Current real-time application systems demand complex functionality and are increasingly coming to be implemented upon multiprocessor platforms, with complex synchronization, data-sharing and parallelism requirements. However, the formal models for representing real-time workloads have traditionally been designed in the context of uniprocessor environments; hence, they are often not able to accurately represent relevant features of multiprocessor realtime systems. Researchers have recently addressed this shortcoming by applying workload models from Divisible Load Theory (DLT) to real-time systems. The resulting theory, referred to as Real-time Divisible Load Theory (RT-DLT), holds great promise for modeling an emergent class of massively parallel real-time workloads. However, the theory needs strong formal foundations before it can be widely used for the design and analysis of real-time systems. In this paper, we briefly describe our current findings on RT-DLT and ongoing research efforts at extending this work to develop such formal foundations. © 2009 IEEE.

We extend the current state of the art in real-time divisible load theory (RT-DLT), by considering the problems of scheduling a real-time divisible job on computing clusters in which different processing nodes have different computing capabilities, as well as different costs associated with executing on them. We seek to minimize the cost of executing a job while also meeting its deadline. © 2011 IEEE.

© 2014 IEEE.Data security and privacy have become the biggest concern in cloud computing due to the fact that data can be compromised if cloud service storage is being attacked or during cloud service breakdown. Therefore, the adoption of data partitioning in multi cloud environment to address this issue has attracted academia and researchers’ interest. Hence, this paper will discuss the concept of data partitioning, the current state-of-the-art of data partitioning and secured data partitioning in multi cloud environment. We then compared the current security approaches used to secure data partitioning in multi cloud environment. Lastly, we conclude that pure data partitioning alone is insufficient to address data security and privacy problem. Therefore, we will extend this issue for our future work.