{'fraction_non_alphanumeric': 0.02364066193853428, 'fraction_numerical': 0.002364066193853428, 'mean_word_length': 5.61328125, '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}}
Understanding the Impact of vCPU Scheduling on DVFS-Based Power Management in Virtualized Cloud Environment

Virtualized platform has emerged as a prominent environment for cloud computing, especially in today's power-constrained data centers. However, due to a lack of coordination between runtime power management and a virtual CPU (vCPU) scheduler, existing virtualized cloud platform is far from efficient. First, current frequency control mechanism is unable to satisfy the fast-changing vCPU frequency requirement imposed by vCPU scheduler, which we refer to as demand imbalance problem. In addition, newly created vCPUs, if scheduled solely based on fairness, can cause inefficient frequency rise and drop on an unmatched physical core, which we refer to as utilization mismatch problem. In both cases, the system incurs degraded power efficiency and sub-optimal workload performance. In this study we perform a comprehensive analysis on the interplay between vCPU scheduling and processor-centric power control in virtualized cloud environment. Using representative workloads from Cloud Suite and real server deployment, we examine the energy/performance implications of frequency scaling and vCPU scheduling on both single-VM and multi-VM cloud host. We show that existing virtualized platform has the potential to improve energy efficiency and workload performance by 32% and 25%, respectively, if vCPUs are balanced and appropriately scheduled. We also show that dirty page rate, virtual block device processing rate, virtual network packets arrival rate, and network I/O buffer availability are important efficiency indicators for energy-efficient virtualized cloud system design.