Concepts and requirements for a cloud-based optimization service

Kurschl, Werner; Pimminger, Sebastian; Wagner, Stefan; Heinzelreiter, Johann

doi:10.1109/apcase.2014.6924464

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Looking to the future, various EA research directions and emerging technologies are already influencing their implementation in computer software. For example, the ubiquity of cloud computing has fostered the emergence of optimization as a service (OaaS) as a practical on-demand tool for decision makers to apply EAs to solve previously intractable problems (Broad et al, 2015a;Kurschl et al, 2014). Rapid and agile OaaS implementation is facilitated by portable and interpreted web-ready languages that support reflection and introspection (e.g.…”

Section: How Do We Implement Eas?mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

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Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best tradeoffs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single-and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.

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Section: How Do We Implement Eas?mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

View full text Add to dashboard Cite

show abstract

“…Similarly, Li et al [12] propose cloud-based framework for simulation-based OaaS to enable a flexible and highly parallelizable dynamic decision support for environments that require optimization driven by large amounts of dynamic data. An analysis of several research initiatives and the requirements in the context of OaaS for mathematical and simulation-based optimization can be found in [13]. Works in the literature also address the optimization of services that are executed in cloud environments, leading to service OaaS, which in practice aim to organize, (re)allocate VMs and deploy services on VM cloud infrastructure [14].…”

Section: A Oaas Exploiting Cloud Computing Infrastructurementioning

confidence: 99%

Network Optimization as a Service with Net2Plan

Garrich¹,

Martinez²,

Moreno-Muro³

et al. 2019

2019 European Conference on Networks and Communications (EuCNC)

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Carrier-grade telecommunication infrastructure must support an ever-increasing traffic volume and dynamicity in operationally-complex networks characterized by multiple domains, multiple technologies and equipment from multiple vendors. At the same time, the advent of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies create transport ecosystems with unprecedented network control and resource dynamicity capabilities. In this context, network optimization is essential to efficiently employ network resources, relying whenever possible on automated procedures and exploiting open application programming interfaces (APIs) inherent of SDN/NFV ecosystems. Automated network optimization procedures on top of network controllers promote a client layer of third-party applications that enable optimization-as-a-service (OaaS) business opportunities. In this paper, we present a network OaaS framework built as an extension of the Net2Plan open-source network planning tool. The proposed framework is based on a classical client-server architecture, and through a REST-based API, holders of network infrastructures (i.e. client role) may request the resolution of a network resource allocation problem through the execution of an algorithmic technique. Thus, third-party players can focus on the development of optimization algorithms (i.e. server role) while potentially providing high-performance computing (HPC) capabilities for their execution. Through the paper, we detail the workflow of the proposed OaaS, illustrate its usage with several use-cases and discuss its applicability in different scenarios.

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Concluding Remarks and Outlook

Kallrath

2021

International Series in Operations Research &Amp; Management Science

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Concepts and requirements for a cloud-based optimization service

Cited by 4 publications

References 34 publications

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Network Optimization as a Service with Net2Plan

Concluding Remarks and Outlook

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