We propose a fog computing simulator for analysing the design and deployment of applications through customized and dynamical strategies. We model the relationships among deployed applications, network connections and infrastructure characteristics through complex network theory, enabling the integration of topological measures in dynamic and customizable strategies such as the placement of application modules, workload location, and path routing and scheduling of services. We present a comparative analysis of the efficiency and the convergence of results of our simulator with the most referenced entity, iFogSim. To highlight YAFS functionalities, we model three scenarios that, to the best of our knowledge, cannot be implemented with current fog simulators: dynamic allocation of new application modules, dynamic failures of network nodes and user mobility along the topology.
iFogSim @cite_4 is a CloudSim extension that supports the management of edge-network entities and the evaluation of allocation policies. The infrastructure is defined by a set of entities:(or fog nodes), , (such as a network link) and . The application is modelled as a directed graph with (representing computational resources), (a data dependency between application modules), and (defining a sequence of that should be monitored along the simulation to compute the response time. In the article, the authors present two placement strategies that we describe in detail in the evaluation section: and . They introduce the simulator with two case studies: a latency-sensitive online game (namely, the EGG Tractor Beam game) and intelligent surveillance through distributed camera networks. Based on the iFogSim simulator, we use the application model in our simulator, introducing new improvements in the API, and we compare our results using the first case study and the two placement strategies as explained in the article.
@cite_4
2414114959 1896690221 2747744888 2755775376
Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances. We model a web application to support analysis and simulation of cloud environments.We implement the model as an extension of the CloudSim simulator.We found that the user behavior has a strong influence on the resource utilization.We found Generalized Extreme Value and Generalized Lambda distributions instead of Exponential distribution to represent session time. Display Omitted Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems' Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator. Internet of Things (IoT) will be one of the driving application for digital data generation in the next years as more than 50 billions of objects will be connected by 2020. IoT data can be processed and used by different devices spread all over the network. The traditional way of centralizing data processing in the Cloud can hardly scale because it cannot satisfy many of the latency critical IoT applications. In addition, it generates a too high network traffic when the number of objects and services increase. Fog infrastructure provides a beginning of an answer to such an issue. In this paper, we present a data placement strategy for Fog infrastructures called iFogStor. The objective of iFogStor is to take profit of the heterogeneity and location of Fog nodes to reduce the overall latency of storing and retrieving data in a Fog. We formulated the data placement problem as a Generalized Assignment Problem (GAP) and proposed two ways to solve it: 1) an exact solution using integer programming and 2) a heuristic one based on geographical zoning to reduce the solving time. Both solutions proved very good performance as they reduced the latency by more than 86 as compared to a Cloud based solution and by 60 as compared to a naive Fog solution. Using geographical zoning heuristic can allow solving problems with large number of Fog nodes efficiently and in a couple of seconds making iFogStor feasible in runtime and scalable. Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20 -80 and reduced data transfer and communication frequency between the edge node and the cloud by up to 95 . These results highlight the potential of fog computing for improving the quality of service and experience.