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.
@cite_16 present a concept of an EaaS (Edge-as-a-Service) platform, offering a discovery protocol. However, the approach relies on specific master controller nodes: to utilize edge, one must know the particular provider of EaaS beforehand to be able to discover the edge resources. Work by A. @cite_2 suggests , an edge computing framework also offering discovery capabilities. Discovery is enabled by special mediator nodes, that keep a connection to the cloud. They do not discuss how mediators are discovered. suggest the concept of @cite_26 , which is quite similar to our view of edge environment, where edge resources reside on a path from the cloud to an end user. Their framework, , addresses many practical issues, but edge discovery does not happen on the fly: the developer is actually responsible for specifying the mappings between application functions and actual computing facilities in the . We suppose that frameworks presented in @cite_16 , @cite_26 , and @cite_2 can potentially gain from utilizing the , which will remove the necessity for highly specialized components, adds more agility, and improves the overall user experience since is capable of discovering previously unknown resources.
@cite_16 @cite_26 @cite_2
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We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery protocol that identifies edge nodes and make them publicly accessible in a computing environment, and (ii) a scalable resource provisioning mechanism for offloading workloads from the cloud on to the edge for servicing multiple user requests. We validate the feasibility of EaaS on an online game use-case to highlight the improvement in the QoS of the application hosted on our cloud-edge platform. On this platform we demonstrate (i) low overheads of less than 6 , (ii) reduced data traffic to the cloud by up to 95 and (iii) minimised application latency between 40 -60 . Path computing is a new paradigm that generalizes the edge computing vision into a multi-tier cloud architecture deployed over the geographic span of the network. Path computing supports scalable and localized processing by providing storage and computation along a succession of datacenters of increasing sizes, positioned between the client device and the traditional wide-area cloud data-center. CloudPath is a platform that implements the path computing paradigm. CloudPath consists of an execution environment that enables the dynamic installation of light-weight stateless event handlers, and a distributed eventual consistent storage system that replicates application data on-demand. CloudPath handlers are small, allowing them to be rapidly instantiated on demand on any server that runs the CloudPath execution framework. In turn, CloudPath automatically migrates application data across the multiple datacenter tiers to optimize access latency and reduce bandwidth consumption. Edge computing is an increasingly popular paradigm, wherein computation comes closer to the sources of data. A key challenge for edge computing is discovering and utilizing the heterogeneous resources of the vast number of mobile devices at the network edge. Mobile edge devices hide behind private networks, they are mobile and their owners hesitate to share them due to privacy considerations. We propose Kinaara a framework for discovering and allocating collective resources for edge computing applications. First, Kinaara uses a multi-tier architecture to organize geographically proximal edge devices in clusters and provide their resources via trusted mediator entities. Second, it uses a novel resource representation to name and encode devices in terms of the resources advertised by their users. Third, it uses a novel distributed indexing scheme to organize devices in a proximal cluster on a ring logical structure based on their resource similarity. Our extensive experimental evaluation on a large Wi-Fi mobility dataset of 150K users and 345 APs show that compared to existing approaches Kinaara reduces resource discovery overhead by up to 70 and is robust to device mobility.