Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Freeze-thawing erosion is mainly distributed in the tundra, which is one of the main factors affecting soil erosion and soil conservation and affects the economic development of relevant countries and regions. The study area was selected to the north of Tanggula Mountain and the south of Bayankera Mountain, to the east of The Qinghai-Tibet Plateau, as the headwaters of the Yangtze River and lancang River. The topography and climate were particularly prone to soil freeze-thawing erosion, and the ecological damage would seriously affect the production and life of people in the whole downstream area. Therefore, based on the analytic hierarchy process (AHP), this paper selects seven evaluation factors to analyze the temporal and spatial characteristics of freeze-thaw erosion in the study area and establishes a comprehensive weight evaluation model for freeze-thaw erosion. The results show that: (1) the evaluation model is effective, and the soil freeze-thawing erosion is strong in the whole research area; (2) the total area of the research area and the freeze-thawing erosion area are 418,843 km2 and 375,514 km2 respectively, the freeze-thawing erosion area accounting for 89.7% of the total research area, and the freeze-thawing erosion intensity ranged from 0.165 to 0.737; (3) the spatial distribution differs significantly, the freeze-thawing erosion intensity is mainly concentrated in high altitude areas, especially in the Tanggula Mountains; (4) slope, poor annual temperature, illumination, altitude and content of sand in soil accelerate soil freeze-thawing erosion, whereas vegetation index does not; wetness index enhanced the influence of vegetation coverage and sand content. (5) this research will provide scientific evidence for protection and restoration of ecological environment in the area.

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The Gravity Recovery and Climate Experiment (GRACE) mission has measured total water storage change (TWSC) and interpreted drought patterns in an unparalleled way since 2002. Nevertheless, there are few sources that can be used to understand drought patterns prior to the GRACE era. In this study, we extended the gridded GRACE TWSC to 1993 by combining principal component analysis (PCA), least square (LS) fitting, and multiple linear regression (MLR) methods using climate variables as input drivers. We used the extended (climate-driven) TWSC to interpret drought patterns (1993–2019) over the Amazon basin. Results showed that, in the Amazon area with the resolution of 0.5°, GRACE, GRACE follow on, and Swarm had correlation coefficients of 0.95, 0.92, and 0.77 compared with climate-driven TWSCS, respectively. The drought patterns assessed by the climate-driven TWSC were consistent with those interpreted by the Palmer Drought Severity Index and GRACE TWSC. We also found that the 1998 and 2016 drought events in the Amazon, both induced by strong El Niño events, showed similar drought patterns. This study provides a new perspective for interpreting long-term drought patterns prior to the GRACE period.

Today's information systems of enterprises are incredibly complex and typically composed of a large number of participants. Running logs are a valuable source of information about the actual execution of the distributed information systems. In this paper, a top-down process mining approach is proposed to construct the structural model for a complex workflow from its multi-source and heterogeneous logs collected from its distributed environment. The discovered top-level process model is represented by an extended Petri net with abstract transitions while the obtained bottom-level process models are represented using classical Petri nets. The Petri net refinement operation is used to integrate these models (both top-level and bottom-level ones) to an integrated one for the whole complex workflow. A multi-modal transportation business process is used as a typical case to display the proposed approach. By evaluating the discovered process model in terms of different quality metrics, we argue that the proposed approach is readily applicable for real-life business scenario. INDEX TERMS Workflow models, multi-source running log, distributed process mining, petri nets, refinement operation.

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