Cloud computing has been widely adopted by many companies and government entities. To ensure high quality computing resource provisioning, cloud platforms should offer smart resource management solutions. An important step toward better resource management is to accurately predict the workloads of the applications running on the cloud. Many existing workload prediction methods are regression based, which require the workloads of the applications show clear seasonality and trend. However, it is difficult to use these methods for tasks which may not have such recurring workload patterns. From careful analysis of the workloads in a real-world cloud, we found that many tasks have busty workloads that are very difficult to predict using regression-based prediction. Instead, we consider a job-pool based approach, where the knowledge about the workloads of a large pool of tasks is used to help predict the workloads of new tasks. In particular, we develop a clustering-based learning approach to realize the job-pool based concept. The pool of jobs are clustered based on their workloads, and a neuralnet is used to learn the characteristics of the workloads in each cluster. When a new job arrives, we use its initial workload pattern and submission parameters to find the cluster it belongs to. Then, the corresponding neuralnet is used to predict the workload of the new job far into the future. Based on this predicted long-term workload, smart resource management decisions can be made to reduce the potential overhead in scaling and migration. We also consider a non-clustering based learning solution and compare it with the clustering-based learning solution. Experimental results show that the clustering-based learning approach can predict the workload more accurately.

Understanding a power wheelchair users mobility characteristics is critical because mobility is an important factor for social participation and quality of life of an individual. Although power wheelchairs can improve the mobility for people with disabilities, research has shown that power wheelchair users tend to live an inactive lifestyle. A sedentary lifestyle exposes wheelchair users to a greater risk of secondary health issues, such as cardiovascular diseases, obesity, diabetes, etc. Therefore, it is critical to assess wheelchair users mobility to ensure that they maintain an active lifestyle. However, existing health tracking applications are not suitable for power wheelchair users. They either require sensors to be installed on the wheels of a wheelchair (hence bringing installation and maintenance burdens) or are designed for able individuals by detecting the users steps, whose characteristics are significantly different from the dynamics of a power wheelchair. Furthermore, data captured by the inertial sensors (e.g., accelerometer or gyroscope) demonstrates a wide variety of patterns owing to different terrains on which the wheelchair travels. In this study, we propose to use the accelerometer in a smartphone for data collection, and employ mathematics and physics techniques to process and transform the raw data so that patterns intrinsic to wheelchair maneuvers are revealed. Based on the processed data, we developed a learning-based approach to analyze wheelchair users mobility by leveraging such patterns. We have conducted a sequence of experiments to evaluate the proposed approach. Experimental results showed that our approach correctly recognized all the bouts (segments of continuous movement), and achieved accurate measurements on bout maneuvering time and maximum period of continuous movement, which are critical indicators of a wheelchair users mobility.