Abstract. Many organizations migrate their on-premise software systems to the cloud. However, current coarse-grained cloud migration solutions have made a transparent migration of on-premise applications to the cloud a difficult, sometimes trial-and-error based endeavor. This paper suggests a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud settings and are specified with architectural notations. The proposed migration patterns are based on empirical evidence from a number of migration projects, best practices for cloud architectures and a systematic literature review of existing research. The pattern catalogue allows an organization to (1) select appropriate architecture migration patterns based on their objectives, (2) compose them to define a migration plan, and (3) extend them based on the identification of new patterns in new contexts.
With the resource‐constrained nature of mobile devices and the resource‐abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource intensive tasks from mobile devices to the cloud. Although these schemes address similar questions within the same domain of mobile cloud application (MCA) optimisation, evaluation is tailored to the scheme and also solely mobile focused, thus making it difficult to clearly compare with other existing counterparts. In this work, we first analyse the existing/commonly adopted evaluation technique, then with the aim to fill the above gap, we propose the behaviour‐driven full‐tier green evaluation approach, which adopts the behaviour‐driven concept for evaluating MCA performance and energy usage—ie, green metrics. To automate the evaluation process, we also present and evaluate the effectiveness of a resultant application program interface and tool driven by the behaviour‐driven full‐tier green evaluation approach. The application program interface is based on Android and has been validated with Elastic Compute Cloud instance. Experiments show that Beftigre is capable of providing a more distinctive, comparable, and reliable green test results for MCAs.
Abstract-Green software is currently gaining interests with the increasing impact of IT in energy consumption. Green-ness in software however, can be achieved at various stages of the Software Development Life Cycle (SDLC). Consequently, several software engineering concepts can be adopted for achieving greener software. Aspect Oriented Programming (AOP) has been used in solving several crosscutting concerns of software, such as security and performance, but has not been well explored within the context of Energy Efficiency (EE). In this paper we propose and implement an Aspect-oriented Model for EE (AMEE) which adopts AOP for software EE as a crosscutting concern and consequently reducing computational energy consumption based on client-server architecture, where the server layer is distributed. By using a selected case study, the paper presents the energy saving outcome of using AMEE model for different simulated workload patterns.
Abstract-Many software projects are built using reusable components (i.e. reusable objects -as per component and connectors in software architectures). During component selection in CBSD, components are evaluated on the criteria of required quality attribute prior to integration into a system. Current green software research exploring software energy efficiency as a quality attribute adopts conventional counter-based white box energy measuring approach. Although the conventional approach provides results at fine granularity, as with its adoption in component selection, the challenge is that to test software energy of each component, the test has to be done prior to integration, which means implementing multiple counters or multiple versions of the system -thus inefficient, especially when involving much components. In this paper, we present an approach and tool for dynamic energy profiling of components for software systems (DEEPC). The proposed approach employs AOP concepts to expedite the energy measurement of components and improve accuracy, by; i) dynamically loading related components for evaluation (load-time weaving) into the base system, as a way to circumvent manual counter implementation, ii) using pointcuts to facilitate power measurement of loaded components. An evaluation of DEEPC approach presents it to be more time and resource efficient with better profiling accuracy compared to its counterpart.
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