An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Galante, Guilherme; Bona, Luis C. E.; Mury, Antonio Roberto; Schulze, Bruno; Righi, Rodrigo Rosa

doi:10.1007/s10723-016-9361-3

Cited by 53 publications

(25 citation statements)

References 81 publications

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“…Elasticity is one of the main characteristics of cloud computing, which means that computing resources can be increased or decreased in a dynamic and on demand way according to the current workload of applications [17]. Current solutions direct use of the auto-scaling groups to spawn VMs on which a dedicated VM is used to run Spark master daemons and other VMs are used to run Spark worker daemons.…”

Section: Discussionmentioning

confidence: 99%

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Huang

Zhang

et al. 2017

IJGI

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Abstract:Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

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Section: Discussionmentioning

confidence: 99%

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Huang

Zhang

et al. 2017

IJGI

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“…Different time series techniques such as Support Vector Machine, Exponential Smoothing, and Neural Networks have been used in predicting future workload [13,14,17,19,20]. Although, functional dependencies between application's services are very effective factors in predicting future workload, most of current proactive techniques do not consider it.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Elasticity of SaaS Applications using Queuing Theory

Shahin¹

2017

ijacsa

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Abstract-Elasticity is one of key features of cloud computing. Elasticity allows Software as a Service (SaaS) applications' provider to reduce cost of running applications. In large SaaS applications that are developed using service-oriented architecture model, each service is deployed in a separated virtual machine and may use one or more services to complete its task. Although, scaling service independently from its required services propagates scaling problem to other services, most of current elasticity approaches do not consider functional dependencies between services, which increases the probability of violating service level agreement. In this paper, architecture of SaaS application is modeled as multi-class M/M/m processor sharing queuing model with deadline to take into account functional dependencies between services during estimating required scaling resources. Experimental results show effectiveness of the proposed model in estimating required resources during scaling virtual resources.

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“…Scientific computing is the key to solve the great challenges in many domains and has provided advances in diverse fields of science [1]. Scientific computing has been dependent on High Performance Computing (HPC) and parallel processing, since running large simulation requires a huge amount of computing resources.…”

Section: Introductionmentioning

confidence: 99%

“…Some academic and commercial HPC users are looking at clouds as a cost effective alternative to dedicated HPC clusters [1]. Renting rather than owning a cluster avoids the up-front and operating expenses associated with a dedicated infrastructure [4].…”

Section: Introductionmentioning

confidence: 99%

Build and Evaluate a Free Virtual Cluster on Amazon Elastic Compute Cloud for Scientific Computing

2017

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Abstract-Scientific computing requires a huge amount of computing resources, but not all the scientific researchers have an access to sufficient highend computing systems. Currently, Amazon provides a free tier account for cloud computing which could be used to build a virtual cluster. In order to investigate whether it is suitable for scientific computing, we first describe how to build a free virtual cluster using StarCluster on Amazon Elastic Compute Cloud (EC2). Then, we perform a comprehensive performance evaluation of the virtual cluster built before. The results show that a free virtual cluster is easily built on Amazon EC2 and it is suitable for the basic scientific computing. It is especially valuable for scientific researchers, who do not have any HPC or cluster, to develop and test their prototype system of scientific computing without paying anything, and move it to a higher performance virtual cluster when necessary by choosing more powerful instance on Amazon EC2.

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An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Cited by 53 publications

References 81 publications

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Enhancing Elasticity of SaaS Applications using Queuing Theory

Build and Evaluate a Free Virtual Cluster on Amazon Elastic Compute Cloud for Scientific Computing

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