Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.

SLA-driven container consolidation with usage prediction for green cloud computing

Since service level agreement(SLA)is essentially used to maintain reliable quality of service between cloud providers and clients in cloud environment,there has been a growing effort in reducing power consumption while complying with the SLA by maximizing physical machine(PM)-level utilization and load balancing techniques in infrastructure as a service.However,with the recent introduction of container as a service by cloud providers,containers are increasingly popular and will become the major deployment model in the cloud environment and specifically in platform as a service.Therefore,reducing power consumption while complying with the SLA at virtual machine(VM)-level becomes essential.In this context,we exploit a container consolidation scheme with usage prediction to achieve the above objectives.To obtain a reliable characterization of overutilized and underutilized PMs,our scheme jointly exploits the current and predicted CPU utilization based on local history of the considered PMs in the process of the container consolidation.We demonstrate our solution through simulations on real workloads.The experimental results show that the container consolidation scheme with usage prediction reduces the power consumption,number of container migrations,and average number of active VMs while complying with the SLA.