Pricing the Cloud: An Adaptive Brokerage for Cloud Computing

Abstract—Using a multi-agent social simulation model to predict the behavior of cloud computing markets, Rogers & Cliff (R&C) demonstrated the existence of a profitable cloud brokerage capable of benefitting cloud providers and cloud consumers alike. Functionally similar to financial market brokers, the cloud broker matches provider supply with consumer demand. This is achieved through options, a type of derivatives contract that enables consumers to purchase the option, but not the obligation, of later purchasing the underlying asset—a cloud computing virtual machine instance—for an agreed fixed price. This model benefits all parties: experiencing more predictable demand, cloud providers can better optimize their workflow to minimize costs; cloud users access cheaper rates offered by brokers; and cloud brokers generate profit from charging fees. Here, we replicate and extend the simulation model of R&C using CReST—an opensource, discrete event, cloud data center simulation modeling platform developed at the University of Bristol. Sensitivity analysis reveals fragility in R&C’s model. We address this by introducing a novel method of autonomous adaptive thresholding (AAT) that enables brokers to adapt to market conditions without requiring a priori domain knowledge. Simulation results demonstrate AAT’s robustness, outperforming the fixed brokerage model of R&C under a variety of market conditions. We believe this could have practical significance in the real-world market for cloud computing. Keywords—CReST; simulation; cloud computing; brokerage I

Not feeling the buzz:Correction study of mispricing and inefficiency in online sportsbooks

We present a strict replication and correction of results published in a recent article (Ramirez, P., Reade, J.J., Singleton, C., Betting on a buzz: Mispricing and inefficiency in online sportsbooks, International Journal of Forecasting, 2022, doi:10.1016/j.ijforecast.2022.07.011). RRS introduced a novel "buzz factor" metric for tennis players, calculated as the log difference between the number of Wikipedia profile page views a player receives the day before a tennis match and the player's median number of daily profile views. The authors claim that their buzz factor metric is able to predict mispricing by bookmakers and they demonstrate that it can be used to form a profitable strategy for betting on tennis match outcomes. Here, we use the same dataset as RRS to reproduce their results exactly. However, we discover that the published results are significantly affected by a single bet (the "Hercog" bet) that returns substantial outlier profits; and these profits are generated by taking advantage of erroneously long odds in the out-of-sample test data. Once this data quality issue is addressed, we show that the strategy of RRS is no longer profitable in "practical" scenarios. Using an extended and cleaned dataset, we then perform further exploration of the models and show that the "impractical" betting strategy that uses best odds in the market remains profitable (in theory). However, evidence suggests that the vast majority of returns are generated by exploiting individual bookmaker's mispricing of odds relative to the market, and the novel buzz factor metric has negligible contribution to profits. We make all code and data available online

Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks

We present a strict replication and correction of results published in a recent article (Ramirez, P., Reade, J.J., Singleton, C., Betting on a buzz: Mispricing and inefficiency in online sportsbooks, International Journal of Forecasting, 2022, doi:10.1016/j.ijforecast.2022.07.011). RRS introduced a novel "buzz factor" metric for tennis players, calculated as the log difference between the number of Wikipedia profile page views a player receives the day before a tennis match and the player's median number of daily profile views. The authors claim that their buzz factor metric is able to predict mispricing by bookmakers and they demonstrate that it can be used to form a profitable strategy for betting on tennis match outcomes. Here, we use the same dataset as RRS to reproduce their results exactly. However, we discover that the published results are significantly affected by a single bet (the "Hercog" bet) that returns substantial outlier profits; and these profits are generated by taking advantage of erroneously long odds in the out-of-sample test data. Once this data quality issue is addressed, we show that the strategy of RRS is no longer profitable in "practical" scenarios. Using an extended and cleaned dataset, we then perform further exploration of the models and show that the "impractical" betting strategy that uses best odds in the market remains profitable (in theory). However, evidence suggests that the vast majority of returns are generated by exploiting individual bookmaker's mispricing of odds relative to the market, and the novel buzz factor metric has negligible contribution to profits. We make all code and data available online.Comment: 26 pages, 2 figures. This paper is a replication study. Problems in the original study are discovered and corrected. Replication code and data are available online: https://github.com/Faxulous/notFeelingTheBuz

The Limit Order Book Recreation Model (LOBRM): An Extended Analysis

The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies. Nevertheless, the availability and high cost of LOB data restrict its wider application. The LOB recreation model (LOBRM) was recently proposed to bridge this gap by synthesizing the LOB from trades and quotes (TAQ) data. However, in the original LOBRM study, there were two limitations: (1) experiments were conducted on a relatively small dataset containing only one day of LOB data; and (2) the training and testing were performed in a non-chronological fashion, which essentially re-frames the task as interpolation and potentially introduces lookahead bias. In this study, we extend the research on LOBRM and further validate its use in real-world application scenarios. We first advance the workflow of LOBRM by (1) adding a time-weighted z-score standardization for the LOB and (2) substituting the ordinary differential equation kernel with an exponential decay kernel to lower computation complexity. Experiments are conducted on the extended LOBSTER dataset in a chronological fashion, as it would be used in a real-world application. We find that (1) LOBRM with decay kernel is superior to traditional non-linear models, and module ensembling is effective; (2) prediction accuracy is negatively related to the volatility of order volumes resting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits good generalization ability and can facilitate manifold tasks; and (4) the influence of stochastic drift on prediction accuracy can be alleviated by increasing historical samples.Comment: 16 pages, preprint accepted for publication in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021

Estimating Demand for Dynamic Pricing in Electronic Markets

Economic theory suggests sellers can increase revenue throughdynamic pricing; selling identical goods or servicesat different prices. However, such discrimination requiresknowledge of the maximum price that each consumer is willingto pay; information that is often unavailable. Fortunately,electronic markets offer a solution; generating vastquantities of transaction data that, if used intelligently, enableconsumer behaviour to be modelled and predicted.Using eBay as an exemplar market, we introduce a model fordynamic pricing that uses a statistical method for derivingthe structure of demand from temporal bidding data. Thiswork is a tentative first step of a wider research programto discover a practical methodology for automatically generatingdynamic pricing models for the provision of cloudcomputing services; a pertinent problem with widespreadcommercial and theoretical interest