Cloud elasticity using probabilistic model checking

• Main Authors: Naskos, Athanasios, Stachtiari, Emmanouela, Gounaris, Anastasios, Katsaros, Panagiotis, Tsoumakos, Dimitrios, Konstantinou, Ioannis, Sioutas, Spyros
• Format: Journal Article
• Language: English
• Published: 19-05-2014
• Subjects: Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Logic in Computer Science
• DOI: 10.48550/arxiv.1405.4699

Cloud computing has become the leading paradigm for deploying large-scale
infrastructures and running big data applications, due to its capacity of
achieving economies of scale. In this work, we focus on one of the most
prominent advantages of cloud computing, namely the on-demand resource
provisioning, which is commonly referred to as elasticity. Although a lot of
effort has been invested in developing systems and mechanisms that enable
elasticity, the elasticity decision policies tend to be designed without
guaranteeing or quantifying the quality of their operation. This work aims to
make the development of elasticity policies more formalized and dependable. We
make two distinct contributions. First, we propose an extensible approach to
enforcing elasticity through the dynamic instantiation and online quantitative
verification of Markov Decision Processes (MDP) using probabilistic model
checking. Second, we propose concrete elasticity models and related elasticity
policies. We evaluate our decision policies using both real and synthetic
datasets in clusters of NoSQL databases. According to the experimental results,
our approach improves upon the state-of-the-art in significantly increasing
user-defined utility values and decreasing user-defined threshold violations.