64 research outputs found
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope
Multi-stage serverless applications, i.e., workflows with many computation
and I/O stages, are becoming increasingly representative of FaaS platforms.
Despite their advantages in terms of fine-grained scalability and modular
development, these applications are subject to suboptimal performance, resource
inefficiency, and high costs to a larger degree than previous simple serverless
functions.
We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for
end-to-end serverless workflows that takes into account the inherent
uncertainty present in FaaS platforms, and improves performance predictability
and resource efficiency. Aquatope uses a set of scalable and validated Bayesian
models to create pre-warmed containers ahead of function invocations, and to
allocate appropriate resources at function granularity to meet a complex
workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set
of analytics and interactive multi-stage serverless workloads, Aquatope
significantly outperforms prior systems, reducing QoS violations by 5x, and
cost by 34% on average and up to 52% compared to other QoS-meeting methods
Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices
Hybrid cloud provides an attractive solution to microservices for better
resource elasticity. A subset of application components can be offloaded from
the on-premises cluster to the cloud, where they can readily access additional
resources. However, the selection of this subset is challenging because of the
large number of possible combinations. A poor choice degrades the application
performance, disrupts the critical services, and increases the cost to the
extent of making the use of hybrid cloud unviable. This paper presents Atlas, a
hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how
each user-facing API utilizes different components and their network footprints
to drive the migration decision. It learns to accelerate the discovery of
high-quality migration plans from millions and offers recommendations with
customizable trade-offs among three quality indicators: end-to-end latency of
user-facing APIs representing application performance, service availability,
and cloud hosting costs. Atlas continuously monitors the application even after
the migration for proactive recommendations. Our evaluation shows that Atlas
can achieve 21% better API performance (latency) and 11% cheaper cost with less
service disruption than widely used solutions.Comment: To appear at EuroSys 202
The workflow trace archive:Open-access data from public and private computing infrastructures
Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. In this work, we focus on traces of workflows - common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes {>}48>48 million workflows captured from {>}10>10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields
The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures -- Technical Report
Realistic, relevant, and reproducible experiments often need input traces
collected from real-world environments. We focus in this work on traces of
workflows---common in datacenters, clouds, and HPC infrastructures. We show
that the state-of-the-art in using workflow-traces raises important issues: (1)
the use of realistic traces is infrequent, and (2) the use of realistic, {\it
open-access} traces even more so. Alleviating these issues, we introduce the
Workflow Trace Archive (WTA), an open-access archive of workflow traces from
diverse computing infrastructures and tooling to parse, validate, and analyze
traces. The WTA includes million workflows captured from
computing infrastructures, representing a broad diversity of trace domains and
characteristics. To emphasize the importance of trace diversity, we
characterize the WTA contents and analyze in simulation the impact of trace
diversity on experiment results. Our results indicate significant differences
in characteristics, properties, and workflow structures between workload
sources, domains, and fields.Comment: Technical repor
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