HHO-ACO hybridized load balancing technique in cloud computing

Sumathi, M.; Vijayaraj, N.; Raja, S. P.; Rajkamal, M

doi:10.1007/s41870-023-01159-0

Cited by 17 publications

(11 citation statements)

References 25 publications

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“…The dataset includes CPU and memory utilization, disc usage, etc. Prior studies 1 , 2 demonstrated that less than 3% of operations need an extended period. A lengthy job containing ID “Job-16-17658948” and 62,071 procedures previously utilized in examinations had been selected to assess proposed and existing models.…”

Section: Resultsmentioning

confidence: 99%

“…Cloud services require adaptive distribution of computing resources with dynamic resource scalability, which helps in delivering a Quality-of-Service (QoS) with the most minor resource expenses. However, in complicated cloud settings with changing workloads, it might be challenging to implement dynamic resource distribution for heterogeneous applications 1 .…”

Section: Introductionmentioning

confidence: 99%

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A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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“…The authors of [30] propose a reinforcement Learning-based parallel PSO for Intelligent Decision-Making. The authors of [31] presented a hybrid algorithm that combines Harries Hawks Optimisation (HHO) with Ant Colony Optimisation (ACO). In [42], the authors provided multi-objective load balancing using fuzzy decision-making and GA. A load-balancing algorithm in cloud computing was proposed by authors [11] that use the Ant colony optimization technique.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Singhal,

Sharma,

Anushree

et al. 2024

IEEE Access

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Cloud computing offers dynamic, scalable, and virtualized computing resources to end users over the internet. Load balancing is crucial for efficient resource use, distributing workloads across multiple resources to prevent overloading. Load balancing is crucial for resource utilization and processing time reduction, but traditional algorithms are often stuck at local maxima, leading to unequal allocation and performance decline. A metaheuristic based algorithm is proposed to dynamically adjust load distribution, ensuring resilience and sensitivity to changing workloads while managing energy consumption. This research presents a Rock Hyrax-based load balancing algorithm that addresses local maxima and power efficiency issues using QoS parameters. The algorithm's performance is evaluated qualitatively and statistically, considering both static and dynamic modes of jobs and virtual machines. Comparing it with existing scheduling algorithms, the algorithm reduces makespan by 10%-15% and total energy consumption in data centers by 8%-13%. These results demonstrate the effectiveness of the Rock Hyrax-based load balancing algorithm in improving performance and energy efficiency in data centers, highlighting its potential impact on optimizing resource allocation and enhancing overall system performance. INDEX TERMSCloud computing, Energy consumption, Load balancing, Makespan, Rock Hyrax I. INTRODUCTION

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“…Dynamic algorithms rely on the system's current state to make decisions without considering any previous information regarding the system. Dynamic algorithms consider multiple criteria, such as transfer, selection, location, and information policies, to achieve an equitable distribution of the burden [13]. Furthermore, it considers any dynamic changes in the state of the servers.…”

Section: Dynamic Load Balancingmentioning

confidence: 99%

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Rajpoot,

Singh,

Pant

2023

Preprint

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Smart healthcare systems are novel innovations that can improve healthcare services by giving individuals details about their medical conditions in real time. However, processing and analyzing huge amounts of information stored in these systems can frequently result in latency and interruptions, negatively impacting the system's efficiency. To solve this issue, this research presents an innovative healthcare framework incorporating cloud and fog computing techniques to reduce latency and enhance whole system performance. The framework has three different tiers: the user tier, the fog tier, and the cloud tier. Health-related information is gathered from users in the user tier using cloud pulses. An optimized Ant Colony Optimization load balancing algorithm subsequently monitors this data. The load balancer allocates user requests to fog servers in the fog tier, considering factors including response time and cost. The fog tier is comprised of fog servers that are located in closer proximity to end-users and are tasked with the real-time processing and analysis of data. The cloud tier accommodates vast quantities of healthcare data and facilitates sophisticated analytics and processing functionalities. The performance of the proposed framework was assessed by implementing it on the Cloud Analyst tool and utilizing metrics such as response time, cost, and system throughput. The experiment findings indicate that the proposed framework performs better in mitigating delay in healthcare compared to conventional healthcare systems. The load balancer that was optimized using Ant Colony Optimization algorithm efficiently allocated the workload among the fog servers, resulting in enhanced system response time and decreased latency. The concept of fog computing proved to be highly productive in mitigating latency by enabling local data processing and analysis near the end-user, thereby reducing reliance on the cloud tier. The empirical findings indicate that the framework put forward can transform the provision of healthcare services, rendering them more streamlined, economical, and focused on the needs of patients.

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HHO-ACO hybridized load balancing technique in cloud computing

Cited by 17 publications

References 25 publications

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

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