Parallelism and Scalability: A Solution Focused on the Cloud Computing Processing Service Billing

Abstract

The application scheduling is an important requirement in the cloud computing context. It allows to define the required resources to execute applications tasks following predefined criteria, for instance, maximum execution time, number of virtual machines, volume of data, among others. Selecting process to choose the most appropriate execution structure is driven by scheduling algorithms. This paper proposes a scheduling mechanism for data processing in cloud computing environments. Such mechanism analyzes some specific variables in the business context of a software house specialized in software for lawyers and law offices. The main goal of this mechanism is to fulfill the seasonal company's demand using IaaS services and considering two policies: (i) the maximum execution time allowed by the application may not be exceeded and (ii) the data have to be processed considering the lowest possible monetary cost. The proposed solution generates strategies to select the best set of virtual machines to process the current bunch of data considering the amount of data, the estimated execution time for each specific strategy and the monetary cost of the virtual machines sets. In the context of this work, the strategy concept means the schedule of a set of virtual machines to process a specific amount of data, load balancing decisions and the parallelism of application's execution flow. The proposed solution has resulted in great impact for that company since it allowed the vertiginous increase of the amount of clients served.