes to integrating systems featuring heterogeneous interaction paradigms, such as client-service, publish-subscribe and tuple space, existing solutions are typically ad hoc and partial, applying to specific interaction protocol technologies. In this paper, we introduce an interoperability solution based on abstraction and merging of the common high-level semantics of interaction paradigms, which is sufficiently general and extensible to accommodate many different protocol technologies. We apply this solution to revisit the SOA- and ESB-based integration of heterogeneous distributed systems.

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Sandrine Beauche

Papers

QoS-aware service composition in dynamic service oriented environments

Automated Service Composition with Adaptive Planning

Service-Oriented Distributed Applications in the Future Internet: The Case for Interaction Paradigm Interoperability