Cost-Aware Elastic Cloud Provisioning for Scientific Workloads

Chard, Ryan; Chard, Kyle; Bubendorfer, Kris; Łaciński, Łukasz; Madduri, Ravi; Foster, Ian

doi:10.1109/cloud.2015.130

Cited by 27 publications

(23 citation statements)

References 10 publications

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“…Chard et al [30] employ spot resources to achieve cost savings. An iterative process repeatedly bids for spot VMs to execute jobs.…”

Section: Public Clouds With Spot Vmsmentioning

confidence: 99%

“…Researchers [24,32,46] have addressed the problem of performance maximisation with a budget constraint with the objective of obtaining with the maximum performance within a budgetary constraint. Chard et al aimed to help users to acquire the desired amount of resources with the minimum cost [30]. Finally, Thai et al [23] defined a requirement, which focused on making a trade-off between performance and cost.…”

Section: Literature Analysismentioning

confidence: 99%

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A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Thai

Varghese

Barker

2018

Future Generation Computer Systems

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Cloud computing has been widely adopted due to the flexibility in resource provisioning and on-demand pricing models. Entire clusters of Virtual Machines (VMs) can be dynamically provisioned to meet the computational demands of users. However, from a user's perspective, it is still challenging to utilise cloud resources efficiently. This is because an overwhelmingly wide variety of resource types with different prices and significant performance variations are available. This paper presents a survey and taxonomy of existing research in optimising the execution of Bag-of-Task applications on cloud resources. A BoT application consists of multiple independent tasks, each of which can be executed by a VM in any order; these applications are widely used by both the scientific communities and commercial organisations. The objectives of this survey are as follows: (i) to provide the reader with a concise understanding of existing research on optimising the execution of BoT applications on the cloud, (ii) to define a taxonomy that categorises current frameworks to compare and contrast them, and (iii) to present current trends and future research directions in the area.

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“…Chard et al [30] employ spot resources to achieve cost savings. An iterative process repeatedly bids for spot VMs to execute jobs.…”

Section: Public Clouds With Spot Vmsmentioning

confidence: 99%

Section: Literature Analysismentioning

confidence: 99%

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Thai

Varghese

Barker

2018

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Other characterisation approaches aim to predict workload resource utilisation, by identifying a feature of the workload. The authors in [80] match applications with appropriate VM types by defining application profiles, which are manually extracted from workflow logs. The authors in [77] classify tasks based on resource utilisation and the authors in [81,82] extract utilisation usage signatures.…”

Section: Open Research Challengesmentioning

confidence: 99%

Adaptation in cloud resource configuration: a survey

2016

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With increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads; the new capability can be used to serve new requests or alternatively unused resources may be turned off to reduce power consumption. The goal of this paper is to highlight features, approaches and findings in the literature, in order to identify open challenges and facilitate future developments. We present a definition of cloud systems adaptation, a classification of the key features and a survey of adapting compute and storage configuration. Based on our analysis, we identify three open research challenges: characterising the workload type, accurate online profiling of workloads, and building highly scalable adaptation mechanisms.

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“…Even small provisioning inefficiencies, such as failure to meet workflow dependencies on time or selecting the wrong resources for a task, can result in significant monetary costs [22,135]. Provisioning the right amount of storage and compute resources leads to decisive cost reduction with no substantial impact on application performance.…”

Section: Introductionmentioning

confidence: 99%

“…These requirements are associated to the continuously increasing power of computing and storage resources that in many cases are required on-demand for specific phases of an experiment, therefore demanding elastic scaling. This motivates the utilization of clouds by scientific researchers as an alternative to using in-house resources [22].…”

Section: Introductionmentioning

confidence: 99%

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

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Cost-Aware Elastic Cloud Provisioning for Scientific Workloads

Cited by 27 publications

References 10 publications

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Adaptation in cloud resource configuration: a survey

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

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