12,383 research outputs found
Early Observations on Performance of Google Compute Engine for Scientific Computing
Although Cloud computing emerged for business applications in industry,
public Cloud services have been widely accepted and encouraged for scientific
computing in academia. The recently available Google Compute Engine (GCE) is
claimed to support high-performance and computationally intensive tasks, while
little evaluation studies can be found to reveal GCE's scientific capabilities.
Considering that fundamental performance benchmarking is the strategy of
early-stage evaluation of new Cloud services, we followed the Cloud Evaluation
Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon
EC2, to help understand the elementary capability of GCE for dealing with
scientific problems. The experimental results and analyses show both potential
advantages of, and possible threats to applying GCE to scientific computing.
For example, compared to Amazon's EC2 service, GCE may better suit applications
that require frequent disk operations, while it may not be ready yet for single
VM-based parallel computing. Following the same evaluation methodology,
different evaluators can replicate and/or supplement this fundamental
evaluation of GCE. Based on the fundamental evaluation results, suitable GCE
environments can be further established for case studies of solving real
science problems.Comment: Proceedings of the 5th International Conference on Cloud Computing
Technologies and Science (CloudCom 2013), pp. 1-8, Bristol, UK, December 2-5,
201
Investigating Decision Support Techniques for Automating Cloud Service Selection
The compass of Cloud infrastructure services advances steadily leaving users
in the agony of choice. To be able to select the best mix of service offering
from an abundance of possibilities, users must consider complex dependencies
and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal
on investigating an intelligent decision support system for selecting Cloud
based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac
Theory of Deep Learning IIb: Optimization Properties of SGD
In Theory IIb we characterize with a mix of theory and experiments the
optimization of deep convolutional networks by Stochastic Gradient Descent. The
main new result in this paper is theoretical and experimental evidence for the
following conjecture about SGD: SGD concentrates in probability -- like the
classical Langevin equation -- on large volume, "flat" minima, selecting flat
minimizers which are with very high probability also global minimizer
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints
The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach
Monetary Policy vs. Foreign Exchange Rate: A Statistical Analysis
The purpose of this paper is to examine the economic impact of the Fed’s rate cuts on foreign exchange movements. Using secondary data, the paper estimates the lagged effects of the changes in money supply due to the rate cuts on the foreign exchange rates between the US dollar and the Japanese Yen (/£), and the euro ($/€), respectively. Since the impact of monetary policy tends to have a time lag, as suggested by Hall and Taylor, the study segments the measurements in six months intervals (6 months form the cut, 12 months from the cut, 18 months from the cut and 24 months from the cut). The relationship between the changes in money supply and potential impact on foreign exchange rate movements will be investigated using the Pearson Product-Moment Correlation coefficients (PPMCC) as well as Spearman’s Rank Correlation coefficients (SRCC, the nonparametric alternative to the PPMCC). Then, a hypothesis test will be conducted to determine whether the correlation between the Federal Reserve’s stimulating monetary policy and foreign exchange rate movements is significant
The Effect of North American Free Trade Agreement (NAFTA): Ten Years Later
This paper examines the economic impact of the North American Free Trade Agreement (NAFTA) on international trade among the three member countries – Canada, Mexico and the United States, in the past ten years. Through regression techniques, estimated volume and the predicted trend for exports among the countries are compared with the actual observations. The empirical results indicate that NAFTA did achieve the desired goal of increasing trade among their member countries. The actual trade volume is greater than what the estimated trade volume would have been without NAFTA. Although all the member countries have seen their exports increased, the volumes vary among the three, with Mexico being the largest beneficiary
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