X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Agile dynamic provisioning of multi-tier Internet applications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Automatic exploration of datacenter performance regimes
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
An Evaluation of Distributed Datastores Using the AppScale Cloud Platform
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Database-Agnostic Transaction Support for Cloud Infrastructures
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
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In this paper we present the design, implementation, and evaluation of a plug gable autoscaler within an open cloud platform-as-a-service (PaaS). We redefine high availability (HA) as the dynamic use of virtual machines to keep services available to users, making it a subset of elasticity (the dynamic use of virtual machines). This makes it possible to investigate autoscalers that simultaneously address HA and elasticity. We present and evaluate autoscalers within this plug gable system that are HA-aware and Quality-of-Service (QoS)-aware for web applications written in different programming languages. Hot spares can also be utilized to provide both HA and improve QoS to web users. Within the open source AppScale PaaS, hot spares can increase the amount of web traffic that the QoS-aware autoscaler serves to users by up to 32%. As this auto scaling system operates at the PaaS layer, it is able to control virtual machines and be cost-aware when addressing HA and QoS. This cost awareness uses Spot Instances within Amazon EC2 to reduce the cost of machines acquired by 91%, in exchange for increased startup time. This plug gable auto scaling system facilitates the investigation of new auto scaling algorithms by others that can take advantage of metrics provided by different levels of the cloud stack.