Predictive Cloud Computing with Big Data: Professional Golf and Tennis Forecasting [Application Notes]

Baughman, Aaron K.; Bogdany, Richard J.; McAvoy, Cameron; Locke, Richard; O'Connell, Brian; Upton, Clay

doi:10.1109/mci.2015.2437551

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…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%

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: On-demand Resource Provisionmentioning

confidence: 99%

Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

646

289

View full text Add to dashboard Cite

show abstract

“…Baughman et al propose a novel forecasting and predictive model which can be used for the analysis of Big Data streams to cover the velocity dimension of Big Data stream [29]. In comparison with BCframework, there is no data, query and scheduling model proposed in their research.…”

Section: Streaming Big Data Schedulingmentioning

confidence: 99%

Efficient deadline-aware scheduling for the analysis of Big Data streams in public Cloud

Mortazavi-Dehkordi

Zamanifar

2019

Cluster Comput

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The emergence of Big Data has had a profound impact on how data are analyzed. Open source distributed stream processing platforms have gained popularity for analyzing streaming Big Data as they provide low latency required for streaming Big Data applications using Cloud resources. However, existing resource schedulers are still lacking the efficiency and deadline meeting that Big Data analytical applications require. Recent works have already considered streaming Big Data characteristics to improve the efficiency and the likelihood of deadline meeting for scheduling in the platforms. Nevertheless, they have not taken into account the specific attributes of analytical application, public Cloud utilization cost and delays caused by performance degradation of leasing public Cloud resources. This study, therefore, presents BCframework, an efficient deadline-aware scheduling framework used by streaming Big Data analysis applications based on public Cloud resources. BCframework proposes a scheduling model which considers public Cloud utilization cost, performance variation, deadline meeting and latency reduction requirements of streaming Big Data analytical applications. Furthermore, it introduces two operator scheduling algorithms based on both a novel partitioning algorithm and an operator replication method. BCframework is highly adaptable to the fluctuation of streaming Big Data and the performance degradation of public Cloud resources. Experiments with the benchmark and real-world queries show that BCframework can significantly reduce the latency and utilization cost and also minimize deadline violations and provisioned virtual machine instances.

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“…In 2015, Zhang, Chen, and Yang argued that data volume plays a vital role in efficient selection of cloud resources for big data streams. On the other hand, Baughman et al illustrated that predicting velocity of incoming big data request is crucial for efficient provisioning of cloud resources. Furthermore, Castiglione et al emphasized that variation in data flow rate (variability) affects cloud resource allocation.…”

Section: Introductionmentioning

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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Predictive Cloud Computing with Big Data: Professional Golf and Tennis Forecasting [Application Notes]

Cited by 12 publications

References 25 publications

Big Data and cloud computing: innovation opportunities and challenges

Big Data and cloud computing: innovation opportunities and challenges

Efficient deadline-aware scheduling for the analysis of Big Data streams in public Cloud

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

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