Summary Cloud multi‐tenancy has a variant requirement, due to its resource sharing nature, satisfying such requirements and maintaining a balance between the resources and the business workloads of multiple tenants is a challenging task, and also the communication between scheduled containers leads to high power consumption. To address these issues, this article proposes an autonomic approach to ensure the elasticity of BPM in multi‐tenant cloud. Where it employs the autonomic computing capabilities for scheduling the containers into the available servers then regulates the communication between the containers using multi‐path routing. For the container scheduling, a multi‐objective crow search optimization algorithm is proposed to schedule the containers into appropriate servers. Then, the discrete wolf search algorithm based multipath routing is proposed to route the communication flows between the containers by finding the optimal path with an objective to minimize the energy consumption. The optimal path is constructed as a multi‐tenancy graph with bandwidths determining the shortest distance between the servers and containers. The overall simulations shows that the proposed algorithm outperformed the other compared approaches in terms of make‐span, resource utilization, execution cost, execution time, and energy consumption.

Summary Automated techniques for brain tumor classification using deep learning approaches have gained significant research interest in recent years. Yet, the difficulties in extracting and classifying the tumor regions from the 3D Magnetic Resonance Imaging (MRI) do not have a definite solution. The major challenge in utilizing machine and deep learning algorithms for brain cancer classification from 3D images is the time complexity in analyzing the multiple frames of a brain MRI. This paper introduces Hyper‐Layer Convolutional Neural Networks (HL‐CNN) and Hyper‐Heuristic Extreme Learning Machine (HH‐ELM). The proposed method consists of three main phases are pre‐processing, deep feature mining and selection, and classification. The input MRI images are pre‐processed through denoising and image enhancement methods in the first phase. In the second phase, the HL‐CNN is introduced for feature extraction. The hyper‐layer technique is a masking technique that also inherent the features of the specified layers instead of only considering the features at the last layer. The best features are selected using a simple correlation‐based selection approach through HL‐CNN validation to minimize the irrelevant features in the system. In the last phase, the HH‐ELM is introduced to classify the tumor images to identify the different types of tumors. HH‐ELM is an enhanced version of ELM through optimal tuning of ELM parameters using a hyper‐heuristic optimization algorithm. Evaluations are performed over the BRATS 2020 database of MRI images and the proposed method of HL‐CNN and HH‐ELM achieved dice scores of 0.9020, 0.9393, and 0.9589 for ED, WT, and TC tumor classes with 95.89% accuracy, 98.46% precision, 96% recall, and 97.21% f‐measure which are 2%–13% higher and processing time of 139.88 s which is 66%–78% lesser than the existing methods.