Maria Salama

Cloud Computing: Paradigms and Technologies

Cloud Computing has recently emerged as a compelling paradigm for managing and delivering services over the internet. It is rapidly changing the landscape of information technology, and ultimately turning the long-held promise of utility computing into a reality. With such speedy progressing and emerging, it becomes crucial to understand all aspects about this technology. This chapter provides a comprehensive overview on the Cloud’s anatomy, definition, characteristic, affects, architecture, and core technology. It clearly classifies the Cloud’s deployment and service models, providing a full description of the Cloud services vendors. The chapter also addresses the customer-related aspects such as the Service Level Agreement, service cost, and security issues. Finally, it covers detailed comparisons between the Cloud Computing paradigm and other existing ones in addition to its significant challenges. By that, the chapter provides a complete overview on the Cloud Computing and paves the way for further research in this area.

Integrated QoS Utility-Based Model for Cloud Computing Service Provider Selection

Cloud Computing is gaining a considerable attention in the past few years. It changes the way people acquire software and hardware as it provides them as services through internet on-demand following a pay-as-you-go financial model. With the exponential increase of such service, selecting the optimal provider based on predefined Quality of Service (QoS) requirements becomes crucial. The current techniques are just designed for performance evaluation and cost-benefit analysis; yet optimal service provider selection based on a group of QoS requirements is still uncovered as it should be. In this paper we propose a mathematical model addressing the Cloud service provider selection optimization problem based on QoS guarantees. The proposed model efficiently matches with the characteristics of market-oriented platforms covering a wide range of service provider selection problems. The efficiency of the proposed model is validated through simulation studies.

A QoS-Oriented Inter-cloud Federation Framework

Cloud federation allows individual cloud providers dynamically collaborate to offer services to their end-users with the Quality of Service (QoS) targets agreed in the Service Level Agreements (SLA). However, the current federated cloud models are not QoS-oriented or SLA-aware. This paper proposes a QoS-oriented federated cloud computing framework where multiple independent cloud providers can cooperate seamlessly to provide scalable QoS-assured services and discusses a high level architecture of the federation components. The distinct features of the proposed federation framework is its QoS-orientation that can trigger the on-demand resource provisioning across multiple providers, hence helping to maximize QoS targets and resources usage, eliminate SLA violations and enhance SLA formalization.

Quality-Driven Architectural Patterns for Self-Aware Cloud-Based Software

Architecture-based self-adaptation has been recognised as one of the prominent ways to design autonomic systems, where self-manageable architectures tend to achieve the required level of dynamicity and compliance with the continual changing in QoS requirements during run-time. Self-awareness and self-expression have recently emerged as promising architectural concepts in the field of self-adaptive software. Self-aware architecture patterns are envisioned as enabler for self-adaptation, but they tend to provide limited support for the QoS run-time requirements. While the research community has developed in architecture quality management, patterns and tactics, addressing quality attributes in self-aware architectures has not been tackled yet. In this paper, we aim to provide quality-driven architectural patterns for emerging class of architecture enabled by the principles of self-awareness. We report on the feasibility of correlating QoS tactics with self-aware capabilities to better respond to QoS run-time requirements and trade-offs. We describe novel extensions which make the correlation between QoS tactics and self-awareness explicit. We quantitatively evaluate the feasibility, generality and fitness of the proposed approach, as well as its potential applicability to self-aware architectures. Though the proposed extensions can potentially benefit architectures which leverage on self-awareness, we use the case of cloud auto-scaling architecture.

Managing Trade-offs in Self-Adaptive Software Architectures: A Systematic Mapping Study

Abstract Self-adaptation has been driven by the need to achieve and maintain quality attributes in the face of the continuously changing requirements, as well as the uncertain demand during run-time. Designing architectures that exhibit a good trade-off between multiple quality attributes is challenging, especially in the case of self-adaptive software systems, due to the complexity, heterogeneity, and ultra-large scale of modern software systems. This challenge increases with the dynamic, open, and uncertain operating environment, as well as the need for complying to environmental, regulatory, and sustainability requirements; such as energy consumption regulations. This study aims at analyzing the research landscape that have explicitly addressed trade-offs management for self-adaptive software architectures, to obtain a comprehensive overview on the current state of research on this specialized area. A systematic mapping study was conducted to identify and analyze research works related to analyzing and managing trade-offs to support decision-making for self-adaptive software architectures. Twenty primary studies were evidently selected and analyzed to classify software paradigms, quality attributes considered, and the self-* properties that drive trade-offs management. The results show constant interest in finding solutions for trade-offs management at design-time and run-time, as well as the success of research initiatives even when new research challenges are found. The findings call for foundational framework to analyze and manage trade-offs for self-adaptive software architectures that can explicitly consider specific multiple quality attributes, the run-time dynamics, the uncertainty of the environment and the complex challenges of modern, ultra-large scale systems in particular given software paradigms.Self-adaptation has been driven by the need to achieve and maintain quality attributes in the face of the continuously changing requirements, as well as the uncertain demand during run-time. Designing architectures that exhibit a good trade-off between multiple quality attributes is challenging, especially in the case of self-adaptive software systems, due to the complexity, heterogeneity, and ultra-large scale of modern software systems. This challenge increases with the dynamic, open, and uncertain operating environment, as well as the need for complying to environmental, regulatory, and sustainability requirements; such as energy consumption regulations. This study aims at analyzing the research landscape that have explicitly addressed trade-offs management for self-adaptive software architectures, to obtain a comprehensive overview on the current state of research on this specialized area. A systematic mapping study was conducted to identify and analyze research works related to analyzing and managing trade-offs to support decision-making for self-adaptive software architectures. Twenty primary studies were evidently selected and analyzed to classify software paradigms, quality attributes considered, and the self-* properties that drive trade-offs management. The results show constant interest in finding solutions for trade-offs management at design-time and run-time, as well as the success of research initiatives even when new research challenges are found. The findings call for foundational framework to analyze and manage trade-offs for self-adaptive software architectures that can explicitly consider specific multiple quality attributes, the run-time dynamics, the uncertainty of the environment and the complex challenges of modern, ultra-large scale systems in particular given software paradigms.

Stability of Self-Adaptive Software Architectures

Stakeholders and organisations are increasingly looking for long-lived software. As architectures have a profound effect on the operational life-time of the software and the quality of the service provision, architectural stability could be considered a primary criterion towards achieving the long-livety of the software. Architectural stability is envisioned as the next step in quality attributes, combining many inter-related qualities. This research suggests the notion of behavioural stability as a primary criterion for evaluating whether the architecture maintains achieving the expected quality attributes, maintaining architecture robustness, and evaluating how well the architecture accommodates run-time evolutionary changes. The research investigates the notion of architecture stability at run-time in the context of self-adaptive software architectures. We expect to define, characterise and analyse this intuitive concept, as well as identify the consequent trade-offs to be dynamically managed and enhance the self-adaptation process for a long-lived software.

A Generic Framework for Modeling and Simulation of Cloud Computing Services

Cloud Computing is a paradigm in which tasks are assigned to a combination of computing resources, software and services accessed over a network following the pay-as–you-go financial model. It has been also described as on-demand computing. With the continuous increases of cloud service providers, it becomes crucial to develop a simulation tool to reflect the properties of such complex environment to help clients selecting the appropriate providers. Available Cloudbased tools are designed for cloud architectures and resources scheduling, not the problem of provider selection. Even the Grid-based tools who share many features with the Cloud cannot cope with such problem due to the novel characteristics and services of the Cloud. This paper provides a new simulation tool that reflects the nature of clouds embedding all its aspects, as well as its QoS parameters. Such tool is designed to simulate any framework or solution for service provider selection problem. The proposed simulation tool is validated by running a framework developed for service provider selection problem based on QoS and utility functions. The paper also reviews various mathematical approaches that have been used to model cloud services, where most of them are formulations of cloud services that aim to optimize its quality of service, performance or energy efficiency under given constraints.

Analysing and modelling runtime architectural stability for self-adaptive software

Abstract With the increased dependence on software, there is a pressing need for engineering long-lived software. As architectures have a profound effect on the life-span of the software and the provisioned quality of service, stable architectures are significant assets. Architectural stability tends to reflect the success of the system in supporting continuous changes without phasing-out. For self-adaptive architectures, the behavioural aspect of stability is essential for seamless operation, to continuously keep the provision of quality requirements stable and prevent unnecessary adaptations that will risk degrading the system. In this paper, we introduce a systematic approach for analysing and modelling architectural stability. Specifically, we leverage architectural concerns and viewpoints to explicitly analyse stability attributes of the intended behaviour. Due to the probabilistic nature of systems’ behaviour, stability modelling is based on a probabilistic relational model for knowledge representation of stability multiple viewpoints. The model, empowered by the quantitative analysis of Bayesian networks, is capable to conduct runtime inference for reasoning about stability under runtime uncertainty. To illustrate the applicability and evaluate the proposed approach, we consider the case of cloud architectures. The results show that the approach increases the efficiency of the architecture in keeping the expected behaviour stable during runtime operation.

A taxonomy for architectural stability

With the increase dependence on software, there is a pressing need for engineering long-lived stable software. As architectures have a profound effect on the life-span of the software and the provisioned quality of the service, a stable architecture is a significant asset of the software. Yet, there is lack of consensus on the concept of stability as an architectural quality attribute. This paper proposes a taxonomy for defining, characterising and analysing architectural stability. The aim is to provide a better understanding and characterisation of this strategic quality attribute within the domain of software architecture, and to explicate a set of general concepts across a wide range of architectures. Such framework would significantly ease understanding the concept in the shed of modern and complex software architectures, and, therefore, allow more systematic guidance in designing and operating architectures. We apply the taxonomy in analysing the behavioural stability of self-aware cloud architectures.

Libraries: From the Classical to Cloud-Based Era

A Library is an organized collection of resources made accessible to a defined community for reference or borrowing. It provides physical or digital access to material, and may be a physical building, or a virtual space, or both. During the last decays, the Libraries had witnessed a continuous revolution and still do. This paper reviews the main milestone of such revolution starting from the classical up to the current Cloud-based era passing by the intermediate digital transformation period. It reviews the library types, services, problems and drive of changes from the classical form. The paper then tackles the transformation of the library to the digital form. It discusses the characteristics of the digital library, the web-based library, and the library 2.0 through their advantages and limitations. The paper finally focuses on the current Cloud-based ear, where most of the library cloud platforms, services management, innovative products and opened environments are addressed through their features, add values, pros and cons. The paper also provides a comparative study on such solutions coming up with opened research issues. Hereby, the paper provides a comprehensive overview on the development of the library till now.