Simulation of Scalability in Cloud-Based IoT Reactive Systems Leveraged on a WSAN Simulator and Cloud Computing Technologies

Implementing a wireless sensor and actuator network (WSAN) in Internet of Things (IoT) applications is a complex task. The need to establish the number of nodes, sensors, and actuators, and their location and characteristics, requires a tool that allows the preliminary determination of this information. Additionally, in IoT scenarios where a large number of sensors and actuators are present, such as in a smart city, it is necessary to analyze the scalability of these systems. Modeling and simulation can help to conduct an early study and reduce development and deployment times in environments such as a smart city. The design-time verification of the system through a network simulation tool is useful for the most complex and expensive part of the system formed by a WSAN. However, the use of real components for other parts of the IoT system is feasible by using cloud computing infrastructure. Although there are cloud computing simulators, the cloud layer is poorly developed for the requirements of IoT applications. Technologies around cloud computing can be used for the rapid deployment of some parts of the IoT application and software services using containers. With this framework, it is possible to accelerate the development of the real system, facilitate the rapid deployment of a prototype, and provide more realistic simulations. This article proposes an approach for the modeling and simulation of IoT systems and services in a smart city leveraged in a WSAN simulator and technologies of cloud computing. Our approach was verified through experiments with two use cases. (1) A model of sensor and actuator networks as an integral part of an IoT application to monitor and control parks in a city. Through this use case, we analyze the scalability of a system whose sensors constantly emit data. (2) A model for cloud-based IoT reactive parking lot systems for a city. Through our approach, we have created an IoT parking system simulation model. The model contains an M/M/c/N queuing system to simulate service requests from users. In this use case, the model replication through hierarchical modeling and scalability of a distributed parking reservation service were evaluated. This last use case showed how the simulation model could provide information to size the system through probability distribution variables related to the queuing system. The experimental results show that the use of simulation techniques for this type of application makes it possible to analyze scalability in a more realistic way.

Download Full-text

Hybrid Multi-Cloud Demystifying SLAs for Smart City Enterprises Using IoT Applications

IoT Architectures, Models, and Platforms for Smart City Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-1253-1.ch003 ◽

2020 ◽

pp. 52-67

Author(s):

Lubna Luxmi Dhirani ◽

Thomas Newe ◽

Shahzad Nizamani

Keyword(s):

Cloud Computing ◽

Smart City ◽

Influencing Factor ◽

It Management ◽

Peak Performance ◽

Cloud Environment ◽

Iot Applications ◽

Single Standard ◽

Multiple Clouds ◽

Multi Cloud

Cloud computing migrations are increasing rapidly. The main influencing factor being IT management costs. IoT-based enterprises that started their cloud journey by setting up small private clouds within their enterprise have often found that as the applications and services they use broaden. Then the shift towards incorporating public clouds becomes inevitable. The current problem that many of these firms are encountering is the difficulty of managing multiple clouds that reside within different vendors running on different platforms, computational requirements, and vendor SLAs. Lack of support for a single standard for an overall multi-cloud hybrid model exposes the hybrid IT-management to further threats. This makes it difficult for an adopting enterprise to manage and maintain its cloud-based systems during peak performance hours, which often leads to system downtime. This chapter discusses various SLA issues specific to a hybrid multi-cloud environment and suggests possible solutions to help adopting firms in their management.

Download Full-text

Stateless IoT

Information ◽

10.3390/info11020085 ◽

2020 ◽

Vol 11 (2) ◽

pp. 85 ◽

Cited By ~ 1

Author(s):

Augusto Ciuffoletti

Keyword(s):

Energy Saving ◽

Design Principle ◽

Internal State ◽

Use Case ◽

System Operation ◽

Sensors And Actuators ◽

Iot Applications ◽

Basic Model ◽

Energy Footprint ◽

Save Energy

Energy consumption is a relevant matter in the design of IoT applications. Edge units—sensors and actuators—save energy by operating intermittently. When idle, they suspend their operation, losing the content of the onboard memory. Their internal state, needed to resume their work, is recorded on external storage: in the end, their internal operation is stateless. The backend infrastructure does not follow the same design principle: concentrators, routers, and servers are always-on devices that frustrate the energy-saving operation of edge devices. In this paper, we show how serverless functions, asynchronously invoked by the stateless edge devices, are an energy-saving option. We introduce a basic model for system operation and energy footprint evaluation. To demonstrate its soundness, we study a simple use case, from the design to a prototype.

Download Full-text

SIMULATION OF A SEA CARGO PORT AS A QUEUING SYSTEM IN ANYLOGIC

the System analysis and logistics ◽

10.31799/2007-5687-2020-4-59-66 ◽

2020 ◽

Vol 4 (26) ◽

pp. 59-66

Author(s):

A. G. Morozkov ◽

◽

M. R. Yazvenko ◽

Keyword(s):

Simulation Model ◽

Key Words ◽

Simulation Modeling ◽

Queue Length ◽

System Simulation ◽

Queuing System ◽

System Model ◽

System Capacity ◽

Mathematical Solution ◽

Structure Simulation

The article presents simplified queuing system model of freight marine port. The article discusses the basic elements of queuing system, its mathematical solution and structure. Simulation model was created using AnyLogic to analyze an effect of system capacity on queue length. The results were analyzed and the solution for queue optimization was proposed. Key words: queuing system, simulation modeling, AnyLogic, marine port, servers, queue.

Download Full-text

Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00211-9 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

VanDung Nguyen ◽

Tran Trong Khanh ◽

Tri D. T. Nguyen ◽

Choong Seon Hong ◽

Eui-Nam Huh

Keyword(s):

Cloud Computing ◽

Augmented Reality ◽

Mobile Devices ◽

Computing System ◽

Mobile User ◽

Computation Offloading ◽

Context Aware ◽

Computing Systems ◽

Iot Applications ◽

Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Download Full-text

GIS spatial information sharing of smart city based on cloud computing

Cluster Computing ◽

10.1007/s10586-018-2311-z ◽

2018 ◽

Vol 22 (S6) ◽

pp. 14435-14443 ◽

Cited By ~ 4

Author(s):

Pengzhen Cai ◽

Qijie Jiang

Keyword(s):

Cloud Computing ◽

Information Sharing ◽

Smart City ◽

Spatial Information ◽

Spatial Information Sharing

Download Full-text

Redundant VoD Streaming Service in a Private Cloud: Availability Modeling and Sensitivity Analysis

Mathematical Problems in Engineering ◽

10.1155/2014/764010 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Rosangela Maria De Melo ◽

Maria Clara Bezerra ◽

Jamilson Dantas ◽

Rubens Matos ◽

Ivanildo José De Melo Filho ◽

...

Keyword(s):

Sensitivity Analysis ◽

Cloud Computing ◽

Hierarchical Modeling ◽

Video On Demand ◽

Differential Sensitivity ◽

Private Cloud ◽

Streaming Service ◽

Critical Components ◽

Modeling Strategy ◽

Vod Streaming

For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability.

Download Full-text