Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

Spider monkey optimization (SMO) algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It simulates the foraging behavior of spider monkeys which have fission-fusion social structure (FFSS). In this paper, a modified spider monkey optimization algorithm is proposed. The self-adaptive inertia weight is introduced in the local leader phase to enhance the self-learning ability of the spider monkey. According to the function value of an individual, the distance from the optimal value is determined, so the inertia weight related the individual function value is added to strength the global search ability or local search ability. The proposed algorithm is tested on 20 benchmark problems and compared with the original SMO and the hybrid algorithm SMOGA and GASMO. The numerical results show that the proposed algorithm has a certain degree of improvement in convergence accuracy and convergence speed. The performance of the proposed algorithm is also inspected by two classical engineering design problems.

PEDTARA: Priority-Based Energy Efficient, Delay and Temperature Aware Routing Algorithm Using Multi-Objective Genetic Chaotic Spider Monkey Optimization for Critical Data Transmission in WBANs

Software-Defined Wireless Body Area Network (WBAN)s have gained significance in emergency healthcare applications for remote patients. Prioritization of healthcare data traffic has a high influence on the congestion and delay in the WBAN routing process. Currently, the energy constraints, packet loss, retransmission delay and increased sensor heat are pivotal research challenges in WBAN. These challenges also degrade the network lifetime and create serious issues for critical health data transmission. In this context, a Priority-based Energy-efficient, Delay and Temperature Aware Routing Algorithm (PEDTARA) is presented in this paper using a hybrid optimization algorithm of Multi-objective Genetic Chaotic Spider Monkey Optimization (MGCSMO). This proposed optimized routing algorithm is designed by incorporating the benefits of chaotic and genetic operators to the position updating function of enhanced Spider Monkey Optimization. For the prioritized routing process, initially, the patient data transmission in the WBAN is categorized into normal, on-demand and emergency data transmissions. Each category is ensured with efficient routing using the three different strategies of the suggested PEDTARA. PEDTARA performs optimal shortest path routing for normal data, energy-efficient emergency routing for high priority critical data and faster but priority verified routing for on-demand data. Thus, the proposed PEDTARA ensures energy-efficient, congestion-controlled and delay and temperature aware routing at any given period of health monitoring. Experiments were performed over a high-performance simulation scenario and the evaluation results showed that the proposed PEDTARA performs efficient routing better than the traditional approaches in terms of energy, temperature, delay, congestion and network lifetime.

An Efficient Regression Test Suite Optimization Approach Using Hybrid Spider Monkey Optimization Algorithm

Regression testing validates the modified software and safeguards against the introduction of new errors during modification. A number of test suite optimization techniques relying on meta-heuristic techniques have been proposed to find the minimal set of test cases to execute for regression purposes. This paper proposes a hybrid spider monkey optimization based regression test suite optimization approach and empirically compares its performance with three other approaches based on bat search, ant colony, and cuckoo search. The authors conducted an empirical study with various subjects retrieved from software artifact infrastructure repository. Fault coverage and execution time of algorithm are used as fitness measures to meet the optimization criteria. Extensive experiments are conducted to evaluate the performance of the proposed approach with other search-based approaches under study using various statistical tests like m-way ANOVA and post hoc tests including odds ratio. Results indicate the superiority of the proposed approach in most of the cases and comparable in others.