An optimization framework for the capacity allocation and admission control of MapReduce jobs in cloud systems

Abstract

It is safe to claim that we live in a Big Data world. In effect, many sectors of our economy are already guided by data-driven decision processes. Big Data and Business Intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions to provide on demand large clusters. Capacity allocation in such systems, meant as the problem of providing computational power to support concurrent MapReduce applications in a cost effective fashion, represents a challenge of paramount importance. In this paper we lay the foundation for a solution implementing admission control and capacity allocation for MapReduce jobs with a priori deadline guarantees. In particular, shared Hadoop 2.x clusters supporting batch and/or interactive jobs are targeted. We formulate a linear programming model able The simulations and numerical analyses have been performed under the Windows Azure

Maintenance Service Configuration Optimization for Complex Equipment

“…Hapsari et al [5] studied the optimal configuration of liquid steel production under the condition of multiple objectives. Malekimajd M et al [6] established a linear programming model, which can minimize the cost of cloud resources, reject the execution of operations belonging to multiple classes, and provide a deadline guarantee. Badurdeen et al [7] considered the recyclability of product modules and used multi-objective optimization to optimize product configuration decisions from the perspective of sustainability of product life cycle.…”