With the widespread popularity of Amazon AWS Lambda, Google Cloud Function, Microsoft Azure Functions, etc., Serverless Computing has gained significant impetus in recent times because of its simplicity. It is the next generation cloud service delivery paradigm and is also known as Function as a Service (FaaS). Almost all big players in the cloud have successfully launched commercially usable serverless computing platforms, although there are many open challenges in terms of their scalability and applicability for widespread deployments. These challenges are many-fold, starting from developing light-weight sandboxing platforms for FaaS supports, deciding optimal deployment strategies for function deployments, increasing the consolidation ratio of the functions, development of economic models for end-users as well as cloud service providers for their individual profit maximization, and so on. Given that majority of the cloud service providers now support serverless computing and direct function execution over the cloud platforms, a thorough investigation of the support systems is necessary through cutting-edge researches in this field.
This workshop aims to provide a forum for researchers and practitioners to exchange innovative ideas, latest research findings, practical experiences, lessons learned, and future directions to propel the research on serverless computing. The topics include, but are not limited to:
We invite original research papers that have not been previously published and are not currently under review for publication elsewhere. Submitted papers should be no longer than 8 pages in two-column IEEE template format. Submitted manuscripts should be structured as technical papers and may not exceed 10 letter size (8.5 x 11) pages including figures, tables and references using the IEEE format for conference proceedings. All accepted papers will be published as part of the CCGRID proceedings. All previous CCGRID proceedings have been published by the IEEE and available online through IEEE Digital Library (EI indexing).