Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

An adaptive dynamic feedback load balancing algorithm based on QoS in distributed file system

An adaptive dynamic load balancing algorithm based on QoS is proposed to improve the performance of load balancing in distributed file system,combining the advantages of a variety of load balancing algorithms.The new algorithm uses a tuple containing the number of files and the total file size as the QoS measure for the requested task.The master node sets a threshold for the requested task based on the QoS to filter storage nodes that meet the requirements of the task.In order to guarantee the reliability of the new algorithm,we consider the impact of CPU utilization,memory usage,disk IO occupancy rate,network bandwidth usage and hard disk usage on load balancing performance when calculating the real-time load balancing of storage nodes.The heterogeneity of the network is considered when the master node schedule task assignments to ensure the fairness of the algorithm.The comprehensive evaluation value is determined based the performance load ratio,which is calculated from the real-time load value of the storage node and a performance value after normalization.The master node assigns tasks to the storage node with the highest comprehensive evaluation value.The storage nodes provide adaptive feedback based on changes in the degree of connectivity,rather than periodic update of the load information.The actual distributed file system environment is set up on the server cluster,the performance of the new algorithm is tested through a contrast experiment.The experimental results show that the new algorithm can effectively reduce the average response time of the system,improve throughput,and enable the system load to reach a good balance.