Innovation in the concept of virtualization acts as a vigorous role in cloud computing. Virtualization helps in splitting the system resources in the form of the multiple and isolated execution environments. Hypervisor-based virtualization is related to the huge requirement of resources and operative overhead due to an additional stage of abstraction. In this review, we first were given an overview of virtualization innovation and Container-based virtualization. Container-based virtualization provides performance enhancements and reduced starting time and hence it is more beneficial than Virtual Machine (VM). Container virtualization does not imitate the hardware but only isolate the processes and it is more light-weight than VM. Container-based virtualization may be successfully implemented in "Fog Computing" framework as containers are suitable to the resource-constrained environment and also enables the distribution of different type of applications on cloud and fog Nodes. This paper describes the possible application of lightweight virtualization to Fog Nodes in the form of case studies.

Fog computing has become adaptable and also as a promising infrastructure for providing elastic resources at the edge of the network. Fog computing reduces the transmission latency and consumption of bandwidth while processing the incoming requests from various Internet of Things (IoT) devices.Moreover, fog computing can support and facilitate geographically distributed applications with low and predictable latency. However, this technology also has significant research issues in its current stage such as successful implementation of service location models. In this article, we propose a deadline-aware and energy-efficient dynamic service placement (DEEDSP) technique for fog computing that supports the placement of IoT based services. Further, hyper-heuristic algorithm based energy-efficient service placement technique is proposed to balance the energy-delay trade-off based on different service placement decision criteria (eg, minimum response time or energy consumption).The proposed algorithm is able to dynamically minimize the energy consumption of the system while ensuring that the response time satisfies a given time constraint. Finally, the proposed technique is evaluated in simulated fog computing environment and experimental results show that this technique performs better than state-of-the-art placement techniques in terms of energy and latency.

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Fog computing is crucial for the success of IOT applications that also need involvement of cloud. The distributed capability provided by Fog computing permits storage and execution to be performed at completely different locations. The mixture of distributed capability, the varyand kinds of user applications, require resource management and programmingways that takes into consideration these factors altogether. Due to tonof constraints the networked primarily basedsystems and services square measurea lot ofor less at risk offailures. The failure ought tobe handled and assessed effectively. The aim of this study is to give a superior comprehension of different QOS based service scheduling which plans to enhance the execution inafog computing, and furthermore audit on various fault tolerant based techniques involved in fog computing environment.

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