High-performance time-series quantitative retrieval from satellite images on a GPU cluster

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.N/

Optimizing execution path of scientific workflow by gradual removal of QoS constraint violations in reverse order

A service-based scientific workflow can be exposed as a composite service that consists of a set of logically connected sub-services. A critical issue in this area is how to achieve overall optimized end-to-end QoS requirements by effectively coordinating individual QoS constraints of single service. Unfortunately, this issue has not been well addressed. In this paper, we propose a Reverse Order-based approach to gradually remove QoS Constraint violations for building an optimized path to execute a scientific workflow. With our approach, an initial execution path for a scientific workflow is first built by employing the local optimization policy without considering user-defined end-to-end QoS constraints. Based on this path, global QoS computing models can be used to calculate the global QoS values for each quality attribute. Then, QoS constraint violations can be detected by comparing global QoS values with end-to-end user- defined QoS constraints. For each violation, a Reverse Order-based correction algorithm by gradually removing QoS constraint violations is proposed to recursively correct it by reselecting critical service execution instances. As a result, an optimized execution path can be rebuilt to meet overall end-to-end QoS requirements. Comparison and simulation further demonstrate the feasibility and performance of our approach

A More Accurate Field-to-Field Method towards the Wind Retrieval of HY-2B Scatterometer

In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B

F2F-NN: A Field-to-Field Wind Speed Retrieval Method of Microwave Radiometer Data Based on Deep Learning

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously

Building quick service query list (QSQL) to support automated service discovery for scientific workflow

Scientific workflow is emerging as a promising scientific computing paradigm to offer the convenience for the scientists to resolve complex scientific problems. To successfully execute a scientific workflow, the workflow creation by depending on service discovery techniques should be made in the first place. Particularly, semantics have been proposed as a key to automatically solve service discovery issue for facilitating users to create a workflow. However, most of the semantic service discovery methods still rat a low-efficiency stage because they generally involve a large number of ontology reasoning that is often time consuming. To address this issue, we present an efficient service discovery method by building Quick Service Query list (QSQL) to support automated service discovery for creating a workflow. QSQL based on graph storage theory is an efficient service index list that is dynamically built by service publication algorithm. In QSQL, semantic relationships between the published services and all related ontology concepts can be processed in advance so that a large number of ontology reasoning can be avoided during service discovery. Further, our proposed discovery algorithm can efficiently select service models from QSQL to match a user query. The final experiments further demonstrate the feasibility and the efficiency of our proposed method

A reverse order-based QoS constraint correction approach for optimizing execution path for service composition

In service oriented computing systems, a business process can be exposed as a composite service which consists of a set of logically connected sub-services. For each service in the composition, many service providers can offer the same function but may different QoS. In the general service composition, when a user submits a request, overall QoS constraints called end-to-end QoS composition's requirements, for example, time should be less than one hour, and cost should be less than 60$, can be transmitted at the same time. As such, how to effectively coordinate individual QoS constraints for single service to achieve the best overall QoS benefits without violating such end-to-end QoS constraint requirements has been a critical issue. With an increasing number of abstract services in a service composition, the possibility of execution path by selecting different service providers for each abstract service blows up exponentially. Therefore, service selection problem for service composition is a computational-hard problem, which can be regarded as a Multiple choice Multiple dimension Knapsack Problem (MMKP) that has been proved np-hard [1, 2, 3]. Recently, a lot of approaches such as graph-based techniques[4], runtime adaptation-based techniques[5], Service Level Agreement(SLA), negotiation and auction based techniques[6], Integer Linear Programming (ILP) based techniques[7] have been proposed to resolve overall QoS constraints for optimizing execution path in a service composition. No matter what the merits and the importance current existing methods have, they rely on directly judging constraint conditions to detect multiple paths for picking out a critical execution path, which easily produces a high-time complexity and even an unsatisfactory result in comparison to the best path. As such, the issue on resolving overall QoS constraints to achieve an optimal execution path has not yet been well addressed