Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Joint system consumption minimization and workload allocation in edge-core network

The explosive increase of smart devices and mobile traffic results intolerable network latency and degraded service to the end-users. in heavy burden on backhaul and core network, As a complement to core network, edge network contributes to relieving network burden and improving user experience. To investigate the problem of optimizing the total consumption in an edge-core network, the system consumption minimization problem is fromulated, considering the energy consumption and delay. Given that the formulated problem is a mixed nonlinear integer programming （MNIP） , a low-complexity workload allocation algorithm is proposed based on interior-point method. The proposed algorithm has an extremely short running time in practice. Finally, simulation results show that edge network can significantly complement core network with much reduced backhaul energy consumption and delay.

Allocating workload to minimize the power consumption of data centers

Reducing the power consumption has become one of the most important challenges in designing modem data centers due to the explosive growth of data. The tradi- tional approaches employed to decrease the power consump- tion normally do not consider the power of IT devices and the power of cooling system simultaneously. In contrast to existing works, this paper proposes a power model which can minimize the overall power consumption of data centers by balancing the computing power and cooling power. Fur- thermore, an enhanced genetic algorithm （EGA） is designed to explore the solution space of the power model since the model is a liner programming problem. However, EGA is computing intensive and the performance gradually decreases with the growth of the problem size. Therefore, heuristic greedy sequence （HGS） is proposed to simplify the calcu- lation by leveraging the nature of greed. In contrast to EGA, HGS can determine the workload allocation of a specific data center layout with only one calculation. Experimental results demonstrate that both the EGA and HGS can significantly reduce the power consumption of data centers in contrast to the random algorithm. Additionally, HGS significantly out- performs EGA in terms of the continuity of workload alloca- tion and execution performance.

Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers

The development of cloud computing has accel-erated the worldwide growth of internet data centers(IDCs).While a large portion of the energy consumption generated by intense computation introduces greater operation expenditures to the IDC enterprises.To manage the overall costs and utilize resources to their fullest extent,this paper introduces the concept of spatio-temporal workload allocation among the geographically distributed IDCs within a cloud,with the guarantee of the workload completion time and the consideration of computing service delay penalties by introducing the cost of inconvenience.Apart from the effort of the workload migration,the spatio-temporal variance of the renewable energies in the data center microgrids(DMGs)is fully considered in this paper.What's more,as the power consumed by the IDCs are primarily converted into heat,the waste heat recovery process is embedded in each IDC to demonstrate the effectiveness of the repurposed heat,which can be used by the residential heating demand in the thermal system,for total cost reduction and energy usage efficiency in the whole operating system.Applying real-life data traces of the electricity price,renewable energies and heating demand,these extensive evaluations demonstrate that both spatial and temporal complementary attempts on the supply side and demand side,along with power and thermal complementary efforts,can significantly reduce the overall cost for the IDC enterprise.