Managing a SaaS application in the cloud using PaaS policy sets and a strategy-tree
Proceedings of the 7th International Conference on Network and Services Management
Optimal autoscaling in a IaaS cloud
Proceedings of the 9th international conference on Autonomic computing
Partitioning applications for hybrid and federated clouds
CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research
A generic framework for service-based business process elasticity in the cloud
BPM'12 Proceedings of the 10th international conference on Business Process Management
Rebalancing in a multi-cloud environment
Proceedings of the 4th ACM workshop on Scientific cloud computing
An architecture for overlaying private clouds on public providers
Proceedings of the 8th International Conference on Network and Service Management
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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An elasticity policy governs how and when resources (e.g., application server instances at the PaaS layer) are added to and/or removed from a cloud environment. The elasticity policy can be implemented as a conventional control loop or as a set of heuristic rules. In the control-theoretic approach, complex constructs such as tracking filters, estimators, regulators, and controllers are utilized. In the heuristic, rule-based approach, various alerts(e.g., events) are defined on instance metrics (e.g., CPU utilization), which are then aggregated at a global scale in order to make provisioning decisions for a given application tier. This work provides an overview of our experiences designing and working with both approaches to construct an auto scaler for simple applications. We enumerate different criteria such as design complexity, ease of comprehension, and maintenance upon which we form an informal comparison between the different methods. We conclude with a brief discussion of how these approaches can be used in the governance of resources to better meet a high-level goal over time.