Automatic VM Allocation for Scientific Application

Pumma, Sarunya; Achalakul, Tiranee; Li, Xiaorong

doi:10.1109/icpads.2012.135

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…However, efficient energy management remains a challenge. While cloud computing provides virtually unlimited storage, computing and networking (Yang et al 2011a;Ji et al 2012;Yang, Xu, and Nebert 2013), obtaining optimal cost and high efficiency are elusive (Pumma, Achalakul, and Li 2012). Research to address this issue is emerging from different facets, including cloud cost modeling (Gui et al 2014), resource auto-and intelligent-scaling (Röme 2010;Jam et al 2013;Xia et al 2015b), location-aware smart job scheduling and workflow (Mao and Humphrey 2011;Lorido-Botrán, Miguel-Alonso, and Lozano 2012;Li et al 2015b;Gui et al 2016) and hybrid cloud solutions (Shen et al 2011;Bicer, Chiu, and Agrawal 2012;Xu 2012).…”

Section: Energy Efficiency and Cost Managementmentioning

confidence: 99%

“…The volume and velocity challenges of Big Data require VM creation on-demand. Autonomous detection of the velocity for provisioning VMs is critical (Baughman et al 2015) and should consider both optimal cost and high efficiency in task execution (Pumma, Achalakul, and Li 2012). Research is being conducted to understand the applications and relevant Big Data changing patterns to form a comprehensive model to predict system behavior as the usage patterns evolve and working loads change (Castiglione et al 2014).…”

Section: On-demand Resource Provisionmentioning

confidence: 99%

“…Research is being conducted to understand the applications and relevant Big Data changing patterns to form a comprehensive model to predict system behavior as the usage patterns evolve and working loads change (Castiglione et al 2014). For example, Pumma, Achalakul, and Li (2012) proposed an automatic mechanism to allocate the optimal numbers of resources in the cloud, and Zhang et al (2015b) proposed a task-level adaptive MapReduce framework for scaling up and down as well as effectively using computing resources. Additionally, many workload prediction and resource-allocation models and algorithms provide or improve auto-scaling and auto-provisioning capability.…”

Section: On-demand Resource Provisionmentioning

confidence: 99%

“…. Developing a comprehensive cost model and relevant automatic scheduling technologies to provide on-demand and cost-efficiency solutions for a variety of applications, including epidemic (Pumma, Achalakul, and Li 2012) and dust storm forecasting (Huang et al 2013a); . Developing benchmarks or criteria for modeling the quality, performance, security and integrity (Kos et al 2015); .…”

Section: );mentioning

confidence: 99%

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Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

646

289

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Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers-Big Data and cloud computingand reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

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Section: Energy Efficiency and Cost Managementmentioning

confidence: 99%

Section: On-demand Resource Provisionmentioning

confidence: 99%

Section: On-demand Resource Provisionmentioning

confidence: 99%

Section: );mentioning

confidence: 99%

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Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

646

289

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“…They presented the fact that selecting appropriate techniques and cloud resources for analyzing big data is a major research problem. Working towards this direction, Pumma, Achalakul, and Li argued that suitable amount of cloud resources should be determined prior to the start of execution. Consequently, the field of predicting and provisioning cloud resources gained momentum.…”

Section: Preliminariesmentioning

confidence: 99%

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

Kaur

Sood

2017

Int J Network Mgmt

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Summary Various Internet‐based applications such as social media, business transactions, mobile applications, cyber‐physical systems, and Internet of Things have led to the generation of big data streams in every field. The growing need to extract knowledge from big data streams has pioneered the challenge of selecting appropriate cloud resources. The current techniques allocate resources based on data characteristics. But because of the stochastic nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Working towards this direction, this paper proposes a system that predicts the data characteristics in terms of volume, velocity, variety, variability, and veracity. The predicted values are expressed in a quadruple called Characteristics of Big data (CoBa). Thereafter, the proposed system uses self‐organizing maps to dynamically create clusters of cloud resources. One of these clusters is allocated to the big data stream based on its CoBa. The proposed system is dynamic in the sense that it changes the cloud cluster allocated to big data stream if its CoBa changes. Experimental results show that the proposed system has a performance edge over other streaming data processing tools such as Storm, Flume, and S4.

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