Refinement and extension of the cloud decision support framework for application migration to the cloud

The maturity and dissemination of Cloud Computing across diverse business domains is leading to an increasing amount of migration projects with the goal to leverage the associated benefits for important legacy applications. However, the migration of applications to the cloud is a complex problem that entails various technical and organizational aspects. The existing Cloud Decision Support Framework has been a first step to provide decision makers with the means to find a suitable migration strategy. This master's thesis has refined the framework's underlying knowledge base by reviewing its decision points, decisions and their relations as well as outcomes. Based on this refinement, the framework has been extended by elaborating the relations between outcomes resulting in greater potential for decision support. In order to allow decision makers to derive migration strategies based on the framework in an interactive manner, a web application has been implemented. In a final step, an evaluation has been carried out comprising a validation of the knowledge base and, by means of a use case, a demonstration of the efficacy of the extended decision support framework

Pattern-based multi-cloud architecture migration

Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model

STRATEGI KOMPUTASI AWAN: ROADMAP FOR CLOUD COMPUTING ADAPTION (ROCCA) - IDENTIFIKASI RESIKO PADA INSTANSI X

Cloud computing (komputasi awan) merupakan sebuah pengembangan infrastruktur Teknologi Informasi yang dapat memberikan solusi atas keterbatasan kemampuan dari beberapa lembaga pemerintah. Tujuan dilakukan penelitan yaitu untuk mengetahui kemudahan, dan memetakan dalam implementasi peralihan data komputasi awan. Penggabunagn ROCCA – identifikasi resiko pada komputasi jaringan di instansi X dapat membuat tahapan migrasi menjadi terstruktur. Selain metoda ROCCA yang generik, sehingga dapat “dilakukan penyesuaian” modelnya dengan menyisipkan identifikasi resiko. Sebagai jaminan agar data sivitas dapat menggunakan data sebagai bagian dari pekerjaan korporat.Metoda yang digunakan dengan menggabungkan ROCCA Adoption Framework dan identifikasi resiko. Masing-masing tahapan pada ROCCA telah disisipkan identifikasi resiko yang dapat memantau dan melakukan evaluasi pada tiap tahapannya. Hasil dari penelitian ini berupa tahapan dari ROCCA dengan indentifikasi resiko agar pelaksanaan migrasi data ke komputasi awan terencana dengan baik dan berjalan dengan baik. Diperlukan evaluasi pada tiap tahapan terutama pada tahapan migrasi, selain itu singkronisasi dan layanan data untuk sivitas, perlu dievaluasi sesuai dengan identifikasi yang telah dilakukan. Komputasi awan pada intansi X lebih mendekati model implementasi Private Cloud as a service (PaaS) dan layanan Infrastructure as a Service (IaaS)

A Knowledge Management Based Cloud Computing Adoption Decision Making Framework

Cloud computing represents a paradigm shift in the way that IT services are delivered within enterprises. There are numerous challenges for enterprises planning to migrate to cloud computing environment as cloud computing impacts multiple different aspects of an organisation and cloud computing adoption issues vary between organisations. A literature review identified that a number of models and frameworks have been developed to support cloud adoption. However, existing models and frameworks have been devised for technologically developed environments and there has been very little examination to determine whether the factors that affect cloud adoption in technologically developing countries are different. The primary research carried out for this thesis included an investigation of the factors that influence cloud adoption in Saudi Arabia, which is regarded as a technologically developing country. This thesis presents an holistic Knowledge Management Based Cloud Adoption Decision Making Framework which has been developed to support decision makers at all stages of the cloud adoption decision making process. The theoretical underpinnings for the research come from Knowledge Management, including the literature on decision making, organisational learning and technology adoption and technology diffusion theories. The framework includes supporting models and tools, combining the Analytical Hierarchical Process and Case Based Reasoning to support decision making at Strategic and Tactical levels and the Pugh Decision Matrix at the Operational level. The Framework was developed based on secondary and primary research and was validated with expert users. The Framework is customisable, allowing decision makers to set their own weightings and add or remove decision making criteria. The results of validation show that the framework enhances Cloud Adoption decision making and provides support for decision makers at all levels of the decision making process