The analytics techniques in Big Data are extensively employed as an alternative to generalized for data mining due to the huge volumes of large-scale high dimensional data. Feature selection techniques eradicate the redundant and inappropriate features to decrease the data dimensionality and increase the classifiers' efficiency. However, the traditional feature selection strategies are dearth of scalability to handle the unique characteristics of large-scale data and extract the valuable features within the restricted time. This article proposed a feature selection algorithm centered on the Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) that tackles the problem of local optimum and reduce convergence rate in standard Squirrel Search Algorithm (SSA). The novelty of this proposed PGS-ISSA is the introduction of chaos theory to improve population initialization so that the search space is increased. Then the acceleration coefficients are used in the position update equations to improve the convergence rate in local search process while inertia weight is also applied to optimally balance the exploration and exploitation in SSA. PGS-ISSA employs the fitness function based on the minimum error rate for ensuring the selection of best features that improve the classification accuracy. The proposed PGS-ISSA based feature section algorithm is evaluated by using Support Vector Machine (SVM) classifier implemented in MATLAB tool to address the big data classification problem. The experiments performed on both small and large-scale datasets illustrated that the suggested PGS-ISSA enhances the classification accuracy by 1.7% to 5.4% better than other compared models through effective handling of the big data problems. The results obtained for the bigger Higgs dataset shows that the proposed PGS-ISSA achieved high performance than the standard SSA, existing ISSA models and other prominent optimizationbased feature selection algorithms with 64.72% accuracy, 67.3194% precision, 62.1528% recall, 62.3026% f-measure, 82.7226% specificity and consumed less time of 140.1366 seconds. PGS-ISSA also achieved comparatively better results for the other benchmark datasets with 0.3% to 6% improvement on statistical metrics and 10% to 25% reduction in execution time.

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.

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