Abstract
Big Data constructed based on the advancement of distributed computing and virtualization is considered as the current emerging trends in Data Analytics. It is used for supporting potential utilization of computing resources focusing on, on-demand services and resource scalability. In particular, resource scheduling is considered as the process of resource distribution through an effective decision making process with the objective of facilitating required tasks over time. The incorporation of heterogeneous computing resources by the Big Data consumers also permits the option of reducing energy usage and enhanced resource efficiency. Further, optimal scheduling of resources is considered as an NP hard problem due to the dynamic characteristics of the resources and fluctuating users’ demand. In this paper, a Hybrid Gradient Descent Spider Monkey Optimization (HGDSMO) algorithm is proposed to efficient resource scheduling by handling the issues and challenges in the Hadoop heterogenous environment. The proposed HGDSMO algorithm uses the Gradient Descentand foraging and social behavior of the spider monkey optimization algorithm involved in the objective of effective resource allocation. It is designed as the efficient task scheduling approach that balances the load of the cloud by allocating them to appropriate VMs depending on their requirements. It is also proposed as a dynamic resource management scheme for efficiently allocating the cloud resources for effective execution of clients’ tasks. The simulation results of the proposed HGDSMO algorithm confirmed to be potent in throughput, load balancing and makespan compared to the baseline hybrid meta-heuristic resource allocation algorithms used for investigation.
Highlights
In the recent days, Big Data has attracted a lot of attention from academia, industry as well as government as it offers substantial value to them
The results clearly proposed that the makespan of the Hybrid Gradient Descent Spider Monkey Optimization (HGDSMO) resource allocation algorithm is estimated to be excellent by 4.82%, 5.78% and 6.94%, remarkable to the compared Hybrid Gradient Descent and Cuckoo Search Optimization (HGDCSO), IHS-CSO and Mean Grey Wolf Optimization Algorithm (MGWO) schemes
This proposed HGDSMO resource allocation algorithm used makespan, imbalance degree and throughput for the objective function that quantifies the availability of the hosts or VMs to the tasks in the hadoop heterogeneous environment
Summary
Big Data has attracted a lot of attention from academia, industry as well as government as it offers substantial value to them. The scheduler enforces weight and dynamically update rules based on the estimation of the situations This Hadoop platform uses the aforementioned approach for job tracking and task allocation in the Big Data heterogeneous environment. It ensures fairness in processing and completing tasks based on its policies and simplicity involved in sharing the resources in the Hadoop heterogeneous environment In this context, computing services are considered to possess virtual data centers that are highly optimized for facilitating software, hardware and information resources for utilization depending on the demand requested from the users [6]. A Cuckoo Search meta-heuristic algorithm-based resource scheduling was proposed for heterogeneous environments [16] This CS-based resource scheduling scheme used the factors of throughput, makespan and response time for estimating the performance. This GLCA scheme confirmed a superior performance over the compared Genetic Algorithm, Min–Min, Ant Colony and Max–Min optimization algorithm-based scheduling approaches
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