Qiang Fan

Energy Driven Avatar Migration in Green Cloudlet Networks

Fully utilizing green energy can remarkably decrease the operational cost of cloudlet providers in provisioning green cloudlet networks (GCNs), which are powered by both green and brown energy. Owing to the spatial and temporal dynamics of energy demands and green energy generation, migrating Avatars (i.e., virtual machines) from green energy deprived cloudlets into green energy over-provisioned cloudlets can reduce the total on-grid energy consumption of GCN. However, Avatar migration itself consumes non-negligible energy consumption. In this letter, we propose the Energy driven AvataR migration (EARN) scheme to reduce the total on-grid energy consumption of GCN by considering the energy consumption of Avatar migrations. The performance of EARN is demonstrated by extensive simulations.

Green energy aware user association in heterogeneous networks

Greening information and communications technology is becoming an environmental and economic sine qua non, and has attracted much research attention. For a cellular network, the base stations (BSs) cost more than 50% of the energy consumption of the whole network. Therefore, BSs can be powered by green energy to reduce its on-grid power consumption. In this paper, we propose a greeN Energy Aware user associaTion (NEAT) scheme in the two-tier green heterogeneous network, that enables a BS depleting of green energy to offload its traffic load to other BSs with excessive green energy. Since the Macro BS (MBS) and Pico BS (PBS) employ different partitions of the licensed spectrum, we also consider the bandwidth allocation and adjust the two spectrum partitions dynamically. However, in the NEAT scheme, achieving the optimal user association in terms of minimizing the on-grid power consumption of BSs, is NP-hard. Therefore, we propose a heuristic NEAT algorithm to approximate the optimal solution with low computational complexity. Finally, the performance and viability of the algorithm are substantiated by simulation results.

Cost Aware cloudlet Placement for big data processing at the edge

As accessing computing resources from the remote cloud for big data processing inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of networks, can potentially mitigate this problem. Although load offloading in cloudlet networks has been proposed, placing the cloudlets to minimize the deployment cost of cloudlet providers and E2E delay of user requests has not been addressed so far. The locations and number of cloudlets and their servers have a crucial impact on both the deployment cost and E2E delay of user requests. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing strategy (CAPABLE) to optimize the tradeoff between the deployment cost and E2E delay. When cloudlets are already placed in the network, we also design a load allocation scheme to minimize the E2E delay of user requests by assigning the workload of each region to the suitable cloudlets. The performance of CAPABLE is demonstrated by extensive simulation results.

Throughput aware and green energy aware user association in heterogeneous networks

Greening information and communications technology is becoming an environmental and economic sine qua non, and has attracted much research attention. For a cellular network, base stations (BSs) incur more than 50% of the energy consumption of the whole network. Therefore, BSs can be powered by green energy to reduce its on-grid power consumption. Meanwhile, the throughput has always been a critical issue in cellular networks. Since the throughput and energy consumption mutually affect each other, saving on-grid power is at the cost of sacrificing a certain amount of throughput. In this paper, we propose a Throughput Aware and Green Energy aware user association (TAGE) scheme in heterogeneous cellular networks (HCNs) to optimize the trade-off between the throughput and on-grid power consumption. Meanwhile, we employ an energy-throughput coefficient α to control the energy-throughput tradeoff. The simulation results verify that TAGE improves the effective throughput and saves a significant amount of on-grid power for HCNs.