A Survey on Spot Pricing in Cloud Computing

Kumar, Dinesh; Baranwal, Gaurav; Raza, Zahid; Vidyarthi, Deo Prakash

doi:10.1007/s10922-017-9444-x

Cited by 49 publications

(33 citation statements)

References 92 publications

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“…It is clear that the slower the devices are, the sharpest these differences become. However, the offloading criteria can consider other aspects such as network usage or money cost of external computing services [26]. In these cases, an indirect relation exists between using outsourcing services and benefits.…”

Section: Related Workmentioning

confidence: 99%

Multilayer Architecture Model for Mobile Cloud Computing Paradigm

et al. 2019

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Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.

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Section: Related Workmentioning

confidence: 99%

Multilayer Architecture Model for Mobile Cloud Computing Paradigm

et al. 2019

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“…9, where the dotted lines from top to down respectively represents the case where x 1 = 200, 400, 600 and 800; the particular results are given by the stars on the same dotted line. The allocation of self-owned instances are determined by the policy (4) or (2). Given a set of jobs, the utilization of self-owned instances under the policy (4) only depends on the parameter β 0 since their allocation is before and independent of the allocation of spot and on-demand instances.…”

Section: Resultsmentioning

confidence: 99%

“…Amazon is the most popular and represents 51.8% of the global market share in 2017; here, two typical purchase options are on-demand and spot instances (i.e., virtual machines). Recently, the issue of cost-effectively utilizing these two types of instances has received significant attention [2].…”

Section: Introductionmentioning

confidence: 99%

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

Loiseau

Hyytiä

2020

IEEE Trans. Parallel Distrib. Syst.

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Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51% when spot and on-demand instances are considered and of up to 43.74% when self-owned instances are considered, compared to previously proposed or intuitive policies.

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Section: Cloud Computing Spot Pricesmentioning

confidence: 99%

“…We aim to contribute to the research of variable-time requests that must not be interrupted, such as MapReduce jobs (Dadashov et al 2014) and other highly parallelized jobs (Kumar et al 2018). Distributed analytics jobs, for example, those using Hadoop or Spark, are particularly suitable for variable-time requests (Kumar et al 2018). Zheng et al (2015) and Tamrakar et al (2017) analyze the execution of MapReduce jobs, with the former concluding that using spot instances from different markets can reduce costs by 93% compared to regular on-demand cloud instances, but can also increase computation time by 15%.…”

Section: Cloud Computing Spot Pricesmentioning

confidence: 99%

Scheduling Flexible Demand in Cloud Computing Spot Markets

et al. 2019

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The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent-a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization.

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A Survey on Spot Pricing in Cloud Computing

Cited by 49 publications

References 92 publications

Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Multilayer Architecture Model for Mobile Cloud Computing Paradigm

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

Scheduling Flexible Demand in Cloud Computing Spot Markets

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