C.O.D.E.

Since the publication of the seminal paper by Liu and Layland in 1973, the problem of real-time task scheduling under different resource models has been studied intensively. However, most of the studies rely on a strong assumption that the computing resource's performance does not change during its lifetime. Unfortunately, for many long-standing real-time applications, such as data acquisition systems (DAQ), deep-space exploration programs and SCADA systems for power, water and other national infrastructures, the performance of computational resources decrease notably after a long and continuous execution period. Due to ever increased complexity of computer system, software aging issues become more difficult, if not impossible, to eradicate. Hence, the assumption that computing resource has a constant performance in its entire lifetime does not hold in real world long-standing systems.

Currently we study real-time task schedulability issues under a resource model that the resource's performance degrades with a known degradation function and the resource is periodically rejuvenated.

Let me tell you a story.

Cloud computing relies on sharing of resources to achieve coherence and economies of scale similar to a utility (like the electricity grid) over a network. At the foundation of cloud computing is the broader concept of converged infrastructure and shared services.
The cloud also focuses on maximizing the effectiveness of the shared resources. Cloud resources are usually not only shared by multiple users but as well as dynamically re-allocated as per demand. This can work for allocating resources to users in different time zones. For example, a cloud computer facility which serves European users during European business hours with a specific application (eg. email) while the same resources are getting reallocated and serve North American users during North America's business hours with another application (eg. web server). This approach should maximize the use of computing powers thus reducing environmental damage as well, since less power, air conditioning, rackspace, and so on, is required for the same functions.

Our focus: Cloud structure, Virtualization, Cloud scheduling.