An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

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.