Edge-cloud computing networks integrate dispersed computing resources of edges and clouds through networks, which improves resource utilization by flexibly scheduling tasks to suitable computing nodes. The performance of edge-cloud computing networks depends significantly on the amount of computing resources and the task scheduling scheme. In this work, we propose a novel computing resource trading and task scheduling framework for edge-cloud computing networks with arbitrary network topology. Specifically, we consider a third-party platform which incentivizes computing nodes to share computing resources by designing proper resource pricing mechanisms, and charges customers execution fees by scheduling tasks optimally in the edge-cloud computing network. The platform’s resource pricing and task scheduling optimization problem captures the unique features of edge-cloud computing networks including the heterogeneities of computing resources and tasks, as well as the multi-hop offloading in arbitrary topology, which is challenging to solve. We solve the problem for the homogeneous workload scenario and the heterogeneous workload scenario, respectively. For the homogeneous workload scenario, we propose a multi-round proposer-voter algorithm (MPV) that achieves the global optimum in polynomial time for the non-competitive case. For the heterogeneous workload scenario, we first propose a Gibbs sampling based iterative algorithm (GSI), which updates task scheduling strategies iteratively using Gibbs sampling and converges to the global optimum with high probability. We further propose a distributed alternating update algorithm (DAU), which converges to the local optimum in a distributed manner with linear complexity. Numerical results demonstrate the effectiveness of our proposed resource trading and task scheduling schemes.

Sea ice plays an important role in global climate system. Operation IceBridge (OIB) was launched in 2009 to bridge the gap in polar observations between the Ice, Cloud, and Land Elevation Satellite (ICESat) and ICESat-2 missions. OIB flew more than 1000 aircraft surveys, providing annual mapping of sea ice, glaciers and ice sheets in the Antarctic and Arctic. This study utilizes altimetry data and synchronized optical images from OIB to derive Arctic sea ice freeboard estimates for the period from 2009 to 2019. Lead detection is important in determining sea ice freeboard. We developed an improved approach by combining optical images, OIB laser pulse apparent reflectivity, and L1B elevation profiles to detect leads. We then determined the local sea surface height with a lowest elevation method from the L1B elevation profile of leads. Thus, sea ice freeboard was calculated by the difference between L2 relative elevation and corresponding local sea surface height. We derived the estimates of the Arctic sea ice freeboard from 2009 to 2019. Spatial and temporal variations of the freeboard were analyzed based on the estimates. The Arctic annual average sea ice freeboard showed a decreasing trend from OIB observations. However, an obvious increase in multiyear ice freeboard was observed in 2014. We validated our freeboard estimates with OIB freeboard product from the National Snow and Ice Data Center and coincident ICESat-2 freeboard. The comparison shows that our freeboard estimates generally compare well with the ICESat-2 freeboard, and our method is more sensitive to thin ice.