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Jan Broeckhove - One of the best experts on this subject based on the ideXlab platform.

  • iaas reserved Contract Procurement optimisation with load prediction
    Future Generation Computer Systems, 2015
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    The increased adoption of cloud computing, combined with the recent proliferation of pricing plans has increased the relevance of automating the complex and time consuming tasks of selecting, procuring and managing cloud resources. In this work, we present an approach to automate the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers. Such reserved Contracts offer the consumer a significant price reduction compared to pay-per-hour pricing models, in exchange for an upfront payment. We present an algorithm that uses load prediction to make cost-efficient purchasing decisions, and evaluate whether the use of automated time series forecasting proves useful in this context. The algorithm takes into account a wide range of Contract types as well as the organisation's current Contract portfolio. We evaluate the effectiveness of different Contract renewal policies and load predictors based on ARIMA, Holt-Winters and exponential smoothing techniques, and compare these with the performance of a simple predictor. We adopt a large set of 51 real-world web application load traces to evaluate the performance and scalability of our algorithm through simulation. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement, but that the use of advanced prediction techniques only proves beneficial in specific cases. The algorithms scalability is shown to be sufficient for its adoption in settings with a large number of Contracts. Introduction of an algorithm for automated IaaS Contract Procurement.Do workload prediction techniques prove valuable w.r.t. acquiring IaaS Contracts?An extensive evaluation using a large set of real-world web traffic workloads.

  • Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

  • IEEE CLOUD - Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

Ruben Van Den Bossche - One of the best experts on this subject based on the ideXlab platform.

  • iaas reserved Contract Procurement optimisation with load prediction
    Future Generation Computer Systems, 2015
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    The increased adoption of cloud computing, combined with the recent proliferation of pricing plans has increased the relevance of automating the complex and time consuming tasks of selecting, procuring and managing cloud resources. In this work, we present an approach to automate the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers. Such reserved Contracts offer the consumer a significant price reduction compared to pay-per-hour pricing models, in exchange for an upfront payment. We present an algorithm that uses load prediction to make cost-efficient purchasing decisions, and evaluate whether the use of automated time series forecasting proves useful in this context. The algorithm takes into account a wide range of Contract types as well as the organisation's current Contract portfolio. We evaluate the effectiveness of different Contract renewal policies and load predictors based on ARIMA, Holt-Winters and exponential smoothing techniques, and compare these with the performance of a simple predictor. We adopt a large set of 51 real-world web application load traces to evaluate the performance and scalability of our algorithm through simulation. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement, but that the use of advanced prediction techniques only proves beneficial in specific cases. The algorithms scalability is shown to be sufficient for its adoption in settings with a large number of Contracts. Introduction of an algorithm for automated IaaS Contract Procurement.Do workload prediction techniques prove valuable w.r.t. acquiring IaaS Contracts?An extensive evaluation using a large set of real-world web traffic workloads.

  • Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

  • IEEE CLOUD - Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

Kurt Vanmechelen - One of the best experts on this subject based on the ideXlab platform.

  • iaas reserved Contract Procurement optimisation with load prediction
    Future Generation Computer Systems, 2015
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    The increased adoption of cloud computing, combined with the recent proliferation of pricing plans has increased the relevance of automating the complex and time consuming tasks of selecting, procuring and managing cloud resources. In this work, we present an approach to automate the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers. Such reserved Contracts offer the consumer a significant price reduction compared to pay-per-hour pricing models, in exchange for an upfront payment. We present an algorithm that uses load prediction to make cost-efficient purchasing decisions, and evaluate whether the use of automated time series forecasting proves useful in this context. The algorithm takes into account a wide range of Contract types as well as the organisation's current Contract portfolio. We evaluate the effectiveness of different Contract renewal policies and load predictors based on ARIMA, Holt-Winters and exponential smoothing techniques, and compare these with the performance of a simple predictor. We adopt a large set of 51 real-world web application load traces to evaluate the performance and scalability of our algorithm through simulation. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement, but that the use of advanced prediction techniques only proves beneficial in specific cases. The algorithms scalability is shown to be sufficient for its adoption in settings with a large number of Contracts. Introduction of an algorithm for automated IaaS Contract Procurement.Do workload prediction techniques prove valuable w.r.t. acquiring IaaS Contracts?An extensive evaluation using a large set of real-world web traffic workloads.

  • Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

  • IEEE CLOUD - Optimizing IaaS Reserved Contract Procurement Using Load Prediction
    2014 IEEE 7th International Conference on Cloud Computing, 2014
    Co-Authors: Ruben Van Den Bossche, Kurt Vanmechelen, Jan Broeckhove
    Abstract:

    With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, Procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the Procurement decision of reserved Contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved Contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of Contract types while taking into account an organization's current Contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary Contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved Contract Procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.

Shouyang Wang - One of the best experts on this subject based on the ideXlab platform.

  • optimal Procurement of long term Contracts in the presence of imperfect spot market
    Omega-international Journal of Management Science, 2015
    Co-Authors: Jinpeng Xu, Gengzhong Feng, Shouyang Wang
    Abstract:

    B2B spot market has grown rapidly and become an effective trading channel for commodity products. Besides long-term Contract Procurement from conventional suppliers (forward and option), a buyer can procure or sell commodities at any time in B2B spot market to adjust her inventory level. However, spot prices are generally volatile and the market is imperfect in the sense that spot trading may be realized with uncertainty in a given period of time and often comes with extra transaction cost. This paper considers a commodity buyer who can order forward and option Contracts in advance and trade in a B2B spot market when spot price and demand are observed stochastically. Based on a single-period newsvendor model, we discuss three optimal order strategies and derive respective expected profits when the buyer is risk-neutral. The sensitivity of purchase costs, market liquidity and transaction cost is investigated. We also compare the optimal expected profits for different strategies to illustrate the effects of the two long-term Contracts in the presence of the B2B spot market. We then extend our model to a multi-period setting and derive the optimal strategy. Finally, we numerically compute the optimal order strategy for a risk-averse buyer and analyze the impact of spot market, risk aversion, as well as the correlation between customer demand and spot price.

Paul R. Kleindorfer - One of the best experts on this subject based on the ideXlab platform.

  • Competitive Options, Supply Contracting, and Electronic Markets
    Management Science, 2005
    Co-Authors: Paul R. Kleindorfer
    Abstract:

    This paper develops a framework for analyzing business-to-business (B2B) transactions and supply chain management based on integrating Contract Procurement markets with spot markets using capacity options and forwards. The framework is motivated by the emergence of B2B exchanges in several industrial sectors to facilitate such integrated Contract and spot Procurement. In the framework developed, a buyer and multiple sellers may either Contract for delivery in advance (the "Contracting" option) or they may buy and sell some or all of their input/output in a spot market. Contract pricing involves both a reservation fee per unit of capacity and an execution fee per unit of output if capacity is called. The key question addressed is the structure of the optimal portfolios of Contracting and spot market transactions for the buyer and these sellers, and the pricing thereof in market equilibrium. Existence and structure of market equilibria are characterized for the associated competitive game between sellers with heterogeneous technologies, under the assumption that they know the buyer's demand function. This allows an explicit characterization of the price of capacity options and the value of managerial flexibility, as well as providing conditions under which B2B exchanges are efficient and sustainable.