A Novel Model for Optimizing Selection of Cloud Instance Types

Liu, Wenqiang; Wang, Pengwei; Meng, Ying; Zhao, Qin; Zhao, Caihui; Zhang, Zhaohui

doi:10.1109/access.2019.2937511

Cited by 11 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method proposed in this paper is applicable to the spot instance price prediction of other cloud providers. Helping users to select appropriate instances [9][10][11] based on price prediction and combining cloud data storage [12] with cloud instance types selection are two directions for the future work.…”

Section: Discussionmentioning

confidence: 99%

“…The purpose is to help users to understand the price characteristics of spot instances, and then design a reasonable bidding scheme and a combination of instance selection solutions. Low monetary cost is one of the most important metrics and driving forces for users to using cloud services and hosting their data into cloud, which has been widely studied in cloud instance selection [9][10][11], cloud storage [12][13][14], and scientific workflow scheduling [15,16]. Therefore, it is important to understand and predict the prices of cloud instances, especially the spot instance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cloud spot instance price prediction using kNN regression

Liu

Wang

Meng

et al. 2020

Hum. Cent. Comput. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

With the amount of data rapidly increasing, many applications need higher-performance hardware to support their running. However, for most individuals and organizations, these hardware are too expensive, and their budgets are limited. Consequently, the cloud computing price scheme based on pay-as-you-go is a very suitable choice for users to migrate their applications to the cloud, and using cloud can reduce the cost of purchasing and maintaining hardware. There are three major service models for cloud computing: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) [1]. Amazon Elastic Compute Cloud (EC2) [2] is one of the representatives of IaaS, which can provide users with basic hardware resources, such as CPU, network, memory and storage. Amazon EC2 provides users with many purchase options. They can be divided into three categories: reserved instances, on-demand instances and spot instances [2]. Reserved instances allow users to purchase a long-term right to use instances at a lower price, which are cost-effective for long-term applications. On-demand instances cost more than reserved instances, however users can purchase it according to the actual

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cloud spot instance price prediction using kNN regression

Liu

Wang

Meng

et al. 2020

Hum. Cent. Comput. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the point of users' cost, the selection of CSP is a research direction in cloud computing. In our previous work, we proposed some approaches for optimizing selection of cloud instance types [25][26][27]. Actually, the price of the same functional storage service provided by the same CSP in different regions is different.…”

Section: Heterogeneous Cloudmentioning

confidence: 99%

An Adaptive Data Placement Architecture in Multicloud Environments

Wang

Zhao

Wei

et al. 2020

Scientific Programming

Self Cite

View full text Add to dashboard Cite

Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.

show abstract

“…The Service broker generally acts as an entity between the service consumer and service provider to perform various functions and the most critical function of matching the service request with the provider [1]. There are various cloud brokers as revealed in the literature [1], [14], [15], [17], [30], [32], [38], [41], [42]. These brokers are generally constrained to the cloud environment and lack focus on the specified integrated environment.…”

Section: Introductionmentioning

confidence: 99%

Global Manager – A Service Broker In An Integrated Cloud Computing, Edge Computing & IoT Environment

2022

KSII TIIS

View full text Add to dashboard Cite

The emergence of technologies like Big data analytics, Industrial Internet of Things, Internet of Things, and applicability of these technologies in various domains leads to increased demand in the underlying execution environment. The demand may be for compute, storage, and network resources. These demands cannot be effectively catered by the conventional cloud environment, which requires an integrated environment. The task of finding an appropriate service provider is tedious for a service consumer as the number of service providers drastically increases and the services provided are heterogeneous in the specification. A service broker is essential to find the service provider for varying service consumer requests. Also, the service broker should be smart enough to make the service providers best fit for consumer requests, ensuring that both service consumer and provider are mutually beneficial. A service broker in an integrated environment named Global Manager is proposed in the paper, which can find an appropriate service provider for every varying service consumer request. The proposed Global Manager is capable of identification of parameters for service negotiation with the service providers thereby making the providers the best fit to the maximum possible extent for every consumer request. The paper describes the architecture of the proposed Global Manager, workflow through the proposed algorithms followed by the pilot implementation with sample datasets retrieved from literature and synthetic data. The experimental results are presented with a few of the future work to be carried out to make the Manager more sustainable and serviceable.

show abstract

A Novel Model for Optimizing Selection of Cloud Instance Types

Cited by 11 publications

References 38 publications

Cloud spot instance price prediction using kNN regression

Cloud spot instance price prediction using kNN regression

An Adaptive Data Placement Architecture in Multicloud Environments

Global Manager – A Service Broker In An Integrated Cloud Computing, Edge Computing & IoT Environment

Contact Info

Product

Resources

About