svLoad: An Automated Test-Driven Architecture for Load Testing in Cloud Systems

Noor, Jannatun; Hossain, Md. Golam; Alam, Muhammad Mahtab; Uddin, Md. Ashraf; Chellappan, Sriram; Islam, A. B. M. Alim Al

doi:10.1109/glocom.2018.8647493

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…A formal model of the TTSP is established, and a chaotic nondominated sorting genetic algorithm is presented to solve the problem. For load testing, [6], [9] use techniques like admission control to allocate virtual machine resources for sending client requests, but they do not use virtual machines in a shared mode. In the above methods, one virtual machine can process at most a single test task at a time.…”

Section: Related Workmentioning

confidence: 99%

Shared-Mode Resource Allocation for Cloud-Based Load Testing

2020

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Resource allocation is essential for cloud-based load testing. The existing techniques use coarse-grained resource allocation methods with an entire virtual machine occupied by a single test task for cloud-based load testing. The idle resources in a virtual machine are unable to be used by other load testing tasks. This may result in uneconomical use of test resources and increase test costs. To optimize the use of test resources, this paper presents a shared-mode resource allocation method for cloud-based load testing. The method shares client-side virtual machine resources among load testing tasks. It takes minimizing resource redundancy, test execution cost, and network communication cost as optimization objectives of resource allocation, with the assurance of enough test resources as a basic constraint. We introduce a multiobjective optimization algorithm to create an optimized resource allocation plan for load testing tasks within a time window. The experiments show that the proposed method can reduce resource demands for load testing and thereby save the test costs. INDEX TERMS cloud testing, load testing, test resource allocation, multi-objective optimization.

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Section: Related Workmentioning

confidence: 99%

Shared-Mode Resource Allocation for Cloud-Based Load Testing

2020

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“…Since repetitive searching is going on, so XDoser is creating load concurrently on the backend. In a recent paper "svLoad: An Automated Test-Driven Architecture forLoad Testing in Cloud Systems" [19], it shows that HTTP and HTTPS requests response time is quite more compare to other methodology used for testing. It present comparison of HTTP and HTTPS requests response time.…”

Section: Comparative Analysismentioning

confidence: 99%

Xdoser, a Benchmarking Tool for System Load Measurement Using Denial of Service Features

Haque¹,

Sultana²,

Fahad³

et al. 2019

IJNSA

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Technology has developed so fast that we feel both safe as well as unsafe in both ways. Systems used today are always prone to attack by malicious users. In most cases, services are hindered because these systems cannot handle the amount of over loads the attacker provides. So, proper service load measurement is necessary. The tool that is being described in this paper for developments is based on the Denial of Service methodologies. This tool, XDoser will put a synthetic load on the servers for testing purpose. The HTTP Flood method is used which includes an HTTP POST method as it forces the website to gather the maximum resources possible in response to every single request. The tool developed in this paper will focus on overloading the backend with multiple requests. So, the tool can be implemented for servers new or old for synthetic test endurance testing.

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“…They saw a 43% drop in 99th-percentile latency and a 93% drop in its variation by adjusting the hardware characteristics according to the attribution's recommendations. In order to evaluate the efficiency of cache with backend servers, the authors of [11] suggest a test-driven automated design called'svLoad' for load testing. In that example, they used technologies like JMeter and Ansible as well as some custom utilities bash scripts to create test cases that take into account a variety of real-world circumstances, such as various kinds of protocols, identical or distinct URLs, load or no load, cache hit or miss, and so on.…”

Section: Related Workmentioning

confidence: 99%

Performance and load testing: Tools and challenges

Yenugula,

Kodam,

2019

Int. J. Eng. Comput. Sci.

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Applications built for mobile devices, websites, online services, the cloud, and grid computing may all undergo performance testing. Tool installation, tool application flexibility, tool configuration, response time created by the tool, etc. are all potential tool-related difficulties that may arise during performance testing. This study will examine the application's performance using two separate tools, one of which is SoapUI and the other is Apache Jmeter. Since many tools provide varied results, we need to choose the testing instrument that is most suited to the job at hand. As an alternative to SoapUI's demand testing tool, Apache-Jmeter is a web application performance assessment tool that helps examine the server's efficiency under severe stress. It evaluates an application's Quality of Services under variable demand.

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svLoad: An Automated Test-Driven Architecture for Load Testing in Cloud Systems

Cited by 5 publications

References 11 publications

Shared-Mode Resource Allocation for Cloud-Based Load Testing

Shared-Mode Resource Allocation for Cloud-Based Load Testing

Xdoser, a Benchmarking Tool for System Load Measurement Using Denial of Service Features

Performance and load testing: Tools and challenges

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