Designing Highly Recoverable Cloud Based Software Applications

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Cloud computing is an increasingly popular paradigm that allows individuals and enterprises to provision and deploy software applications over the Internet. Customers can lease services provided by these ‘cloud’ applications (a.k.a cloud apps), ramping up or down the capacity as they need and paying only for what they use. Cloud apps typically run on cloud platforms such as Google App Engine, Windows Azure, or OpenStack. Cloud apps are used in about every industry today; from financial, retail, education, and communications, to manufacturing, utilities and transportation. Forrester Research predicts that cloud apps sales will more than quadruple by 2016 (from $21.2 billion this year to $92.8 billion) to account for around 16% of the total software market. However, cloud apps dependability is still a major issue for both providers and users. Failures of cloud apps generally result in big economic losses as core business activities now rely on them. This was the case in December 24, 2012 when a failure of Amazon web services caused an outage of Netflix cloud services for 19 hours. The demand for highly dependable cloud apps has reached unprecedentedly high levels today. Yet, there is still no clear methodology in the industry for developing highly dependable cloud apps. Developers usually delegate dependability issues to the cloud platforms running the apps. A rule of thumb is to replicate services across multiple availability zones (AZ) as summarized by Netflix’s strategy: "Deploy in multiple AZ with no extra instances – target autoscale 30-60% until you have 50% headroom for load spikes. Lose an AZ leads to 90% utilization". Yet, stress tests conducted by Sydney-based researchers have revealed that infrastructure and platform services offered by big players like Amazon, Google, and Microsoft suffer from regular performance and availability issues due to service overload, hardware failures, software errors, and operator errors. The response times of these services was found to vary by a factor of twenty depending on the time of day. Therefore, cloud apps should be robust to failures if they are to be highly dependable. The long-term goal of this research program is to develop techniques and tools to improve the recoverability of cloud apps. By reducing the recovery time of cloud apps, we will be able to improve their dependability and reduce the amount of money lost during service-downtime. I will achieve this goal by developing and applying a novel and innovative methodology supported by a framework to incorporate fault recovery mechanisms in the architecture of cloud apps. One big asset of cloud computing is the constantly increasing number of Application Programming Interfaces (API) that allow developers to integrate multiple third party services into cloud apps. Examples of cloud API platforms include Apache CloudStack, Amazon Web Services, Eucalyptus, Simple Cloud, and OpenStack. These service-level APIs provide a lot of redundant services that can be incorporate in cloud apps to implement fault-tolerance. Using patterns like Heartbeat or Watchdog, a cloud app can monitor a specific service on which it depends and, in case of failure of this service, redirect requests to a backup service and trigger a recovery mechanism to maintain high availability. I will propose architectural patterns to integrate and monitor services, as well as a framework to integrate fault recovery mechanisms in the architecture of cloud apps.