Mobile Edge Computing Performance Evaluation using Stochastic Petri Nets

Carvalho, Daclé Vilma; Rodrigues, Laécio; Endo, Patrícia Takako; Kosta, Sokol; Silva, Francisco Airton

doi:10.1109/iscc50000.2020.9219650

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…This section presents the availability analysis for the two models already presented. Six scenarios are defined, varying the number of available UAVs (8,16,24,32,40, and 48 UAVs). These scenarios are defined based on the sensitivity analysis of the models.…”

Section: Results For Availabilitymentioning

confidence: 99%

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Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

et al. 2021

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Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, new forms of a connection must be explored to ensure the system’s availability and reliability as a whole. Unmanned aerial vehicles (UAVs) are becoming increasingly empowered in terms of processing power and autonomy. UAVs can be used as a bridge between IoT devices and remote servers, such as edge or cloud computing. UAVs can collect data from mobile devices and process them, if possible. If there is no processing power in the UAV, the data are sent and processed on servers at the edge or in the cloud. Data offloading throughout UAVs is a reality today, but one with many challenges, mainly due to unavailability constraints. This work proposes stochastic Petri net (SPN) models and reliability block diagrams (RBDs) to evaluate a distributed architecture, with UAVs focusing on the system’s availability and reliability. Among the various existing methodologies, stochastic Petri nets (SPN) provide models that represent complex systems with different characteristics. UAVs are used to route data from IoT devices to the edge or the cloud through a base station. The base station receives data from UAVs and retransmits them to the cloud. The data are processed in the cloud, and the responses are returned to the IoT devices. A sensitivity analysis through Design of Experiments (DoE) showed key points of improvement for the base model, which was enhanced. A numerical analysis indicated the components with the most significant impact on availability. For example, the cloud proved to be a very relevant component for the availability of the architecture. The final results could prove the effectiveness of improving the base model. The present work can help system architects develop distributed architectures with more optimized UAVs and low evaluation costs.

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Section: Results For Availabilitymentioning

confidence: 99%

“…SPNs [32][33][34][35][36][37][38] can be identified as a type of directed graph divided into two parts, filled by three types of objects. These objects are places, transitions, and directed arcs that connect places for transitions and transitions to places.…”

Section: Stochastic Petri Netmentioning

confidence: 99%

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

et al. 2021

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“…Stochastic modeling is appropriate for both deterministic and nondeterministic events. Stochastic Petri nets (SPNs) are special cases of stochastic models [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. SPNs enable setting up of state equations, algebraic equations, and other mathematical models governing the behavior of systems.…”

Section: Introductionmentioning

confidence: 99%

Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing

Fé¹,

Matos²,

Dantas

et al. 2022

Sensors

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Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

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“…Fog [10] brings part of the cloud capacity closer to the IoT layer. Edge devices [11] connect IoT sensors to the fog and cloud layers. Such a Cloud-Fog-Edge architecture provides the processing support needed by the IoHT constrained devices.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Silva

Nguyen

Fé³

et al. 2021

IEEE Access

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Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient's family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure's system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.

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Mobile Edge Computing Performance Evaluation using Stochastic Petri Nets

Cited by 10 publications

References 25 publications

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

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