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

Exploring the SCOAP3 model for high energy physics : a new innovation in open access

A new model for funding high energy physics (HEP) journals has emerged and is drawing concerted interest and questions from the library community—SCOAP3, the Sponsoring Consortium for Open Access in Particle Physics Publishing....This initiative redirects institutional journal subscription dollars through an international consortium to pay for peer-review management, editing and formatting services, and ensures author rights for open reuse and sharing of published papers, as well as instituting a bidding process to establish the price of these services

An Introduction to Mechanized Reasoning

Mechanized reasoning uses computers to verify proofs and to help discover new theorems. Computer scientists have applied mechanized reasoning to economic problems but -- to date -- this work has not yet been properly presented in economics journals. We introduce mechanized reasoning to economists in three ways. First, we introduce mechanized reasoning in general, describing both the techniques and their successful applications. Second, we explain how mechanized reasoning has been applied to economic problems, concentrating on the two domains that have attracted the most attention: social choice theory and auction theory. Finally, we present a detailed example of mechanized reasoning in practice by means of a proof of Vickrey's familiar theorem on second-price auctions

Fiscal competition for FDI when bidding is costly

We introduce bidding costs into a standard model of tax/subsidy competition between two potential host countries to attract the plant of a monopoly firm. Such a bidding cost, even if it is infinitesimal, qualitatively alters the resulting equilibrium. At most one country offers fiscal inducements to the firm, and this attenuates the familiar "race to the bottom" in corporate taxes. In general, the successful host country benefits from the resulting absence of active tax/subsidy competition, at the expense of the owners of the firm in the rest of the world

Wind generator behaviour in a pay-as-bid curtailment market

A pay-as-bid curtailment market, where Wind Power Plants (WPPs) may offer prices to have their output reduced in the event of network balancing or stability constraints, is one approach towards the market integration of a high proportion of wind energy onto a power system. Such a market aims to procure curtailment at a cost close to the marginal value of the electricity plus renewable subsidies and incentives, reducing risks for WPPs while minimising costs to the Independent System Operator (ISO). Through the use of game theory and market modelling, a key set of bidding strategies are identified that may evolve within such a market, which may act in opposition to the goals of the ISO. These are applied to a variety of network conditions in order to determine their likely impact and the resulting bidding signals provided to market participants. Bidding behaviours and market fluidity may also be affected by factors particular to wind power plants. Through analysis of both ex ante and ex post case studies, the existence of these behaviours is demonstrated, illustrating that a pay-as-bid curtailment market may not be efficient at price discovery in practice

Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL