Big Data Scientific Workflows in the Cloud: Challenges and Future Prospects

Abstract

The concept of workflows was implemented to mitigate the complexities involved in tasks related to scientific computing and business analytics. With time, they have found applications in many diverse fields and domains. Handling big data has given rise to many other issues like growing computing complexity, increasing data size, provisioning of resources and the need for such systems to enable working together of heterogeneous systems. As a result, traditional systems are deemed obsolete for this purpose. To meet the variable resource requirements, cloud has emerged as an ostensible solution. Execution and deployment of big data scientific workflows in the cloud is an area that requires research attention before a synergistic model for the same can be presented. This paper identifies open research problems associated with this domain, giving insights on specific issues like workflow scheduling and execution and deployment of big data scientific workflows in a multi-site cloud environment.