Mobile grid computing broadens the scope of grid computing by including a vast resource pool available in the form of mobile devices. As the number of mobile devices in use is bound to increase in coming years, and with enhanced features, mobile grid computing will have a lot to offer in the area of high performance computing. This paper surveys the paradigm of mobile grid computing and the fea...

An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves energy spent transmitting and processing large data volumes distant cloud-deployed servers. However, due to memory computing limitations, often cannot support required resource-intensive fail accurately execute tasks. In this work, we address prob...

In Mobile Edge Computing, edge servers have limited storage and computing resources which can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting multiple dependent tasks, modeled as Directed Acyclic Graph (DAG). When request arrives, typically in an online manner with deadline specified, we need to configure the assign tasks for effici...

Mobile devices such as smart mobile phones, personal digital assistants (PDAs) and other handheld computing devices have become very popular. The early findings about the use of mobile devices and mobile systems show that the mobile computing environment differs from the traditional desktop environment in significant ways. Several areas of computing, related to mobile devices, have surfaced ove...

Due to the advancement in mobile devices and wireless networks mobile cloud computing, which combines mobile computing and cloud computing has gained momentum since 2009. The characteristics of mobile devices and wireless network makes the implementation of mobile cloud computing more complicated than for fixed clouds. This section lists some of the major issues in Mobile Cloud Computing. One o...