1,910 research outputs found
Infrastructure Operations Final Report
This document serves as a final report of the activities and achievements of WP5 throughout the whole duration of the project. The document covers the areas of infrastructure operation, service provisioning, support, testing and benchmarking. In addition, the document provides a record of the practical knowledge accumulated during the provision of various public cloud services over a period of almost two years
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U.S. Government has
been making a concerted effort to ensure American leadership in AI. These broad
strategy documents have influenced organizations such as the United States
Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative
between the DAF and MIT to bridge the gap between AI researchers and DAF
mission requirements. Several projects supported by the DAF-MIT AI Accelerator
are developing public challenge problems that address numerous Federal AI
research priorities. These challenges target priorities by making large,
AI-ready datasets publicly available, incentivizing open-source solutions, and
creating a demand signal for dual use technologies that can stimulate further
research. In this article, we describe these public challenges being developed
and how their application contributes to scientific advances
AIOps for a Cloud Object Storage Service
With the growing reliance on the ubiquitous availability of IT systems and
services, these systems become more global, scaled, and complex to operate. To
maintain business viability, IT service providers must put in place reliable
and cost efficient operations support. Artificial Intelligence for IT
Operations (AIOps) is a promising technology for alleviating operational
complexity of IT systems and services. AIOps platforms utilize big data,
machine learning and other advanced analytics technologies to enhance IT
operations with proactive actionable dynamic insight.
In this paper we share our experience applying the AIOps approach to a
production cloud object storage service to get actionable insights into
system's behavior and health. We describe a real-life production cloud scale
service and its operational data, present the AIOps platform we have created,
and show how it has helped us resolving operational pain points.Comment: 5 page
BigDataBench: a Big Data Benchmark Suite from Internet Services
As architecture, systems, and data management communities pay greater
attention to innovative big data systems and architectures, the pressure of
benchmarking and evaluating these systems rises. Considering the broad use of
big data systems, big data benchmarks must include diversity of data and
workloads. Most of the state-of-the-art big data benchmarking efforts target
evaluating specific types of applications or system software stacks, and hence
they are not qualified for serving the purposes mentioned above. This paper
presents our joint research efforts on this issue with several industrial
partners. Our big data benchmark suite BigDataBench not only covers broad
application scenarios, but also includes diverse and representative data sets.
BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench .
Also, we comprehensively characterize 19 big data workloads included in
BigDataBench with varying data inputs. On a typical state-of-practice
processor, Intel Xeon E5645, we have the following observations: First, in
comparison with the traditional benchmarks: including PARSEC, HPCC, and
SPECCPU, big data applications have very low operation intensity; Second, the
volume of data input has non-negligible impact on micro-architecture
characteristics, which may impose challenges for simulation-based big data
architecture research; Last but not least, corroborating the observations in
CloudSuite and DCBench (which use smaller data inputs), we find that the
numbers of L1 instruction cache misses per 1000 instructions of the big data
applications are higher than in the traditional benchmarks; also, we find that
L3 caches are effective for the big data applications, corroborating the
observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High
Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando,
Florida, US
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