Listado de publicacións

TítuloEME: An automated, elastic and efficient prototype for provisioning Hadoop clusters on-demand
AutoresFeras M. Awaysheh, Tomás F. Pena, and José C. Cabaleiro
TipoComunicación para congreso
Fonte The 7th International Conference on Cloud Computing and Services Science, Porto, Portugal, 2017.
AbstractAiming at enhancing the MapReduce-based applications Quality of Service (QoS), many frameworks suggest a scale-out approach, statically adding new nodes to the cluster. Such frameworks are still expensive to acquire and does not consider the optimal usage of available resources in a dynamic manner. This paper introduces a prototype to address with this issue, by extending MapReduce resource manager with dynamic provisioning and low-cost resources capacity uplift on-demand. We propose an Enhanced Mapreduce Environment (EME), to support heterogeneous environments by extending Apache Hadoop to an opportunistically containerized environment, which enhances system throughput by adding underused resources to a local or cloud-based cluster. The main architectural elements of this framework are presented, as well as the requirements, challenges, and opportunities of a first prototype.
Palabras chaveMapReduce, Hadoop, Big Data, Cloud computing, Prototyping, Elastic Computing, Quality of Service.