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Recent technological advancements have enabled a generation of Ultra-Low Latency (ULL) SSDs that blurs the performance gap between primary and secondary storage devices. However, their power consumption characteristics are largely unknown. In addition, ULL performance in a block device is expected to put extra pressure on operating system components, significantly affecting energy efficiency of the entire system. In this work, we empirically study overall energy efficiency using a real ULL storage device, Optane SSD, a power meter, and a wide range of IO workload behaviors. We present a comparative analysis by laying out several critical observations related to idle vs. active behavior, read vs. write behavior, energy proportionality, impact on system software, as well as impact on overall energy efficiency. To the best of our knowledge, this is the first published study of a ULL SSD's impact on the system's overall power consumption, which can hopefully lead to future energy-efficient designs. 

The growing energy consumption of data centers is a compelling global problem and effective server consolidation is at the heart of energy efficient cloud data centers. A variant of bin packing can be used to model the server consolidation problem, where the constraints are multidimensional and heterogeneous vectors rather than scalars and the goal is to satisfy the requested resource allocation using the minimum number physical servers. Since bin packing is NP-hard, we rely on heuristics for practical solutions. Variations of First Fit Decreasing (FFD) based heuristics have been shown to be effective both in theory and practice for the one dimensional homogeneous case. However, the multidimensional and heterogeneous aspects of the server consolidation problem make it more complicated, requiring additional research to adapt FFD to the server consolidation problem. In this paper, we present a new FFD-based server consolidation technique using a Monte Carlo method and Shannon entropy, which considers resource bottlenecks and dynamically adjusts to variance in the utilization of different resources. The proposed heuristic outperforms existing techniques in all scenarios, achieving within 2-5% of optimal on average for medium to high variance in resource utilization, and within 10% worse than optimal on average for all scenarios. 

While society continues to be transformed by insights from processing big data, the increasing rate at which this data is gathered is making processing in private clusters obsolete. A vast amount of big data already resides in the cloud, and cloud infrastructures provide a scalable platform for both the computational and I/O needs of big data processing applications. Virtualization is used as a base technology in the cloud; however, existing virtual machine placement techniques do not consider data replication and I/O bottlenecks of the infrastructure, yielding sub-optimal data retrieval times. This paper targets efficient big data processing in the cloud and proposes novel virtual machine placement techniques, which minimize data retrieval time by considering data replication, storage performance, and network bandwidth. We first present an integer-programming based optimal virtual machine placement algorithm and then propose two low cost data- and energy-aware virtual machine placement heuristics. Our proposed heuristics are compared with optimal and existing algorithms through extensive evaluation. Experimental results provide strong indications for the superiority of our proposed solutions in both performance and energy, and clearly outline the importance of big data aware virtual machine placement for efficient processing of large datasets in the cloud. 

In the US alone, data centers consumed around $20 billion (200 TWh) yearly electricity in 2016, and this amount doubles itself every five years. Data storage alone is estimated to be responsible for about 25% to 35% of data-center power consumption. Servers in data centers generally include multiple HDDs or SSDs, commonly arranged in a RAID level for better performance, reliability, and availability. In this study, we evaluate HDD and SSD based Linux (md) software RAIDs' impact on the energy consumption of popular servers. We used the Filebench workload generator to emulate three common server workloads: web, file, and mail, and measured the energy consumption of the system using the HOBO power meter. We observed some similarities and some differences in energy consumption characteristics of HDD and SSD RAIDs, and provided our insights for better energy-efficiency. We hope that our observations will shed light on new energy-efficient RAID designs tailored for HDD and SSD RAIDs' specific energy consumption characteristics.