Resource Management Framework for Distributed Heterogeneous Systems

Abstract: In distributed heterogeneous computing environments, such as in-home entertainment networks and mobile computing systems, independently developed applications share common resources, e.g., CPU or network bandwidth. The resource demands coming from different applications are usually highly fluctuating over time. For example, video processing results in both stochastic fluctuations, caused by different coding techniques for video frames, and structural fluctuations, due to scene changes. Similarly, wireless networks applications are exposed to long-term bandwidth variations caused by other application in the system that are using the same wireless network simultaneously, and short-term oscillations due to radio frequency interference, like microwave ovens or cordless phones. Still, applications in such open, dynamic and heterogeneous environments are expected to maintain required performance levels.In this thesis, we look into solutions for efficient transport of video streams with acceptable playout quality in home networks, which requires management of both networks and CPUs. We propose a framework for efficient resource management for streaming in heterogeneous system, called the Matrix. The Matrix is based on a global abstraction of device states, which reduces system state information and decreases overheads for its determination and dissemination. It provides access to the entire system state in acceptable fresh way, enabling system wide optimized decisions to be taken.Moreover, we use the Matrix framework as the platform to develop a method for an efficient Quality-of-Service (QoS) provision and adaptation in dynamic, heterogeneous systems. QoS adaptation is one of the crucial operations to maximize overall system quality as perceived by the user while still satisfying individual application demands. It integrates local QoS mechanisms of the involved devices that deal mostly with short-term resource fluctuations, with a global adaptation mechanism that handles structural and long-term load variations on the system level. We have illustrated the effectiveness of our QoS adaptation approach in the context of video streaming. However, we do not see any limitation to expand the usage of our approach to the health sector, or some other community social/industrial applications. Resource management and QoS adaptation are required whenever we are surrounded with heterogeneous, mobile, and dynamic environment.