Edge computing is seen as an enabler for upcoming applications requiring low latency offloading, such as augmented reality, and as a key building block for Internet of Things. Edge computing extends the centralized cloud computing model by distributing servers also close to the users, at the edge of the network. A key challenge for the clients remains on how to discover the nearby edge servers and how to access them. In this paper, we present eDisco, DNS-based edge discovery, that leverages existing protocols and requires no modifications to deployed infrastructure. eDisco enables service providers and clients to discover edge servers, and determine the optimal edge deployment configuration.
Edge computing, which is a fundamental component of emerging 5G architectures, involves onloading or offloading multiple virtual network functions from mobile devices to an edge network substrate. In this paper, we present a model for the complete edge function onloading problem, which consists of three main phases: (1) Cyber foraging, which involves discovery of resources monitoring the state of edge resources, (2) edge function mapping, which involves matching requests to available resources, and (3) allocation, which involves assigning resources to mappings. Using optimization theory, we show how these three phases are tightly connected, and how the wide spectrum of existing solutions that either solve a particular phase, or jointly solve two of the phases (along with their interactions), are incomplete and may lead to inefficiencies. Moreover, with extensive simulation experiments we demonstrate that joint optimization of all three phases enables the edge network to host a larger set of constrained edge function requests. This paper argues for the utility of back-end driven onloading to the edge as a way to address bandwidth use and latency challenges for future device-cloud interactions. Supporting such edge functions (EFs) requires solutions that can provide (i) fast and scalable EF provisioning and (ii) strong guarantees for the integrity of the EF execution and confidentiality of the state stored at the edge. In response to these goals, we (i) present a detailed design space exploration of the current technologies that can be leveraged in the design of edge function platforms (EFPs), (ii) develop a solution to address security concerns of EFs that leverages emerging hardware support for OS agnostic trusted execution environments such as Intel SGX enclaves, and (iii) propose and evaluate AirBox, a platform for fast, scalable and secure onloading of edge functions. 