For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.
Cloud computing has taken the world by storm. In this category of utility computing, a collection of computing resources (e.g. network, servers, storage) are pooled to serve multiple consumers, using a multi-tenant model. These resources are available over a network, and accessed through standard mechanisms . The cloud computing paradigm provides a variety of deployment models and service models, from public clouds (organizations provide cloud computing services to any customer) to private clouds (organizations deploy their own private cloud computing platform), and from Infrastructure as a Service models (IaaS, where fundamental computing resources are offered as a capability) to Software as a Service models (SaaS, where applications are offered as a capability), among other things. The benefits of cloud computing – minimal management effort, convenience, rapid elasticity, pay per use, ubiquity – have given birth to a multi-billion industry that is growing worldwide . Despite its benefits, cloud computing is not a panacea. Generally, public cloud vendors have built a few large data centers in various parts of the world. These large-scale, commodity-computer data centers have enough computing resources to serve a very large number of users. However, this centralization of resources implies a large average separation between end user devices and their clouds, which in turn increases the average network latency and jitter . Because of this physical distance, cloud services are not able to directly access local contextual information, such as precise user location, local network conditions, or even information about users’ mobility behaviour. For various delay-sensitive applications, such as vehicular networks and augmented reality, these requirements (low latency and jitter, context awareness, mobility support) are needed.