Optimal Cloud Resource Selection Method Considering Hard and Soft Constraints and Multiple Conflicting Objectives

Powell, Courtney; Miura, Katsunori; Munetomo, Masaharu

doi:10.1109/cloud.2018.00115

Cited by 7 publications

(5 citation statements)

References 7 publications

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Section: Related Workmentioning

confidence: 99%

“…Jin et al 23 propose a model that considers demand of a cloud instance and cost to recommend instances. Powell et al 24 propose a model that obtains user constraints such as geographic location, latency, response time, and instance family and then uses an equivalent transformation to make recommendations to the user. Grandhi et al 25 propose a fuzzy decision making approach that considers predefined quality‐of‐service (QoS) attributes from industry experts to determine suitable Cloud instances for applications.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

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This article proposes a random-forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud-ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single-precision (SP) floating-point operations (FLOPs), double-precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud-ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application-to-Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application-specific random forests (ARF) and application-class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application-class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real-world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Addressing the issue of service selection in mobile edge computing environment, Wu et al [8] put forward a selection scheme minimizing the response time, whose arithmetic design integrated genetic and simulated annealing algorithm. Powell et al [9] conducted research on service selection in the case study of cloud resource configurations, structuring the set Table I SUMMARY OF NOTATIONS Notation Definition n, N, N index, number, set of multiple service requests (users) i, M, M index, number, set of third-party service providers C i set of candidate services offered by provider i j index of service from the candidate set C i Sn set of service providers for service request n authorized to select service from N i set of service requests which are authorized service access by the service provider i x n i,j whether the service request n elect the j th candidate service in the candidate set C i (=1) or not (=0) Θ solution space formed by all of variables…”

Section: A Service Selection Based On Single Service Requestmentioning

confidence: 99%

Section: A Service Selection Based On Single Service Requestmentioning

confidence: 99%

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FASS: A Fairness-Aware Approach for Concurrent Service Selection with Constraints

Huang

Cheng

et al. 2019

2019 IEEE International Conference on Web Services (ICWS)

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The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and service providers. Despite many existing works have extensively handled the issue of service selection, it remains an open question in the case where requests from multiple users are performed simultaneously by a certain set of shared candidate services. Meanwhile, there exist some constraints enforced on the context of service selection, e.g. service placement location and contracts between users and service providers. In this paper, we focus on the QoS-aware service selection with constraints from a fairness aspect, with the objective of achieving max-min fairness across multiple service requests sharing candidate service sets. To be more specific, we study the problem of fairly selecting services from shared candidate sets while service providers are self-motivated to offer better services with higher QoS values. We formulate this problem as a lexicographical maximization problem, which is far from trivial to deal with practically due to its inherently multiobjective and discrete nature. A fairness-aware algorithm for concurrent service selection (FASS) is proposed, whose basic idea is to iteratively solve the single-objective subproblems by transforming them into linear programming problems. Experimental results based on real-world datasets also validate the effectiveness and practicality of our proposed approach.

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DDBJ Database updates and computational infrastructure enhancement

Ogasawara

Kodama

Mashima

et al. 2019

Nucleic Acids Research

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The Bioinformation and DDBJ Center (https://www.ddbj.nig.ac.jp) in the National Institute of Genetics (NIG) maintains a primary nucleotide sequence database as a member of the International Nucleotide Sequence Database Collaboration (INSDC) in partnership with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The NIG operates the NIG supercomputer as a computational basis for the construction of DDBJ databases and as a large-scale computational resource for Japanese biologists and medical researchers. In order to accommodate the rapidly growing amount of deoxyribonucleic acid (DNA) nucleotide sequence data, NIG replaced its supercomputer system, which is designed for big data analysis of genome data, in early 2019. The new system is equipped with 30 PB of DNA data archiving storage; large-scale parallel distributed file systems (13.8 PB in total) and 1.1 PFLOPS computation nodes and graphics processing units (GPUs). Moreover, as a starting point of developing multi-cloud infrastructure of bioinformatics, we have also installed an automatic file transfer system that allows users to prevent data lock-in and to achieve cost/performance balance by exploiting the most suitable environment from among the supercomputer and public clouds for different workloads.

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Optimal Cloud Resource Selection Method Considering Hard and Soft Constraints and Multiple Conflicting Objectives

Cited by 7 publications

References 7 publications

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

FASS: A Fairness-Aware Approach for Concurrent Service Selection with Constraints

DDBJ Database updates and computational infrastructure enhancement

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