Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Predeployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers-possibly from multiple locations-and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.

-This paper introduces a new paradigm for service oriented networking being developed in the FUSION project 1 . Despite recent proposals in the area of information centric networking, a similar treatment of services -where networked software functions, rather than content, are dynamically deployed, replicated and invoked -has received little attention by the network research community to date. Our approach provides the mechanisms required to deploy a replicated service instance in the network and to route client requests to the closest instance in an efficient manner. We address the main issues that such a paradigm raises including load balancing, resource registration, domain monitoring and inter-domain orchestration. We also present preliminary evaluation results of current work.

Abstract-We see a trend to design services as a suite of small service components instead of the typical monolithic nature of classic web services, which led to an increasing amount of longtail services on the Internet. Deploying instances everywhere to achieve a fast response time results in high costs, especially when these services are used infrequently and remain idle most of the time. One way to avoid needless over-provisioning is to deploy instances on-demand but this requires every component to be available upon request arrival.We propose a placement algorithm to maximize the amount of clients we can serve on-demand using the Docker layered filesystem. Docker facilitates automated deployment of services in lightweight software containers, allowing almost instantaneous deployment. Our algorithm finds the optimal storage location for layers so we can retrieve all service layers, deploy a service instance and provide a first response to a request within the desired time. We solve this problem using integer linear programming (ILP) and present techniques to improve the scalability of ILP while minimizing the performance loss. Results show that our approximation performs better with large scale problems than the classic ILP case.

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature.We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency (QuLa), a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of sourcebased routing with minimized state in forwarding tables.