RM-BDP: Resource management for Big Data platforms

Florin Pop (1) , Radu Prodan (2) , Gabriel Antoniu (3)

1 Department of Computer Science [Bucharest]
2 Leopold Franzens Universität Innsbruck - University of Innsbruck
3 KerData - Scalable Storage for Clouds and Beyond

Nowadays, when we face with numerous data, when data cannot be classified into regular relational databases and new solutions are required, and when data are generated and processed rapidly, we need powerful platforms and infrastructure as support. Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are located in different situations or contexts. Cloud computing, which concerns large-scale interconnected systems with the main purpose of aggregation and efficient exploiting the power of widely distributed resources, represent one viable solution. Resource management and task scheduling play an essential role, in cases where one is concerned with optimized use of resources (Negru et al., 2017) [1].The goal of this special issue is to explore new directions and approaches for reasoning about advanced resource management and task scheduling methods and algorithms for Big Data platforms. The accepted papers present new results in the domain of resource management and task scheduling, Cloud platforms supporting Big Data processing, data handling and Big Data applications.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Florin Pop, Radu Prodan, Gabriel Antoniu. RM-BDP: Resource management for Big Data platforms. Future Generation Computer Systems, 2018, 86, pp.961 - 963. ⟨10.1016/j.future.2018.05.018⟩. ⟨hal-01892942⟩