The 3D applications, until recently restricted to the desktops and workstations, are expanding into the mobile platforms, such as cellular phones and PDAs. Similar to the desktop, the consumers will expect high-quality 3D experience, and this is a big challenge. Handheld devices have slower processors that are less capable of computing large workloads, and the batteries have limited lifetimes, so for large and complex workload, we need an excellent power management policy for saving power. Besides, although mobile platforms have lower resolution than desktop, each pixel must still be rendered since the screen is closed to the observer¡¦s eye, or we will see some imperfections. For the reasons above, we make a point of performance optimization and power saving, and these rely on accuracy and fast workload estimation. We refer to some workload estimation methods which researchers have mentioned before, such as UW1, UW5, PID[8], Frame Structure[9], Signature Table[1], and hybrid power management policy[10].UW1 and UW5 both use the previous workload as the estimation workload. PID uses the feedback loop to correct the estimation workload. Frame Structure classifies frames into several structures, and sums the workload of each structure up as the estimation workload. Signature Table stores some 3D parameters in the table, and when a new frame comes in, the 3D parameters of this frame will compare with the table, if match, we use the workload in the table as the estimation workload. Our method is a hybrid policy of UW1 and UW5, and we will decide to use UW1 or UW5 when a new frame comes in. Finally we will compare the performance of each power management policy.

<div>The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of spatial data in real-time with high-throughput and low response time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, the real-time streamed spatial data and query follow non-uniform spatial distributions that are continuously changing over time. Therefore, Distributed spatial streaming systems need to track the changes in the distribution of spatial data and queries and redistribute their workload accordingly. This thesis addresses the challenges of adapting to workload changes in distributed spatial streaming systems to improve the performance while preserving the system's security. </div><div>The thesis proposes TrioStat, an online workload estimation technique that relies on a probabilistic model for estimating the cost of partitions and machines of distributed spatial streaming systems. TrioStat has a decentralised technique to collect and maintain the required statistics in real-time with minimal overhead. In addition, this thesis introduces SWARM, a light-weight adaptive load-balancing protocol that continuously monitors the data and query workloads across the distributed processes of spatial data streaming systems, and redistribute the workloads soon as performance bottlenecks get detected. SWARM uses TrioStat to estimate the workload of the system's machines. Although using adaptive load-balancing techniques significantly improves the performance of distributed streaming systems, they make the system vulnerable to attacks. In this thesis, we introduce a novel attack model that targets adaptive load-balancing mechanisms of distributed streaming systems. The attack reduces the throughput and the availability of the system by making it stay in a continuous state of rebalancing. The thesis proposes Guard, a component that detects and blocks attacks that target the adaptive load balancing of distributed streaming systems. Guard is deployed in SWARM to develop an attack-resilient adaptive load balancing mechanism for Distributed spatial streaming systems.<br></div>