26,555 research outputs found
A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances
Cloud providers sell their idle capacity on markets through an auction-like
mechanism to increase their return on investment. The instances sold in this
way are called spot instances. In spite that spot instances are usually 90%
cheaper than on-demand instances, they can be terminated by provider when their
bidding prices are lower than market prices. Thus, they are largely used to
provision fault-tolerant applications only. In this paper, we explore how to
utilize spot instances to provision web applications, which are usually
considered availability-critical. The idea is to take advantage of differences
in price among various types of spot instances to reach both high availability
and significant cost saving. We first propose a fault-tolerant model for web
applications provisioned by spot instances. Based on that, we devise novel
auto-scaling polices for hourly billed cloud markets. We implemented the
proposed model and policies both on a simulation testbed for repeatable
validation and Amazon EC2. The experiments on the simulation testbed and the
real platform against the benchmarks show that the proposed approach can
greatly reduce resource cost and still achieve satisfactory Quality of Service
(QoS) in terms of response time and availability
A study on performance measures for auto-scaling CPU-intensive containerized applications
Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented
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
Forecasting inflation with thick models and neural networks
This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models
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