With the rise of the Internet of Things (IoT), fog computing has emerged to help traditional cloud computing in meeting scalability demands. Fog computing makes it possible to fulfill real-time requirements of applications by bringing more processing, storage, and control power geographically closer to edge devices. However, since fog computing is a relatively new field, there is no standard platform for research and development in a realistic environment, and this dramatically inhibits innovation and development of applications suitable for the fog. In response to these challenges, we propose the FDK: A Fog Development Kit for software-defined edge-fog systems. By providing high-level interfaces for allocating computing and networking resources, the FDK abstracts the complexities of fog computing from developers and enables rapid development of edge-fog systems. Also, the FDK supports the utilization of virtualized devices to create a highly realistic emulation environment, allowing fog application prototypes to be built with zero additional costs and enabling portability to a physical infrastructure. We evaluate the resource allocation performance of the FDK using a testbed, including eight edge devices, four fog nodes, and five OpenFlow switches. Our evaluations show that the delay of resource allocation and deallocation is less than 279ms and 256ms for 95 of edge-fog transactions, respectively. Besides, we demonstrate that resource allocations are appropriately enforced and guaranteed, even amidst extreme network congestion.
There are also many extensions to CloudSim, such as CloudSimSDN @cite_21 , ContainerCloudSim @cite_33 , and iFogSim @cite_2 , which attempt to broaden CloudSim's model to include SDN, Docker container migration simulations, and fog computing, respectively. However, because CloudSim and these associated extensions are strictly-simulation based, they utlimately do not solve the problems of cost and complexity associated with developing an actual edge-fog system. Rather, they simply avoid the problem altogether by simulating the entire system. Therefore, while CloudSim is a worthy platform for evaluating cloud architectures, load balancing algorithms, etc., it fails to actually serve as a valid edge-fog application development platform because . Likewise, the same can be said for most other simulation platforms for similar reasons.
@cite_21 @cite_33 @cite_2
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Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application. Summary Containers are increasingly gaining popularity and becoming one of the major deployment models in cloud environments. To evaluate the performance of scheduling and allocation policies in containerized cloud data centers, there is a need for evaluation environments that support scalable and repeatable experiments. Simulation techniques provide repeatable and controllable environments, and hence, they serve as a powerful tool for such purpose. This paper introduces ContainerCloudSim, which provides support for modeling and simulation of containerized cloud computing environments. We developed a simulation architecture for containerized clouds and implemented it as an extension of CloudSim. We described a number of use cases to demonstrate how one can plug in and compare their container scheduling and provisioning policies in terms of energy efficiency and SLA compliance. Our system is highly scalable as it supports simulation of large number of containers, given that there are more containers than virtual machines in a data center. Copyright © 2016 John Wiley & Sons, Ltd. 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.