Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications

Relationships Among Dimensions of Information System Success and Benefits of Cloud

Despite the many benefits offered by cloud computing’s design architecture, there are many fundamental performance challenges for IT managers to manage cloud infrastructures to meet business expectations effectively. Grounded in the information systems success model, the purpose of this quantitative correlational study was to evaluate the relationships among the perception of information quality, perception of system quality, perception of service quality, perception of system use, perception of user satisfaction, and net benefits of cloud computing services. The participants (n = 137) were IT cloud services managers in the United States, who completed the DeLone and McLean ISS authors’ validated survey instrument. The multiple regression finding were signification, F(5, 131) = 85.16, p \u3c .001, R2 = 0.76. In the final model, perception of information quality (β = .188, t = 2.844, p \u3c .05), perception of service quality (β = .178, t = 2.102, p \u3c .05), and perception of user satisfaction (β = .379, t = 5.024, p \u3c .001) were statistically significant; perception of system quality and perception of system use were not statistically significant. A recommendation is for IT managers to implement comprehensive customer evaluation of the cloud service(s) to meet customer expectations and afford satisfaction. The implications for positive social change include decision-makers in healthcare, human services, social services, and other critical service organizations better understand the vital predictors of attitude toward system use and user satisfaction of customer-facing cloud-based applications