Cloud Migration Methodologies Preliminary Findings
April 17, 2020 Β· Declared Dead Β· π ESOCC Workshops
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mahdi Fahmideh, Farhad Daneshgar, Fethi Rabhi
arXiv ID
2004.10137
Category
cs.SE: Software Engineering
Citations
9
Venue
ESOCC Workshops
Last Checked
4 months ago
Abstract
Research around cloud computing has largely been dedicated to ad-dressing technical aspects associated with utilizing cloud services, surveying critical success factors for the cloud adoption, and opinions about its impact on IT functions. Nevertheless, the aspect of process models for the cloud migration has been slow in pace. Several methodologies have been proposed by both aca-demia and industry for moving legacy applications to the cloud. This paper pre-sents a criteria-based appraisal of such existing methodologies. The results of the analysis highlight the strengths and weaknesses of these methodologies and can be used by cloud service consumers for comparing and selecting the most appropriate ones that fit specific migration scenarios. The paper also suggests research opportunities to improve the status quo. Keywords Cloud Migration; Legacy Applications; Cloud Migration Method-ology, Evaluation Framework
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted