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A hybrid scheduler for many task computing in big data systems

Vasiliu L., Pop F., Negru C., Mocanu M., Cristea V., Kolodziej J.

International Journal of Applied Mathematics and Computer Science

2017

Vol. 27, no. 2

385--399

With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

FIM-SIM: Fault Injection Module for CloudSim Based on Statistical Distributions

Nita M.-C., Pop F., Mocanu M., Cristea V.

Journal of Telecommunications and Information Technology

2014

nr 4

14--23

The evolution of ICT systems in the way data is accessed and used is very fast nowadays. Cloud computing is an innovative way of using and providing computing resources to businesses and individuals and it has gained a faster popularity in the last years. In this context, the user’s expectations are increasing and cloud providers are facing huge challenges. One of these challenges is fault tolerance and both researchers and companies have focused on finding and developing strong fault tolerance models. To validate these models, cloud simulation tools are used as an easy, flexible and fast solution. This paper proposes a Fault Injector Module for CloudSim tool (FIM-SIM) for helping the cloud developers to test and validate their infrastructure. FIM-SIM follows the event- driven model and inserts faults in CloudSim based on statistical distributions. The authors have tested and validated it by conducting several experiments designed to highlight the statistical distribution influence on the failures generated and to observe the CloudSim behavior in its current state and implementation.