ICWA 2015 - IEEE International Workshop on Cloud Analytics

Cloud computing promises unlimited, cost-effective and agile computing resources for users. However, this new computing paradigm also poses a unique set of challenges to both cloud providers and users. On the one hand, cloud providers need to ensure that resources being provided are highly available and deliver high performance, while optimizing cloud infrastructure to reduce their operational costs. On the other hand, cloud users need to ensure that their applications receive the best performance from the cloud, while maintaining their budgetary constraints and the terms of any Service Level Agreements (SLAs) they have with their cloud providers.
Given the scale of cloud deployment, systematic analytical approaches are critically needed to provide insights to both providers and users to achieve their respective goals. For instance, cloud providers need to constantly be aware of the running status and/or anomalies in functionality from their cloud, to be able to quickly fix any issues that may arise, to adjust physical resource allocations to ensure that their customers get best performance, or plan which services to offer to get the best return on investment. Similarly, cloud users need to understand the workload to be deployed into the cloud, plan the deployment in a cost-effective way, or ascertain the flexibility and service quality provided by different cloud environments and use this to decide their deployment strategy. Analytics can play a pivotal role in all these scenarios. By gathering insights from the large amount of data from the cloud, both cloud providers and consumers can develop analytical approaches to achieving their respective objectives in spite of the scale that clouds provide.
The purpose of this workshop is to provide a forum for researchers in the related fields to exchange ideas, and share their experiences in developing analytics to better deploy, operate and use the cloud. Specifically, we seek and wish to foster research contributions that draw on statistical analysis, analytical modeling, and machine learning to develop novel solutions in this problem area.