Application-centric Resource Provisioning for Amazon EC2 Spot Instances

In late 2009, Amazon introduced spot instances to offer their unused resources at lower cost with reduced reliability. Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. The spot price changes periodically based on supply and demand, and customers whose bids exceed it gain access to the available spot instances. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However the reliability is compromised since the instances(IaaS) providing the service(SaaS) may become unavailable at any time without any notice to the customer. Checkpointing and migration schemes are of great use to cope with such situation. In this paper we study various checkpointing schemes that can be used with spot instances. Also we device some algorithms for checkpointing scheme on top of application-centric resource provisioning framework that increase the reliability while reducing the cost significantly

Simple Pricing Schemes for the Cloud

The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.Comment: To appear in the 13th Conference on Web and Internet Economics (WINE), 2017. A preliminary version was presented at the 12th Workshop on the Economics of Networks, Systems and Computation (NetEcon), 201

Energy-Aware Cloud Management through Progressive SLA Specification

Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed VM may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energy-aware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel SLA specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc

Price forecasting for spot instances in Cloud computing

[EN] Big data applications usually need to rent a large number of virtual machines from Cloud computing providers. As a result of the policies employed by Cloud providers, the prices of spot virtual machine instances behavior stochastically. Spot prices (prices of spot instances) fluctuate greatly or have multiple regimes. Choosing virtual machines according to trends in prices is helpful in decreasing the resource rental cost. Existing price prediction methods are unable to accurately predict prices in these environments. As a result, a dynamic-ARIMA and two markov regime-switching autoregressive model based forecasting methods have been developed in this paper. Experimental results show that the proposals are better than the existing MonthAR in most scenarios. (C) 2017 Elsevier B.V. All rights reserved.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and No. 61572127), the Natural Science Foundation of Jiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DPI2015-65895-R) financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2018). Price forecasting for spot instances in Cloud computing. Future Generation Computer Systems. 79:38-53. https://doi.org/10.1016/j.future.2017.09.038S38537

Roads, lands, markets, and deforestation : a spatial model of land use in Belize

Rural roads promote economic development but also facilitate deforestation. To explore the tradeoffs between development and environmental damage posed by road building, the authors develop and estimate a spatially explicit model of land use. This model takes into account location and land characteristics and predicts land use at each point on the landscape. They find that: (a) market access and distance to roads strongly affect the probability of agricultural use, especially for commercial agriculture; (b) high slopes, poor drainage, and low soil fertility discourage both commercial and semi subsistence agriculture; and (c) semi-subsistence agriculture is especially sensitive to soil acidity and lack nitrogen (confirming anthropological findings that subsistence farmers are shrewd judges of soil). Spatially explicit models are analytically powerful because they exploit rich spatial variation in causal variables, including the precise siting of roads. They are useful for policy because they can pinpoint threats to particular critical habitats and watersheds. This model is a descendant of the venerable von Thunen model. It assumes that land will tend to be devoted to its highest-value use, taking into account tenure and other constraints. The value of a plot for a particular use depends on the land's physical productivity for that use and the farmgate prices of relevant inputs and outputs. A reduced-form, multinomial logit specification of this model calculates implicit values of land in alternative uses as a function of land location and characteristics. The resulting equations can then be used for prediction or analysis. The model was applied to cross-sectional data for 1989-92 for Belize, a forested country currently experiencing rapid expansion of both subsistence and commercial agriculture. A geographic information system was used to manage the spatial data and extract variables based on the three kilometer sample grid. Three land uses were distinguished:"natural"vegetation, comprising forests, woodlands, wetlands, and savanna; semi-subsistence agriculture, comprising traditional milpa (slash-and-burn) cultivation and other nonmechanized cultivation of annual crops; and commercial agriculture, consisting mainly of sugarcane, pasture, citrus, and mechanized production of corn and kidney beans. Two dimensions of distance to market were distinguished: the distance from each sample point to the road, and on-road travel time to the nearest town. Data on a wide variety of land and soil characteristics were also used.Wetlands,Water Conservation,Environmental Economics&Policies,Climate Change,Land Use and Policies,Forestry,Environmental Economics&Policies,Climate Change,Energy and Environment,Wetlands