A Framework for Allocating Server Time to Spot and On-Demand Services in Cloud Computing

Abstract

Cloud computing delivers value to users by facilitating their access to servers at any time period needed. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different time periods on servers. The latter allows users to bid for the remaining unoccupied time periods via dynamic pricing; however, without appropriate design, such time periods may be arbitrarily short since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this article, we provide the first integral framework for sharing time on servers between on-demand and spot services while optimally pricing spot service. It guarantees that on-demand users can get served quickly while spot users can stably use servers for a properly long period once accepted, which is a key feature in making both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 461.5%. The framework is designed under assumptions that are met in real environments. It is a new tool that other cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, making the case for eventually integrating this service model into their own infrastructures.

iThermoFog: IoT‐Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques

“…6 To solve this problem, a novel approach is required that is quickly able to adapt to dynamic task characteristics and host resource utilization in a heterogeneous environment that can not only keep temperature under control but also reduce energy, latency and bandwidth usage. To this end, we propose an Artificial Intelligence (AI) based automatic scheduling technique that uses the thermal-profile characteristics, 5,7 to control the cooling facilities and maintain the CDC temperatures 8 using an integrated Internet of Things (IoT) 1 and Fog computing 9 environment. Specifically, our method creates the thermal-aware profile of the CDC using a Gaussian Mixture Model applied to past thermal measurements to predict and schedule tasks in order to minimize CDC temperature.…”

Toward Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning

“…1. Furthermore, tenants can bid a price for spot instances and spot prices are updated at regular time slots (e.g., every L = 5 minutes in Amazon) [28], [29]. Spot instances are assigned to a job and continue running if the spot price is lower than the bid price.…”

Towards Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning

“…1. Furthermore, tenants can bid a price for spot instances and spot prices are updated at regular time slots (e.g., every L = 5 minutes in Amazon) [28], [29]. Spot instances are assigned to a job and continue running if the spot price is lower than the bid price.…”

“…To date, multiple service and pricing models have been proposed [33], [35] and the spot and on-demand service model is a major service offering [32], [34], [36]. Jain et al are the first to enable the application of an online learning approach to infer the cost-effective parametric policy for utilizing spot and on-demand instances [9], [10].…”

“…On-demand and spot instances are two typical purchase options [36]. On-demand instances are always available at a fixed price once requested by users.…”