Network Emulation as a Service (NEaaS): Towards a Cloud-Based Network Emulation Platform

Lai, Junyu; Tian, Jiaqi; Zhang, Ke; Yang, Zheng; Jiang, Dingde

doi:10.1007/s11036-019-01426-0

Cited by 24 publications

(18 citation statements)

References 18 publications

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“…For example, Dockemu [33] adopted both Docker for emulating network nodes and NS-3 for simulating the network traffic. NEaaS [34], a cloudbased network emulation platform, utilizes both Docker and virtual machines to emulate various networking scenarios.…”

Section: Threads Services Networkmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan

Taheri

Al-Dulaimy

et al. 2023

IEEE Trans. Cloud Comput.

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Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.

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Section: Threads Services Networkmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan

Taheri

Al-Dulaimy

et al. 2023

IEEE Trans. Cloud Comput.

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show abstract

“…Several simulation and emulation testbeds 11,16,17 have been proposed in order to develop or test existing or new network applications, protocols, algorithms, or services. Unfortunately, not all simulation or emulation tools satisfy all the requirements mentioned before, leading to partial results as highlighted by Grajzer and Glabowski 7 Typically, network simulation is used for testing performance and scalability whereas network emulation is used later for final verification stages of the solution.…”

Section: Related Workmentioning

confidence: 99%

“…10 Furthermore, the reproducibility of the experiments is limited due to the dynamic changes of each network feature (i.e., do not represent isolated environments) and the long wait times required for allocating/consuming resources for large-scale experiments with complex network topologies. An alternate approach is the use of network emulators 11 which combine the advantages of both network simulations (e.g., isolated environments) and programmable testbeds (e.g., high fidelity environments) but without the need of actually setting up a real network. In a broader sense, network emulators provide an easy setup to reproduce (in a controllable manner) the behavior of a real network at low costs.…”

Section: Introductionmentioning

confidence: 99%

DockSDN: A hybrid container‐based software‐defined networking emulation tool

Petersen

2021

Int J Network Mgmt

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Summary The Internet has proven to be able to connect billions of devices across the globe. In order to keep pace with today's high demands or even larger services and applications, traditional ways of networking will have to change or update. The future of computer networks needs to be agile without degrading efficiency, capacity, and availability. These new trends push forward the need for network programmability. Software‐defined networkings (SDNs) are the future of networking, but in order to deploy newer standards, they have to be tested. Furthermore, this calls for better testbeds that are close to a real environment as possible. Many tools have been proposed to provide a framework for testing newer approaches of networking, but they have come short in various characteristics, limiting the scenarios and their results. Our experience in containers (Docker) and the development of testbeds using this technology has now brought our attention to SDNs. DockSDN provides a tool that meets current and future needs and is our biggest contribution to the scientific community so far. DockSDN is presented in this work, which proposes various benefits and advantages over other tools. One of those benefits is that it uses dockers as building blocks for scenario deployment. Moreover, these scenarios can be done fairly quickly and is fully scalable through local PC hardware or elastic through cloud services. Now, the scientific community will be able to test a wide array of protocols in near real‐world conditions, saving financial resources and time.

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“…For example, Dockemu [32] adopted both Docker for emulating network nodes and NS-3 for simulating the network traffic. NEaaS [33], a cloudbased network emulation platform, utilizes both Docker and virtual machines to emulate various networking scenarios.…”

Section: Performance Emulatorsmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan,

Taheri,

Al-Dulaimy

et al. 2021

Preprint

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Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.

show abstract

Network Emulation as a Service (NEaaS): Towards a Cloud-Based Network Emulation Platform

Cited by 24 publications

References 18 publications

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

DockSDN: A hybrid container‐based software‐defined networking emulation tool

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

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