In retail management, the continuous monitoring of shelves to keep track of the availability of the products and following proper layout are the two important factors that boost the sales and improve customer’s level of satisfaction. The studies conducted earlier were either performing shelf monitoring or verifying planogram compliance. As both the activities are important, to tackle this problem, we presented a deep learning and computer vision-based hybrid approach called Hyb-SMPC that deals with both activities. The Hyb-SMPC approach consists of two modules: The first module detects fine-grained retail products using one-stage deep learning detector. For the detection part, the comparison of three deep learning-based detectors, You Only Look Once (YOLO V4), YOLO V5, and You Only Learn One Representation (YOLOR), is provided and the one giving the best result will be selected. The selected detector will perform detection of different categories of SKUs and racks. The second module performs planogram compliance; for this purpose, the company-provided layout is first converted to JavaScript Object Notation (JSON) and then the matching is performed with the postprocessed retail images. The compliance reports will be generated at the end for indicating the level of compliance. The approach is tested in both quantitative and qualitative manners. The quantitative analysis demonstrates that the proposed approach achieved an accuracy up to 99% on the provided dataset of retail, whereas the qualitative evaluation indicates increase in sales and customers’ satisfaction level.

Software-defined storage (SDS) is an abrupt technology that aims to address the explosive data proliferation and storage management complexity through the separation of control and data path, which are tightly bounded in the traditional storage area network (SAN) model. In SDS, the centralized controller manages and controls the overall storage services and operations. However, the centralized controller approach raises the scalability and single point of failure flag. In such a paradigm, the controller placement is an important activity. A random placement of controllers suffers from unpredictable delay in a large enterprise. Multiple SDS controllers mitigate the single point of failure and provide redundancy. We propose a method called controller placement based on the center of gravity (CPCG) to solve the optimal placement controllers’ locations to reduce the delay. CPCG incorporates different coordinates and analyzes their density (the number of attached devices to a switch) to suggest new optimal sites. For the least number of SDS controllers, we propose an areawise optimal placement location strategy; the areawise approach mitigates workload processing from the central controller and also provides a scalable and reliable storage system. Experiments show that the optimal placement drastically reduces delay and, hence, boosts scalability.