Location-aware brokering for consumers in multi-cloud computing environments

The variety and complexity in cloud marketplaces is growing, making it difficult for cloud consumers to choose cloud services from multiple providers in an economic and suitable way by taking into account multiple objectives and constraints. In this paper, we present an extension of CloudSim implementing cloud management functionality to enable the assessment of consumer-oriented brokering schemes. The underlying discrete-event simulation framework allows evaluating their performance in more realistic operating conditions in a repeatable manner. We integrate brokering mechanisms to support a multi-criteria location-aware selection of virtual machines in multi-cloud environments by implementing a greedy heuristic and two large neighborhood search metaheuristics. Based on microbenchmarks of real cloud offerings and a diverse set of scenarios and workloads, we conduct simulation experiments to assess the performance of our approaches. The results show that approximately 10 - 12% of the total costs can be saved by using a large neighborhood search approach compared to the greedy heuristic. Finally, we analyze and discuss the trade-off between costs and latency as well as the impact of region constraints, showing, e.g., that latency improvements often come at a high price and a greater regional flexibility can lead to latency improvements while solely optimizing costs. Using real data of cloud marketplaces, we show that the proposed CloudSim extension can support decision makers as a tool for assessing cloud portfolios and market dynamics.