Dynamic Load Balancing Approach for Minimizing the Response Time Using An Enhanced Throttled Load Balancer in Cloud Computing

Mirobi, G. Justy; Arockiam, L.

doi:10.1109/icssit46314.2019.8987845

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Although this algorithm helps to achieve low response time, low make span, low task migration time, and handle heterogeneous host, it does not consider the status of the VM before task migration. Mirobi and Arockiam [39] proposed an enhanced throttled load balancer (ETLB) that allocated the workloads uniformly to all VMs. By using the threshold value, the load balancer classified the VMs to overloaded, balanced, and under-loaded VMs.…”

Section: Related Workmentioning

confidence: 99%

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Elnagar

ElKabbany

Al-Awamry

et al. 2022

IJECE

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<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

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Section: Related Workmentioning

confidence: 99%

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Elnagar

ElKabbany

Al-Awamry

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…• Load Balancing at the API level is usually realized using API Gateways [10], [11]. It works by distributing the load over multiple node instances to avoid overloading or underloading them [12]. This is realized using Load Balancing algorithms like round-robin, random, and weighted algorithms that keep track of the API requests and their assigned node instances utilizing a database.…”

Section: Introductionmentioning

confidence: 99%

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Malki,

Zdun

2023

2023 IEEE International Conference on Web Services (ICWS)

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There are many challenges in maintaining the desired quality of service levels in modern microservice and cloud applications. Numerous techniques and patterns, such as API Rate Limit, Load Balancing, and Request Bundle, have been suggested for API services and clients to improve quality properties related to performance and reliability. However, no study has measured the impact of these techniques and their combinations in a specific configuration, especially using a large distributed system workload setting. This paper experimentally studies the effects of combining the API Rate Limit, Load Balancing, and Request Bundle patterns based on a realistic, third-party microservice-based application deployed in a private cloud and on the Amazon Web Services cloud (AWS) using 130 different configurations. We have run each configuration 500 times in the private cloud, totaling more than 4500 hours of runtime, and 200 times on AWS, totaling more than 3900 hours of runtime. We developed regression models from the collected data to predict the performance and reliability impacts of combining such techniques and patterns. We found that the models provide acceptable prediction errors below 30% on the private cloud and AWS. Further, we found that the models work best in highly reliable environments like AWS. In addition to the concrete analyses provided in our work, we propose a general and largely automated method that can be followed iteratively to evaluate similar techniques and patterns for their quality properties.

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“…This pattern often makes use of Front or Edge proxies [9] and may combine both of them. • Load Balancing is usually achieved using API Gateways [10], [11] and aims to balance the load between multiple instances in the backend [12]. Many algorithms supporting Load Balancing exist but must be selected carefully to avoid problems like bottlenecks and performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Automated Pattern-Based Recommendation for Improving API Operation Performance and Reliability in Cloud-Based Architectures

Malki,

Zdun

2023

2023 IEEE International Conference on Software Services Engineering (SSE)

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The extensive use of APIs as the entry point to many Cloud-based applications has created challenging problems, especially concerning API quality properties such as performance and reliability. API best practices and patterns, such as bundling requests, rate limiting, or load balancing, have been proposed to solve these challenges. Unfortunately, no study investigating the impact of existing API practices and patterns on such quality properties exists beyond informal recommendations. In this paper, we fill this gap by proposing a pattern-based, automated recommendation approach to improve the performance and reliability of API operations. We provide a benchmark suite based on a realistic open-source microservice application to enable the automatic generation of comprehensive decision tree models. These models are then processed to generate API design recommendation algorithms to improve API operations regarding performance and reliability stored in catalogs for reuse. We validate our algorithms using extensive data sets generated by running the benchmark on a private cloud and AWS. For both environments, based on the decision tree models automatically generated from the measured data, API design recommendation algorithms have been calculated using our approach.

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Dynamic Load Balancing Approach for Minimizing the Response Time Using An Enhanced Throttled Load Balancer in Cloud Computing

Cited by 8 publications

References 10 publications

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Automated Pattern-Based Recommendation for Improving API Operation Performance and Reliability in Cloud-Based Architectures

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