Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Pre-deployed 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

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

Towards network-aware service placement in community network micro-clouds

Cloud services in community networks have been enabled by micro-cloud providers. They form community network micro-clouds (CNMCs), which grow organically, i.e. without being planned and optimized beforehand. Services running in community networks face specific challenges intrinsic to these infrastructures, such as the limited capacity of nodes and links, their dynamics and geographic distribution. CNMCs are used to deploy distributed applications, such as streaming and storage services, which transfer significant amounts of data between the nodes on which they run. Currently there is no support given to users for enabling them to chose better or the best option for specific service deployments. This paper looks at the next step in community network cloud service deployments, by taking network characteristics into account when deciding placement of service instances. We propose a service placement algorithm (PASP) that minimizes the service overlay diameter, while fulfilling service specific criteria. First, we characterize with simulations the potential performance gains of our approach. Secondly, we apply our algorithm to deploy a distributed storage service currently used in Guifi.net, and evaluate it in the real production network, assessing the performance and effects of our algorithm. We find that our PASP algorithm reduces the client reading times by an average of 16% (with a max. improvement of 31 %) compared to the currently used organic placement scheme. Our results show how the choice of an appropriate set of nodes, taken from a larger resource pool, can influence service performance significantly.Peer Reviewe

Towards network-aware service placement in community network micro-clouds

Cloud services in community networks have been enabled by micro-cloud providers. They form community network micro-clouds (CNMCs), which grow organically, i.e. without being planned and optimized beforehand. Services running in community networks face specific challenges intrinsic to these infrastructures, such as the limited capacity of nodes and links, their dynamics and geographic distribution. CNMCs are used to deploy distributed applications, such as streaming and storage services, which transfer significant amounts of data between the nodes on which they run. Currently there is no support given to users for enabling them to chose better or the best option for specific service deployments. This paper looks at the next step in community network cloud service deployments, by taking network characteristics into account when deciding placement of service instances. We propose a service placement algorithm (PASP) that minimizes the service overlay diameter, while fulfilling service specific criteria. First, we characterize with simulations the potential performance gains of our approach. Secondly, we apply our algorithm to deploy a distributed storage service currently used in Guifi.net, and evaluate it in the real production network, assessing the performance and effects of our algorithm. We find that our PASP algorithm reduces the client reading times by an average of 16% (with a max. improvement of 31 %) compared to the currently used organic placement scheme. Our results show how the choice of an appropriate set of nodes, taken from a larger resource pool, can influence service performance significantly.Peer Reviewe