3,404 research outputs found
Dynamic Fine-Grained Scheduling for Energy-Efficient Main-Memory Queries
Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in this area is energy proportionality, arguing that the system's power consumption should be proportional to its performance. Currently, a major trend among server processors, which stems from the design of chips for mobile devices, is the inclusion of advanced power management techniques, such as dynamic voltage-frequency scaling, clock gating, and turbo modes. A lot of recent work on energy efficiency of database management systems is focused on coarse-grained power management at the granularity of multiple machines and whole queries. These techniques, however, cannot efficiently adapt to the frequently fluctuating behavior of contemporary workloads. In this paper, we argue that databases should employ a fine-grained approach by dynamically scheduling tasks using precise hardware models. These models can be produced by calibrating operators under different combinations of scheduling policies, parallelism, and memory access strategies. The models can be employed at run-time for dynamic scheduling and power management in order to improve the overall energy efficiency. We experimentally show that energy efficiency can be improved by up to 4x for fundamental memory-intensive database operations, such as scans
TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications
Datacenters running on-line, data-intensive applications (OLDIs) consume
significant amounts of energy. However, reducing their energy is challenging
due to their tight response time requirements. A key aspect of OLDIs is that
each user query goes to all or many of the nodes in the cluster, so that the
overall time budget is dictated by the tail of the replies' latency
distribution; replies see latency variations both in the network and compute.
Previous work proposes to achieve load-proportional energy by slowing down the
computation at lower datacenter loads based directly on response times (i.e.,
at lower loads, the proposal exploits the average slack in the time budget
provisioned for the peak load). In contrast, we propose TimeTrader to reduce
energy by exploiting the latency slack in the sub- critical replies which
arrive before the deadline (e.g., 80% of replies are 3-4x faster than the
tail). This slack is present at all loads and subsumes the previous work's
load-related slack. While the previous work shifts the leaves' response time
distribution to consume the slack at lower loads, TimeTrader reshapes the
distribution at all loads by slowing down individual sub-critical nodes without
increasing missed deadlines. TimeTrader exploits slack in both the network and
compute budgets. Further, TimeTrader leverages Earliest Deadline First
scheduling to largely decouple critical requests from the queuing delays of
sub- critical requests which can then be slowed down without hurting critical
requests. A combination of real-system measurements and at-scale simulations
shows that without adding to missed deadlines, TimeTrader saves 15-19% and
41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512
nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page
Saber: window-based hybrid stream processing for heterogeneous architectures
Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes
Explainable and Resource-Efficient Stream Processing Through Provenance and Scheduling
In our era of big data, information is captured at unprecedented volumes and velocities, with technologies such as Cyber-Physical Systems making quick decisions based on the processing of streaming, unbounded datasets. In such scenarios, it can be beneficial to process the data in an online manner, using the stream processing paradigm implemented by Stream Processing Engines (SPEs). While SPEs enable high-throughput, low-latency analysis, they are faced with challenges connected to evolving deployment scenarios, like the increasing use of heterogeneous, resource-constrained edge devices together with cloud resources and the increasing user expectations for usability, control, and resource-efficiency, on par with features provided by traditional databases.This thesis tackles open challenges regarding making stream processing more user-friendly, customizable, and resource-efficient. The first part outlines our work, providing high-level background information, descriptions of the research problems, and our contributions. The second part presents our three state-of-the-art frameworks for explainable data streaming using data provenance, which can help users of streaming queries to identify important data points, explain unexpected behaviors, and aid query understanding and debugging. (A) GeneaLog provides backward provenance allowing users to identify the inputs that contributed to the generation of each output of a streaming query. (B) Ananke is the first framework to provide a duplicate-free graph of live forward provenance, enabling easy bidirectional tracing of input-output relationships in streaming queries and identifying data points that have finished contributing to results. (C) Erebus is the first framework that allows users to define expectations about the results of a streaming query, validating whether these expectations are met or providing explanations in the form of why-not provenance otherwise. The third part presents techniques for execution efficiency through custom scheduling, introducing our state-of-the-art scheduling frameworks that control resource allocation and achieve user-defined performance goals. (D) Haren is an SPE-agnostic user-level scheduler that can efficiently enforce user-defined scheduling policies. (E) Lachesis is a standalone scheduling middleware that requires no changes to SPEs but, instead, directly guides the scheduling decisions of the underlying Operating System. Our extensive evaluations using real-world SPEs and workloads show that our work significantly improves over the state-of-the-art while introducing only small performance overheads
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Energy Measurements of High Performance Computing Systems: From Instrumentation to Analysis
Energy efficiency is a major criterion for computing in general and High Performance Computing in particular. When optimizing for energy efficiency, it is essential to measure the underlying metric: energy consumption. To fully leverage energy measurements, their quality needs to be well-understood. To that end, this thesis provides a rigorous evaluation of various energy measurement techniques. I demonstrate how the deliberate selection of instrumentation points, sensors, and analog processing schemes can enhance the temporal and spatial resolution while preserving a well-known accuracy. Further, I evaluate a scalable energy measurement solution for production HPC systems and address its shortcomings.
Such high-resolution and large-scale measurements present challenges regarding the management of large volumes of generated metric data. I address these challenges with a scalable infrastructure for collecting, storing, and analyzing metric data. With this infrastructure, I also introduce a novel persistent storage scheme for metric time series data, which allows efficient queries for aggregate timelines.
To ensure that it satisfies the demanding requirements for scalable power measurements, I conduct an extensive performance evaluation and describe a productive deployment of the infrastructure.
Finally, I describe different approaches and practical examples of analyses based on energy measurement data. In particular, I focus on the combination of energy measurements and application performance traces. However, interweaving fine-grained power recordings and application events requires accurately synchronized timestamps on both sides. To overcome this obstacle, I develop a resilient and automated technique for time synchronization, which utilizes crosscorrelation of a specifically influenced power measurement signal. Ultimately, this careful combination of sophisticated energy measurements and application performance traces yields a detailed insight into application and system energy efficiency at full-scale HPC systems and down to millisecond-range regions.:1 Introduction
2 Background and Related Work
2.1 Basic Concepts of Energy Measurements
2.1.1 Basics of Metrology
2.1.2 Measuring Voltage, Current, and Power
2.1.3 Measurement Signal Conditioning and Analog-to-Digital Conversion
2.2 Power Measurements for Computing Systems
2.2.1 Measuring Compute Nodes using External Power Meters
2.2.2 Custom Solutions for Measuring Compute Node Power
2.2.3 Measurement Solutions of System Integrators
2.2.4 CPU Energy Counters
2.2.5 Using Models to Determine Energy Consumption
2.3 Processing of Power Measurement Data
2.3.1 Time Series Databases
2.3.2 Data Center Monitoring Systems
2.4 Influences on the Energy Consumption of Computing Systems
2.4.1 Processor Power Consumption Breakdown
2.4.2 Energy-Efficient Hardware Configuration
2.5 HPC Performance and Energy Analysis
2.5.1 Performance Analysis Techniques
2.5.2 HPC Performance Analysis Tools
2.5.3 Combining Application and Power Measurements
2.6 Conclusion
3 Evaluating and Improving Energy Measurements
3.1 Description of the Systems Under Test
3.2 Instrumentation Points and Measurement Sensors
3.2.1 Analog Measurement at Voltage Regulators
3.2.2 Instrumentation with Hall Effect Transducers
3.2.3 Modular Instrumentation of DC Consumers
3.2.4 Optimal Wiring for Shunt-Based Measurements
3.2.5 Node-Level Instrumentation for HPC Systems
3.3 Analog Signal Conditioning and Analog-to-Digital Conversion
3.3.1 Signal Amplification
3.3.2 Analog Filtering and Analog-To-Digital Conversion
3.3.3 Integrated Solutions for High-Resolution Measurement
3.4 Accuracy Evaluation and Calibration
3.4.1 Synthetic Workloads for Evaluating Power Measurements
3.4.2 Improving and Evaluating the Accuracy of a Single-Node Measuring System
3.4.3 Absolute Accuracy Evaluation of a Many-Node Measuring System
3.5 Evaluating Temporal Granularity and Energy Correctness
3.5.1 Measurement Signal Bandwidth at Different Instrumentation Points
3.5.2 Retaining Energy Correctness During Digital Processing
3.6 Evaluating CPU Energy Counters
3.6.1 Energy Readouts with RAPL
3.6.2 Methodology
3.6.3 RAPL on Intel Sandy Bridge-EP
3.6.4 RAPL on Intel Haswell-EP and Skylake-SP
3.7 Conclusion
4 A Scalable Infrastructure for Processing Power Measurement Data
4.1 Requirements for Power Measurement Data Processing
4.2 Concepts and Implementation of Measurement Data Management
4.2.1 Message-Based Communication between Agents
4.2.2 Protocols
4.2.3 Application Programming Interfaces
4.2.4 Efficient Metric Time Series Storage and Retrieval
4.2.5 Hierarchical Timeline Aggregation
4.3 Performance Evaluation
4.3.1 Benchmark Hardware Specifications
4.3.2 Throughput in Symmetric Configuration with Replication
4.3.3 Throughput with Many Data Sources and Single Consumers
4.3.4 Temporary Storage in Message Queues
4.3.5 Persistent Metric Time Series Request Performance
4.3.6 Performance Comparison with Contemporary Time Series Storage Solutions
4.3.7 Practical Usage of MetricQ
4.4 Conclusion
5 Energy Efficiency Analysis
5.1 General Energy Efficiency Analysis Scenarios
5.1.1 Live Visualization of Power Measurements
5.1.2 Visualization of Long-Term Measurements
5.1.3 Integration in Application Performance Traces
5.1.4 Graphical Analysis of Application Power Traces
5.2 Correlating Power Measurements with Application Events
5.2.1 Challenges for Time Synchronization of Power Measurements
5.2.2 Reliable Automatic Time Synchronization with Correlation Sequences
5.2.3 Creating a Correlation Signal on a Power Measurement Channel
5.2.4 Processing the Correlation Signal and Measured Power Values
5.2.5 Common Oversampling of the Correlation Signals at Different Rates
5.2.6 Evaluation of Correlation and Time Synchronization
5.3 Use Cases for Application Power Traces
5.3.1 Analyzing Complex Power Anomalies
5.3.2 Quantifying C-State Transitions
5.3.3 Measuring the Dynamic Power Consumption of HPC Applications
5.4 Conclusion
6 Summary and Outloo
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
Energy Elasticity on Heterogeneous Hardware using Adaptive Resource Reconfiguration LIVE
Energy awareness of database systems has emerged as a critical research topic, since energy consumption is becoming a major limiter for their scalability. Recent energy-related hardware developments trend towards offering more and more configuration opportunities for the software to control its own energy consumption. Existing research so far mainly focused on leveraging this configuration spectrum to find the most energy-efficient configuration for specific operators or entire queries. In this demo, we introduce the concept of energy elasticity and propose the energy-control loop as an implementation of this concept. Energy elasticity refers to the ability of software to behave energy-proportional and energy-efficient at the same time while maintaining a certain quality of service. Thus, our system does not draw the least energy possible but the least energy necessary to still perform reasonably. We demonstrate our overall approach using a rich interactive GUI to give attendees the opportunity to learn more about our concept
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