130,591 research outputs found
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon\u27s Public Cloud Infrastructure
Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon\u27s public cloud infrastructure using compute, memory and IO resource utilization data
Anomaly Detection in Cloud Components
Cloud platforms, under the hood, consist of a complex inter-connected stack
of hardware and software components. Each of these components can fail which
may lead to an outage. Our goal is to improve the quality of Cloud services
through early detection of such failures by analyzing resource utilization
metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood
function to detect anomalies in various multi-dimensional time series and
achieved high performance.Comment: Accepted for publication in Proceedings of the IEEE International
Conference on Cloud Computing (CLOUD 2020). Fix dataset descriptio
Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets
Cloud spot markets rent VMs for a variable price that is typically much lower
than the price of on-demand VMs, which makes them attractive for a wide range
of large-scale applications. However, applications that run on spot VMs suffer
from cost uncertainty, since spot prices fluctuate, in part, based on supply,
demand, or both. The difficulty in predicting spot prices affects users and
applications: the former cannot effectively plan their IT expenditures, while
the latter cannot infer the availability and performance of spot VMs, which are
a function of their variable price. To address the problem, we use properties
of cloud infrastructure and workloads to show that prices become more stable
and predictable as they are aggregated together. We leverage this observation
to define an aggregate index price for spot VMs that serves as a reference for
what users should expect to pay. We show that, even when the spot prices for
individual VMs are volatile, the index price remains stable and predictable. We
then introduce cloud index tracking: a migration policy that tracks the index
price to ensure applications running on spot VMs incur a predictable cost by
migrating to a new spot VM if the current VM's price significantly deviates
from the index price.Comment: ACM Symposium on Cloud Computing 201
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The use of Petri nets for modeling pipelined processors
This paper discusses the use of Petri Nets for modeling and analyzing pipelined processors. Petri Nets are particularly well-suited to modeling the synchronization, buffering, resource contention and delicate timing so common in pipelined processors. Tools for simulating, animating and analyzing the behavior of the models are described. The usefulness of the tools and the analysis methods they support in evaluating the performance and analyzing the detailed timing of pipelined microprocessors is illustrated through an example
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