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One of the main challenges in cloud computing is an enormous amount of energy consumed in data-centers. Several researches have been conducted on Virtual Machine(VM) consolidation to optimize energy consumption. Among the proposed VM consolidations, OpenStack Neat is notable for its practicality. OpenStack Neat is an open-source consolidation framework that can seamlessly integrate to OpenStack, one of the most common and widely used opensource cloud management tool. The framework has components for deciding when to migrate VMs and for selecting suitable hosts for the VMs (VM placement). The VM placement algorithm of OpenStack Neat is called Modified Best-Fit Decreasing (MBFD). MBFD is based on a heuristic that handles only minimizing the number of servers. The heuristic is not only less energy efficient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. To improve the energy efficiency, we propose VM placement algorithms based on both bin-packing heuristics and servers' power efficiency. In addition, we introduce a new bin-packing heuristic called a Medium-Fit (MF) to reduce SLA violation. To evaluate performance of the proposed algorithms we have conducted experiments using CloudSim on three cloud data-center scenarios: homogeneous, heterogeneous and default. Workloads that run in the data-centers are generated from traces of PlanetLab and Bitbrains clouds. The results of the experiment show up-to 67% improvement in energy consumption and up-to 78% and 46% reduction in SLA violation and amount of VM migrations, respectively. Moreover, all improvements are statistically significant with significance level of 0.01.

Context: IR-based bug localization is a classifier that assists developers in locating buggy source code entities (e.g., files and methods) based on the content of a bug report. Such IR-based classifiers have various parameters that can be configured differently (e.g., the choice of entity representation). Objective: In this paper, we investigate the impact of the choice of the IR-based classifier configuration on the top-k performance and the required effort to examine source code entities before locating a bug at the method level. Method: We execute a large space of classifier configuration, 3,172 in total, on 5,266 bug reports of two software systems, i.e., Eclipse and Mozilla. Results: We find that (1) the choice of classifier configuration impacts the top-k performance from 0.44% to 36% and the required effort from 4,395 to 50,000 LOC; (2) classifier configurations with similar top-k performance might require different efforts; (3) VSM achieves both the best top-k performance and the least required effort for method-level bug localization; (4) the likelihood of randomly picking a configuration that performs within 20% of the best top-k classifier configuration is on average 5.4% and that of the least effort is on average 1%; (5) configurations related to the entity representation of the analyzed data have the most impact on both the top-k performance and the required effort; and (6) the most efficient classifier configuration obtained at the method-level can also be used at the file-level (and vice versa). Conclusion: Our results lead us to conclude that configuration has a large impact on both the top-k performance and the required effort for method-level bug localization, suggesting that the IR-based configuration settings should be carefully selected and the required effort metric should be included in future bug localization studies.

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