IBM Predicts Cloud Computing Demand for Sports Tournaments

Aaron K. Baughman (), Richard Bogdany (), Benjie Harrison (), Brian O’Connell (), Herbie Pearthree (), Brandon Frankel (), Cameron McAvoy (), Sandy Sun () and Clay Upton ()
Additional contact information
Aaron K. Baughman: IBM Corporation, Research Triangle Park, North Carolina 27709
Richard Bogdany: IBM Corporation, Research Triangle Park, North Carolina 27709
Benjie Harrison: IBM Corporation, Research Triangle Park, North Carolina 27709
Brian O’Connell: IBM Corporation, Research Triangle Park, North Carolina 27709
Herbie Pearthree: IBM Corporation, Research Triangle Park, North Carolina 27709
Brandon Frankel: IBM Corporation, Research Triangle Park, North Carolina 27709
Cameron McAvoy: IBM Corporation, Research Triangle Park, North Carolina 27709
Sandy Sun: IBM Corporation, Research Triangle Park, North Carolina 27709
Clay Upton: IBM Corporation, Research Triangle Park, North Carolina 27709

Interfaces, 2016, vol. 46, issue 1, 33-48

Abstract: The rapid growth of the Internet and of mobile and other smart technologies has generated increased demand on digital platforms, which are supported by enterprise cloud-computing capabilities. To support IBM’s leadership in analytics, mobile, and cloud technologies, a small team within IBM Global Technology Services (GTS) developed a system that uses advanced analytics to address the dynamic and unpredictable Web traffic patterns produced by a digital-enterprise workload, while driving greater operational efficiencies in computing and labor resources. Current cloud platforms are reactive; that is, they require human intervention to scale computing resources to meet demand. To address this shortcoming, the GTS team developed the Predictive Cloud Computing (PCC) system. PCC uses multiple advanced analytical techniques, such as novel numerical analysis techniques, discrete-event simulation, and advanced forecasting to produce models that forecast Internet traffic demands in near real time, allocating computing resources as needed. In 2014, GTS applied the PCC system across tennis and golf sporting tournaments reducing our cloud-computing hours by about 50 percent, while driving a reduction in labor through automation. The PCC system continues to expand IBM’s technology base; since its inception, it has resulted in 16 patent filings, strengthening IBM’s analytics patent portfolio and overall brand.

Keywords: predictive modeling; forecasting; cloud computing; big data; sports; stream computing; social analytics (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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