Mock Fog 2.0 Automated Execution of Fog Application Experiments in the Cloud

Abstract : Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime infrastructure will typically be in use or may not exist, yet. While approaches for the emulation of infrastructure testbeds do exist, their focus is typically the emulation of edge devices. Other approaches also emulate infrastructure within the core network or the cloud, but they miss support for automated experiment orchestration. In this paper, we propose to evaluate fog applications on an emulated infrastructure testbed created in the cloud which can be manipulated based on a pre-defined orchestration schedule. Developers can freely design the infrastructure, configure performance characteristics, manage application components, and orchestrate their experiments. We also present our proof-of-concept implementation MockFog 2.0. We use MockFog 2.0 to evaluate a fog-based smart factory application and showcase how its features can be used to study the impact of infrastructure changes and workload variations. With these experiments, we also show that MockFog can achieve good experiment reproducibility, even in a public cloud environment.

 EXISTING SYSTEM :

 ? In the ecosystem, the resources exist in different types and capabilities, and therefore an accurate resource monitoring algorithm is required for controlling any resource assignment. ? Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. ? The contribution of this survey paper is to design, frame, and suggest automatic components by studying and analyzing the existing literature and IoT application. ? Data heterogeneity exists in two types: local data accessible to IoT and Fog layer, and global data accessible to all layers. ? The machines’ social ring is a social network among machines similar to the one that exists among humans.

 DISADVANTAGE :

 ? If there are any problems that cannot be recovered autonomously, the node manager notifies the developer. ? While the Issue Application Commands action may distribute complex scripts if necessary, this action is a lightweight notification mechanism. ? Reducing the available compute and network resources seems to impact the stability of affected communication paths negatively. ? As the resource limitation seems not to impact aggregate, the additional operating system resources slightly decrease latency. ? We demonstrated how Mock Fog’s features can be used to study the impact of infrastructure changes and workload variations.

 PROPOSED SYSTEM :

 • Micro service is the proposed software architecture and MQTT, AMQP, and XMPP are examples of networking and communication protocols, which support orchestration and collaboration automation. • Although there are plenty of surveys on IoT tasks, IoT domains and case studies, the architecture, networks, and protocols, none of them has proposed a framework of automating different parts of the IoT ecosystem. • The main purpose of any IoT application is to help end-users in automating tasks, which are still manual. • The survey discusses the purpose, tasks, operations, and techniques used in each automation as well.

 ADVANTAGE :

 ? Developers can freely design the infrastructure, configure performance characteristics, manage application components, and orchestrate their experiments. ? This way, fog applications can be fully deployed in the cloud while experiencing comparable performance and failure characteristics as a real fog deployment. ? It is also possible to use more generic performance indicators such as instructions per second (IPS) or floating-point operations per second (FLOPS). ? Mock Fog aims to simplify experimenting with fog applications by providing developers with the means to design emulated fog infrastructure, configure performance characteristics, manage application components, and orchestrate their experiments.