Wednesday, 13 December 2017

Session 3: Power & Performance

Speeding Up Processing Data from Millions of Smart Meters

Authors:

Jiang Zheng (ABB Inc., US Corporate Research)
Zhao Li (ABB Inc., US Corporate Research)
Aldo Dagnino (ABB Inc., US Corporate Research)

Abstract:

As an important element of the Smart Grid, Advanced Metering Infrastructure (AMI) systems have been implemented and deployed throughout the world in the past several years. An AMI system connects millions of end devices (e.g., smart meters and sensors in the residential level) with utility control centers via an efficient two-way communication infrastructure. AMI systems are able to exchange substantial meter data and control information between utilities and end devices in real-time or near real-time. The major challenge our research was to scale ABB’s Meter Data Management System (MDMS) to manage data that originates from millions of smart meters. We designed a lightweight architecture capable of collect ever-increasing large amount of meter data from various metering systems, clean, analyze, and aggregate the meter data to support various smart grid applications. To meet critical high performance requirements, various concurrency processing techniques were implemented and integrated in our prototype. Our experiments showed that on average the implemented data file parser took about 42 minutes to complete parsing, cleaning, and aggregating 5.184 billion meter reads on a single machine with the hardware configuration of 12- core CPU, 32G RAM, and SSD Hard Drives. The throughput is about 7.38 billion meter reads (206.7GB data) per hour (i.e., 1811TB/year). In addition, well-designed publish/subscribe and communication infrastructures ensure the scalability and flexibility of the system.

DOI: 10.1145/2568088.2576798

Full text: PDF

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Automated Analysis of Performance and Energy Consumption for Cloud Applications

Authors:

Feifei Chen (Swinburne University of Technology)
John Grundy (Swinburne University of Technology)
Jean-Guy Schneider (Swinburne University of Technology)
Yun Yang (Swinburne University of Technology)
Qiang He (Swinburne University of Technology)

Abstract:

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

DOI: 10.1145/2568088.2568093

Full text: PDF

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An Experimental Methodology to Evaluate Energy Efficiency and Performance in an Enterprise Virtualized Environment

Authors:

Jesus Omana Iglesias (University College Dublin)
Philip Perry (University College Dublin)
Liam Murphy (University College Dublin)
Teodora Sandra Buda (University College Dublin)
James Thorburn (IBM Software Group)

Abstract:

Computing servers generally have a narrow dynamic power range. For instance, even completely idle servers consume between 50% and 70% of their peak power. Since the usage rate of the server has the main influence on its power consumption, energy-efficiency is achieved whenever the utilization of the servers that are powered on reaches its peak. For this purpose, enterprises generally adopt the following technique: consolidate as many workloads as possible via virtualization in a minimum amount of servers (i.e. maximize utilization) and power down the ones that remain idle (i.e. reduce power consumption). However, such approach can severely impact servers’ performance and reliability. In this paper, we propose a methodology to determine the ideal values for power consumption and utilization for a server without performance degradation. We accomplish this through a series of experiments using two typical types of workloads commonly found in enterprises: TPC-H and SPECpower ssj2008 benchmarks. We use the first to measure the amount of queries responded successfully per hour for different numbers of users (i.e. Throughput@Size) in the VM. Moreover, we use the latter to measure the power consumption and number of operations successfully handled by a VM at different target loads. We conducted experiments varying the utilization level and number of users for different VMs and the results show that it is possible to reach the maximum value of power consumption for a server, without experiencing performance degradations when running individual, or mixing workloads.

DOI: 10.1145/2568088.2568099

Full text: PDF

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