Dynamic Physiological Partitioning on a Shared-nothing Database Cluster

Traditional DBMS servers are usually over-provisioned for most of their daily workloads and, because they do not show good-enough energy proportionality, waste a lot of energy while underutilized. A cluster of small (wimpy) servers, where its size can be dynamically adjusted to the current workload, offers better energy characteristics for these workloads. Yet, data migration, necessary to balance utilization among the nodes, is a non-trivial and time-consuming task that may consume the energy saved. For this reason, a sophisticated and easy to adjust partitioning scheme fostering dynamic reorganization is needed. In this paper, we adapt a technique originally created for SMP systems, called physiological partitioning, to distribute data among nodes, that allows to easily repartition data without interrupting transactions. We dynamically partition DB tables based on the nodes' utilization and given energy constraints and compare our approach with physical partitioning and logical partitioning methods. To quantify possible energy saving and its conceivable drawback on query runtimes, we evaluate our implementation on an experimental cluster and compare the results w.r.t. performance and energy consumption. Depending on the workload, we can substantially save energy without sacrificing too much performance

EFFICIENT TIME-ENERGY EXECUTION OF DATA-PARALLEL APPLICATIONS ON HETEROGENEOUS SYSTEMS WITH GPU

Ph.DDOCTOR OF PHILOSOPH

An Experimental Evaluation of Datacenter Workloads On Low-Power Embedded Micro Servers

This paper presents a comprehensive evaluation of an ultra-low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.This research was supported in part by NSF grant 13-20209.Ope

The Mont-Blanc prototype: an alternative approach for high-performance computing systems

High-performance computing (HPC) is recognized as one of the pillars for further advance of science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging challenges in order to reach Exascale level of performance,which is expected by the year 2020. The much larger embedded and mobile market allows for rapid development of IP blocks, and provides more flexibility in designing an application-specific SoC, in turn giving possibility in balancing performance, energy-efficiency and cost. In the Mont-Blanc project, we advocate for HPC systems be built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and NICs from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling and integration solutions. We present the system’s architecture, and evaluation including both performance and energy efficiency. Further, we compare the system’s abilities against a production level supercomputer. At the end, we discuss parallel scalability, and estimate the maximum scalability point of this approach across a set of HPC applications.Postprint (published version

SoC-Cluster as an Edge Server: an Application-driven Measurement Study

Huge electricity consumption is a severe issue for edge data centers. To this end, we propose a new form of edge server, namely SoC-Cluster, that orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC-Cluster server that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server has been commercialized successfully and deployed in large scale on edge clouds. The current dominant workload on those deployed SoC-Clusters is cloud gaming, as mobile SoCs can seamlessly run native mobile games. The primary goal of this work is to demystify whether SoC-Cluster can efficiently serve more general-purpose, edge-typical workloads. Therefore, we built a benchmark suite that leverages state-of-the-art libraries for two killer edge workloads, i.e., video transcoding and deep learning inference. The benchmark comprehensively reports the performance, power consumption, and other application-specific metrics. We then performed a thorough measurement study and directly compared SoC-Cluster with traditional edge servers (with Intel CPU and NVIDIA GPU) with respect to physical size, electricity, and billing. The results reveal the advantages of SoC-Cluster, especially its high energy efficiency and the ability to proportionally scale energy consumption with various incoming loads, as well as its limitations. The results also provide insightful implications and valuable guidance to further improve SoC-Cluster and land it in broader edge scenarios

Cloud computing: survey on energy efficiency

International audienceCloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions