scholarly journals Performance modelling of multi-tier cloud applications using Simpy

2021 ◽  
Author(s):  
Dayle Chettiar
Keyword(s):  
Response Time ◽  
Discrete Event Simulation ◽  
Service Providers ◽  
Discrete Event ◽  
Queueing Network ◽  
Analytical Models ◽  
Performance Modelling ◽  
Event Simulation ◽  
Application Service ◽  
The Mean

Todays cloud deployed applications are mostly multi-tiered. Usually, the first tier consists of an Application Service Providers' (ASPs) web servers, the second tier has application servers and the third tier contains database servers. Tiered architectures are often difficult to evaluate in terms of performance. Existing performance models are very effective in finding the mean performance measures. However, metrics such as response-time percentiles are of greater importance to the end-users since it is more desirable to reduce the variability of a system’s response time, rather than minimizing the mean response time. In this work, a multi-tier application is modeled as an open queuing network of 3-tiers and the response-time percentiles are estimated using discrete event simulation. Here, we assume that each tier is replicated into a number of copies and each copy runs on a separate Virtual Machine (VM). Although simulation models are computationally more expensive as compared to analytical models, they are much more general. The simulation model of this work can be used as decision support for ASPs in order to determine the optimal configuration of VMs for a given workload such that a required response-time percentile is within a given threshold. In this work, Simpy, a discrete event simulation framework, has been used. The results show that as the number of VMs are increased in a 3-tier open queueing network, the overall system performance (i.e. percentiles and mean response times) does not necessarily become better. The results further show that different system configurations containing the same number of VMs, yield different performance depending on the replication level in different tiers.

scholarly journals Performance modelling of multi-tier cloud applications using Simpy

2021 ◽  
Author(s):  
Dayle Chettiar
Keyword(s):  
Response Time ◽  
Discrete Event Simulation ◽  
Service Providers ◽  
Discrete Event ◽  
Queueing Network ◽  
Analytical Models ◽  
Performance Modelling ◽  
Event Simulation ◽  
Application Service ◽  
The Mean

Todays cloud deployed applications are mostly multi-tiered. Usually, the first tier consists of an Application Service Providers' (ASPs) web servers, the second tier has application servers and the third tier contains database servers. Tiered architectures are often difficult to evaluate in terms of performance. Existing performance models are very effective in finding the mean performance measures. However, metrics such as response-time percentiles are of greater importance to the end-users since it is more desirable to reduce the variability of a system’s response time, rather than minimizing the mean response time. In this work, a multi-tier application is modeled as an open queuing network of 3-tiers and the response-time percentiles are estimated using discrete event simulation. Here, we assume that each tier is replicated into a number of copies and each copy runs on a separate Virtual Machine (VM). Although simulation models are computationally more expensive as compared to analytical models, they are much more general. The simulation model of this work can be used as decision support for ASPs in order to determine the optimal configuration of VMs for a given workload such that a required response-time percentile is within a given threshold. In this work, Simpy, a discrete event simulation framework, has been used. The results show that as the number of VMs are increased in a 3-tier open queueing network, the overall system performance (i.e. percentiles and mean response times) does not necessarily become better. The results further show that different system configurations containing the same number of VMs, yield different performance depending on the replication level in different tiers.

scholarly journals An online short-term bed occupancy rate prediction procedure based on discrete event simulation

2014 ◽  
Vol 3 (4) ◽  
pp. 37 ◽  
Author(s):  
Zhu Zhecheng
Keyword(s):  
Health Service ◽  
Discrete Event Simulation ◽  
Service Providers ◽  
Discrete Event ◽  
Occupancy Rate ◽  
Bed Occupancy ◽  
Short Term ◽  
Hospital System ◽  
Event Simulation ◽  
Health Service Providers

In recent years, population growth and aging society impose large pressure on the resource requirement in Singapore public hospital system. Beds are one of the most critical resources in healthcare system. How to manage beds efficiently is an important and challenging task for the health service providers in any healthcare systems. One frequently used performance indicator of bed management is bed occupancy rate, which measures the bed utilization. In this paper, an online prediction procedure based on discrete event simulation is proposed and developed to predict bed occupancy rate in a short term period. Simulation results show that the predicted values are closer to the actual values with narrower confidence interval compared to the offline approach. Hence such a prediction procedure is able to provide a more reliable reference for decision making of the health service providers.

scholarly journals Analysis of the Dynamics of Rougher Cells on the Basis of Phenomenological Models and Discrete Event Simulation Framework

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1454
Author(s):  
Manuel Saldaña ◽  
Purísima Neira ◽  
Víctor Flores ◽  
Carlos Moraga ◽  
Pedro Robles ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Theoretical Models ◽  
Analytical Models ◽  
Operational Parameters ◽  
Event Simulation ◽  
Mineral Recovery ◽  
Superficial Gas Velocity ◽  
The Impact ◽  
Over Time

Due to the increase in the amount of copper sulphide minerals processed through concentration processes and the need to improve the efficiency of these production processes, the development of theoretical models is making an important contribution to generating a better understanding of their dynamics, making it possible to identify the optimal conditions for the recovery of minerals, the impact of the independent variables in the responses, and the sensitivity of the recovery to variations in both the input variables and the operational parameters. This paper proposes a method for modeling, sensitizing, and optimizing the mineral recovery in rougher cells using a discrete event simulation (DES) framework and the fitting of analytical models on the basis of operational data from a concentration pilot plant. A sensitivity analysis was performed for low, medium, and high levels of the operative variables and/or parameters. The outcomes of the modeling indicate that the optimum mineral recovery is reached at medium levels of the flow rate of gas, bubble size, turbulence dissipation rate, surface tension, Reynolds number of bubble, bubble–particle contact angle, superficial gas velocity and gas hold-up in the froth zone. Additionally, the optimal response is reached at maximum levels of particle size and density and at minimum levels of bubble speed, fluid kinematic viscosity and fluid density in the sampled range. Finally, the recovery has an asymptotic behavior over time; however, the optimum recovery depends on an economic analysis, examining the marginalization of the response over time in an operational context.

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