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Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. fuzzy theory to analyze resource performance, preprocess the resources, and achieve the integration and classification of a large number of resources, then we improve the HEFT list sort and complete the task scheduling according to their priority. PROBLEM DESCRIPTIONS Cloud computingCloud computing is hinting at a future in which we would not compute on local computers but on centralized facilities operated by third-party compute and storage utilities [5]. Computing pioneer John McCarthy predicted that 'computation may someday be organized as a public utility', and went on to speculate how this might occur [3,4]. There is little consensus on how to define the cloud computing, many computing researchers and practitioners have attempted to define clouds in various ways.Buyya et al.[22] thought that cloud computing was a new and promising paradigm delivering services as computing utilities. Clouds were designed to provide services to external users, providers needed to be compensated for sharing their resources and capabilities. Wang L.Z et al.[23] defined the science cloud computing system, they pointed out that cloud computing could not only provide the hardware service HaaS (Hardware as a service), software service SaaS (software as a service) and data resource service DaaS (Data as a service), but also could provide the platform service PaaS (platform as a service) to the user. So the users would submit their own on-demand to the computing platform about the hardware configuration, software installation, data access requirements, and so on. With the increasing demand for process automation in the cloud, there is a requirement to create ...

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To solve the problems of a poor manual garbage sorting environment, including heavy tasks and low sorting efficiency, we propose the Lightweight Feature Fusion Single Shot Multibox Detector (L-SSD) algorithm to realize intelligent trash classification and recognition. Since waste has a small volume and the image resolution of garbage is always low, the algorithm that we propose is an enhanced single shot multibox detector (SSD) with a lightweight and novel feature fusion module. This SSD can significantly improve the performance of rubbish detection. In this feature fusion module, features from different layers with different scales are connected in series. A new feature pyramid was generated by using downsampling blocks, which will be fed to appointed multibox detectors to predict the final detection results. Due to the extremely unbalanced ratio of positive samples to negative samples, which leads to a low accuracy of SSD, Focal Loss using balanced cross-entropy is employed, which is provided by easy examples that corresponds to difficult samples with a decline in the loss weight. Thus, the training is biased towards meaningful samples. We have replaced the backbone network of VGG16 with ResNet-101 to achieve more accurate detection. We analyzed the performance of a nonmaximum suppression (NMS) algorithm and discovered that Soft-NMS was more suitable for learning better image representations. The strategy of Soft-NMS is to suppress the undesirable detection box rather than remove it completely. The experimental results show that the L-SSD exceeds a large number of state-of-the-art object detection algorithms in both accuracy and speed.