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 . Edge computing (EC) has emerged as a key technology in future communication networks to enhance user experience and enable various Internet of Things (IoT) applications. In this paper, we propose an online framework for edge computing resource procurement in which a marketplace (platform) is established between sellers (i.e., resource contributors) and buyers (i.e., resource purchasers). Each buyer has a certain budget for his procurement campaign. The sellers arrive to the platform in an online fashion and offer their computing capacities along with prices that they want to be compensated for their services. Upon the arrival of every new offer, the platform has to make an irrevocable decision, without knowing future information, to accept the offer or not and to allocate the accepted resource to which buyer. We present an efficient online optimization method that helps the platform maximize the total system utility with guaranteed performance. Indeed, the developed model can be applied to other interesting settings such as edge caching and content delivery with slight modifications. Finally, numerical studies are conducted to illustrate the effectiveness of the proposed solution approach. The ubiquitous computing vision is to make knowledge and services easily available in our everyday environments. A wide range of devices, applications and services can be interconnected to provide intelligent and automatic systems that make our lives more enjoyable and our workplaces more efficient. Interaction typically is to be between peers rather than clients and servers. In this context, the JXTA peer-to-peer infrastructure, designed for interoperability, platform independence and ubiquity, is a suitable foundation to build future computer systems on. Peers need ways to effortlessly discover, consume and provide services, and to take advantage of new services as they become available in a dynamically changing network. However, JXTA does not currently handle this servicediscovery problem. In this paper, we examine several service-discovery architectures, to see whether they can be adapted to JXTA. We conclude that none of them adequately support the flexibility and expressiveness that ubiquitous computing requires. We therefore argue that Web Ontology Language (OWL) and OWL Services (OWL-S) ontologies should be used to express detailed semantic information about services, devices and other service-discovery concepts. This kind of approach allows peers to reason about service offerings and achieve intelligent service discovery by using an inference engine. We present an experimental implementation of this ontological approach to service-discovery, called Oden (Ontology-based Discovery- Mobile edge computing is an emerging technology to offer resource-intensive yet delay-sensitive applications from the edge of mobile networks, where a major challenge is to allocate limited edge resources to competing demands. While prior works often make a simplifying assumption that resources assigned to different users are non-sharable, this assumption does not hold for storage resources, where users interested in services (e.g., data analytics) based on the same set of data code can share storage resource. Meanwhile, serving each user request also consumes non-sharable resources (e.g., CPU cycles, bandwidth). We study the optimal provisioning of edge services with non-trivial demands of both sharable (storage) and non-sharable (communication, computation) resources via joint service placement and request scheduling. In the homogeneous case, we show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources. We further show that the hardness is caused by the service placement subproblem, while the request scheduling subproblem is polynomial-time solvable via maximum-flow algorithms. In the general case, both subproblems are NP-hard. We develop a constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case. Our trace-driven simulations show that the proposed algorithms, especially the approximation algorithm, can achieve near-optimal performance, serving 2–3 times more requests than a baseline solution that optimizes service placement and request scheduling separately.