PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Quantitative verification: models techniques and tools
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
A break in the clouds: towards a cloud definition
ACM SIGCOMM Computer Communication Review
On Technical Security Issues in Cloud Computing
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
The Ins and Outs of the Probabilistic Model Checker MRMC
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Communications of the ACM
Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Using observation ageing to improve markovian model learning in QoS engineering
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Dynamic QoS Management and Optimization in Service-Based Systems
IEEE Transactions on Software Engineering
FOCS'10 Proceedings of the 16th Monterey conference on Foundations of computer software: modeling, development, and verification of adaptive systems
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
The Cloud Adoption Toolkit: supporting cloud adoption decisions in the enterprise
Software—Practice & Experience
Algebraic Specifications of Computing as a Service with Applications to Cost Analysis
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
log2cloud: log-based prediction of cost-performance trade-offs for cloud deployments
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments.