{'fraction_non_alphanumeric': 0.017628205128205128, 'fraction_numerical': 0.0, 'mean_word_length': 5.438144329896907, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fine-grained power analysis of emerging graph processing workloads for cloud operations management

In modern cloud computing and analytics applications, large-scale data is often represented in the form of graphs. Many recent works have focused on understanding and improving performance of graph processing frameworks. Power consumption, which also serves as a key factor in the deployment and management of graph processing frameworks, has not been extensively studied. In this paper, we demonstrate the use of an online software power estimation tool that is capable of obtaining fine-grained power traces. By leveraging component-level power behavior, we show that static power consumption still constitutes a significant portion of the total power. Moreover, we illustrate the impact of various dynamic voltage and frequency scaling polices on these workloads, and observe that setting the computing node to its maximum frequency can achieve optimal performance and energy consumption. From our analysis on the impact of machine scale-up, we conclude that computing nodes with small number of computing threads consume more energy than the powerful ones. This observation can help cloud administrators on energy-efficient resource allocation.