Stochastic performance model for web server capacity planning in fog computing

Pereira, Paulo; Araújo, Jean; Torquato, Matheus; Dantas, Jamilson; Melo, Carlos; Maciel, Paulo

doi:10.1007/s11227-020-03218-w

Cited by 25 publications

(11 citation statements)

References 29 publications

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“…Few researchers also investigated the impact on performance using the reactive autoscaling policies and configurations [26] [27]. Some researchers also used fog computing techniques based on to develop a stochastic performance model for capacity planning using Markovian chain model [28]. Few other researchers developed Configuration Recommender tools, based on Support Vector Regression (SVR) and Genetic Algorithms (GA) [30].…”

Section: Related Workmentioning

confidence: 99%

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

Rudrabhatla¹

2020

IEEE Access

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Microservice architecture (MSA) has become a de facto standard for developing complex web applications lately. Horizontal scalability, domain isolation, agility and the provision to use heterogenous technologies are some of the key factors for the growing popularity of this architecture. To automatically cater to varying load patterns, quite a lot of advancements have been made in the field of cloud computing, containerization and orchestrating mechanisms which aid to perform the auto scaling of the microservices. However, setting up the scaling policies, optimal upper and lower thresholds is a daunting task for large applications. It generally involves some initial guess work followed by multiple rounds of tuning based on the real time load variations. This process causes situations where either the service becomes unavailable to the load when the thresholds are on the lower side, (or) underutilization of the compute resources when they are on higher side. This paper aims to find a quantitative way of determining the thresholds and step-up policies by deducing the mathematical formulas. To solve this formidable problem, we propose a model in which the total resource consumption of a container running in the peak load scenario can be calculated by-(1) first identifying the critical transactions and their maximum concurrency rates,(2) then calculating the resource consumption of such transactions in a controlled environment and (3) finally applying those values to the mathematical formulas based on Gaussian functions to calculate the total resource consumption for the peak load scenario. Using the total resource consumption value and considering the network and startup latencies, an optimal upper threshold value for step-up functions can be calculated. In this paper, we calculated the upper threshold values using the above-mentioned approach and verified using a research project that the calculated value is indeed the minimum number of containers to handle load.

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Section: Related Workmentioning

confidence: 99%

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

Rudrabhatla¹

2020

IEEE Access

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“…Usually, the processes in the computing server are modeled with multiple servers, including homogeneous servers [ 24 , 25 ] and heterogeneous servers [ 29 , 39 ]. An admission manager or load balancer is often modeled as a single server ahead of the computing servers [ 25 , 27 ]. For the task/data arrival, most studies described the task/data flow by Poisson distribution.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Kafhali and Salah [ 24 ] modeled the edge server by M/M/s/K queueing model. Pereira et al [ 25 ] proposed a queueing model for a Fog node containing a load balancer and several web servers to solve a closed-form solution. Liu et al [ 27 ] analyzed the user equipment (UE) side delay and server-side delay, respectively, from a perspective of task offloading and resource allocation.…”

Section: Related Workmentioning

confidence: 99%

“…All this considered, present studies on VM delay did not investigate the influence of VM migration on the total delay at the VMs, for the queueing models of VMs ignored the failure and migration of VMs [ 24 , 25 , 27 ], and the VM migration delay was measured separately from the total delay [ 12 , 13 , 14 , 42 , 44 , 45 , 46 , 47 , 53 ]. Meanwhile, due to the unique process of failure, migration, and recovery of VMs, present queueing models with unreliable servers [ 20 , 21 , 22 , 23 , 26 ] cannot be directly applied to analyze the VM delay.…”

Section: Related Workmentioning

confidence: 99%

“…To measure the timely reliability of edge computing more precisely, we need to build a queueing model considering failure, migration, and reboot of VM. Present mathematical models [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] cannot take the migration of VM into account and neglect the migration downtime. Therefore, to analyze the edge computing system considering migration and recovery of VM, the essential migration-related states of VM need to be defined in a new evaluation model.…”

Section: Introductionmentioning

confidence: 99%

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Timely Reliability Analysis of Virtual Machines Considering Migration and Recovery in an Edge Server

Liu

Guo

Wang

et al. 2020

Sensors

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For the edge computing network, whether the end-to-end delay satisfies the delay constraint of the task is critical, especially for delay-sensitive tasks. Virtual machine (VM) migration improves the robustness of the network, whereas it also causes service downtime and increases the end-to-end delay. To study the influence of failure, migration, and recovery of VMs, we define three states for the VMs in an edge server and build a continuous-time Markov chain (CTMC). Then, we develop a matrix-geometric method and a first passage time method to obtain the VMs timely reliability (VTR) and the end-to-end timely reliability (ETR). The numerical results are verified by simulation based on OMNeT++. Results show that VTR is a monotonic function of the migration rate and the number of VMs. However, in some cases, the increase in task VMs (TVMs) may conversely decrease VTR, since more TVMs also brings about more failures in a given time. Moreover, we find that there is a trade-off between TVMs and backup VMs (BVMs) when the total number of VMs is limited. Our findings may shed light on understanding the impact of VM migration on end-to-end delay and designing a more reliable edge computing network for delay-sensitive applications.

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