Since a large number of irregular task sets have low resource requirements and high parallelism， the use of Graphics Processing Unit （GPU） to accelerate processing is the current mainstream. However， the existing irregular task scheduling strategies either use an exclusive GPU approach or use the traditional optimization methods to map tasks to GPU devices. The former leads to the idleness of GPU resources， and the latter cannot make maximum use of GPU computing resources. Based on the analysis of existing problems， an idea of multi-knapsack optimization was adopted to enable more irregular tasks to share GPU equipment in the best way. Firstly， according to the characteristics of GPU clusters， a distributed GPU job scheduling framework consisting of schedulers and executions was given. Then， with GPU memory as the cost， an Extended-grained Greedy Scheduling （EGS） algorithm based on GPU computing resources was designed. In the algorithm， as many irregular tasks as possible were scheduled on multiple available GPUs to maximize the use of GPU computing resources， and the problem of idle GPU resources was solved. Finally， the actual benchmark programs were used to randomly generate a target task set to verify the effectiveness of the proposed scheduling strategy. Experimental results show that， compared with the traditional greedy algorithm， the Minimum Completion Time （MCT） algorithm and the Min-min algorithm， when the number of tasks is equal to 1 000，the execution time of EGS algorithm is reduced to 58%， 64% and 80% of the original ones on average respectively， and the proposed algorithm can effectively improve the GPU resource utilization.

The exponential growth in the number of wireless mobile devices leads that heterogeneous cooperative Small Base Stations (SBS) carry large-scale traffic load. Aiming at this problem, an Online-hot Video Cache Replacement Policy (OVCRP) based on cooperative SBS and popularity prediction was proposed. Firstly, the changes of popularity in short term of online-hot videos were analyzed, then a k-nearest neighbor model was constructed to predict the popularities of the online-hot videos, and finally the locations for cache replacement of online-hot videos were determined. In order to select appropriate locations to cache the online-hot videos, with minimization of overall transmission delay as the goal, a mathematical model was built and an integer programming optimization algorithm was designed. The simulation experiment results show that compared with the schemes such as RANDOM cache (RANDOM), Least Recently Used (LRU) and Least Frequently Used (LFU), the proposed OVCRP has obvious advantages in average cache hit rate and average access delay, reducing the network burden of cooperative SBS.

A construction of topology-transparent Media Access Control (MAC) scheduling code for mobile Ad Hoc network supporting multi-class services was proposed.The code length was minimized under the condition that one successful transmission slot was guaranteed for each user in lowest priority class.The proposed code has much flexibility in choosing the transmission slots and its construction is simple.The performance analysis shows that the wireless network employing the proposed MAC scheduling code can support users with multi-class services and provide them guaranteed Quality of Service (QoS).

Based on the analysis of the basic principle and characteristics of RFID, the existing RFID indoor location-sensing system LANDMARC was discussed in detail. Then, an enhanced nearest neighbor positioning algorithm was proposed based on multi-level error processing. Simulation results show that the enhanced nearest neighbor algorithm can achieve better positioning accuracy than the existing nearest neighbor algorithms.