IBM Predicts Cloud Computing Demand for Sports Tournaments

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.

Big Data Analytics in Operations Management

“…Expectedly, more OM studies would consider this strategy and factor in their analysis in the presence of big data. Finally, note that for most real -world applications of big data analytics (such as the IBM project reported by Baughman et al (2016)), they adopt a multi-methodological approach (Choi et al 2016) in their strategy to cope with the big data challenge. This provides an alternative reason to explain why researchers should consider multiple methods in conducting OM research in the big data era.…”

“…Finally, note that for most real ‐world applications of big data analytics (such as the IBM project reported by Baughman et al. (2016)), they adopt a multi‐methodological approach (Choi et al. 2016) in their strategy to cope with the big data challenge.…”

Research in Operations Management and Information Systems Interface

“…In a recent study, Choi et al (2018) present multiple use cases and examples of applications of machine learning and AI techniques in OM research. The examples range from applications in forecasting (Baughman et al 2016, Ferreira et al 2016, Liu et al 2016, inventory management (Huang and Van Mieghem 2014), detecting review manipulations ), risk analysis, revenue management, and marketing and supply chain management. These examples and studies provide a good foundation and framework for future research in this domain.…”

Nurse’s Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses

“…© 2021 Sigma Theta Tau International infrared sensors, and GPS could be used to track traffic patterns and design emission models (Zhu, Yu, Wang, Ning, & Tang, 2019), and IBM was able to forecast web traffic patterns in near real time (Baughman et al, 2016). Researchers were able to predict patient acuity scores for the next day based heavily on textual nursing notes (Konito et al, 2014).…”

“…The science of big data allows researchers to compare and analyze large volumes of data from multiple sources and in multiple formats to develop a better understanding of relationships and patterns among data that are useful for the prediction and estimation of future trends and needs. Data from video cameras, infrared sensors, and GPS could be used to track traffic patterns and design emission models (Zhu, Yu, Wang, Ning, & Tang, 2019), and IBM was able to forecast web traffic patterns in near real time (Baughman et al, 2016). Researchers were able to predict patient acuity scores for the next day based heavily on textual nursing notes (Konito et al, 2014).…”