Cost-Aware Cloud Provisioning

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

doi:10.1109/escience.2015.67

Cited by 40 publications

(22 citation statements)

References 19 publications

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“…It should be noted that the specialized GPU enhanced cluster instances used by our service are only offered in two Amazon regions (only one in the US) which removes the need for complex provisioning approaches at present. While limiting execution to a single region may affect cost and performance, in related work we have developed cost-aware provisioning techniques that could be used to further reduce costs [31].…”

Section: Costmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

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We describe a cloud-based medical image reconstruction service designed to meet a real-time and daily demand to reconstruct thousands of images from proton cancer treatment facilities worldwide. Rapid reconstruction of a three-dimensional Proton Computed Tomography (pCT) image can require the transfer of 100GB of data and use of approximately 120 GPU-enabled compute nodes. The nature of proton therapy means that demand for such a service is sporadic and comes from potentially hundreds of clients worldwide. We thus explore the use of a commercial cloud as a scalable and cost-efficient platform for pCT reconstruction. To address the high performance requirements of this application we leverage Amazon Web Services' GPU-enabled cluster resources that are provisioned with high performance networks between nodes. To support episodic demand, we develop an on-demand multi-user provisioning service that can dynamically provision and resize clusters based on image reconstruction requirements, priorities, and wait times. We compare the performance of our pCT reconstruction service running on commercial cloud resources with that of the same application on dedicated local high performance computing resources. We show that we can achieve scalable and on-demand reconstruction of large scale pCT images for simultaneous multi-client requests, processing images in less than ten minutes for less than $10 per image.

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Section: Costmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

Self Cite

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“…Therefore, the task execution order can be arranged in many ways. Then, the resource allocation phase aims to choose the number and type of VMs to execute tasks [13]. In this phase, the number of VMs in the cloud is regarded as unlimited: a task can run on only one VM and a VM can run only one task at a time.…”

Section: Related Workmentioning

confidence: 99%

“…Notably, although this approach does not take into account the data transmission time between different VMs, it can still be considered as an approximate alternative. Similarly, the latter process is to assign all tasks on the same VM with the slowest speed, and Task-VM and VM-Type are set to 0 (lines [12][13][14]. These two individuals account for only one-third of the total population in order to achieve better optimization performance.…”

Section: Population Initializationmentioning

confidence: 99%

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gao

et al. 2019

J Wireless Com Network

122

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Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

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“…Trying to overcome cloud shortcomings describe an elastic provisioning system that improves costs and processing time. This solution still needs adjustments to serve a multiclient situation.…”

Section: Related Workmentioning

confidence: 99%

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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Cost-Aware Cloud Provisioning

Cited by 40 publications

References 19 publications

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Scalable pCT Image Reconstruction Delivered as a Cloud Service

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

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