Value-Based Allocation of Docker Containers

Recently, an increasing number of public cloud vendors added Containers as a Service (CaaS) to their service portfolio. This is an adequate answer to the growing popularity of Docker, a software technology allowing Linux containers to run independently on a host in an isolated environment. As any software can be deployed in a container, the nature of containers differs and thus assorted allocation and orchestration approaches are needed for their effective execution. In this paper, we focus on containers whose execution value for end users varies over time. A baseline and two dynamic allocation algorithms are proposed and compared with the default Docker scheduling algorithm. Experiments show that the proposed approach can increase the total value obtained from a workload up to three times depending on the workload heaviness. It is also demonstrated that the algorithms scale well with the growing number of nodes in a cloud

Value-Based Allocation of Docker Containers

Recently, an increasing number of public cloud vendors added Containers as a Service (CaaS) to their service portfolio. This is an adequate answer to the growing popularity of Docker, a software technology allowing Linux containers to run independently on a host in an isolated environment. As any software can be deployed in a container, the nature of containers differs and thus assorted allocation and orchestration approaches are needed for their effective execution. In this paper, we focus on containers whose execution value for end users varies over time. A baseline and two dynamic allocation algorithms are proposed and compared with the default Docker scheduling algorithm. Experiments show that the proposed approach can increase the total value obtained from a workload up to three times depending on the workload heaviness. It is also demonstrated that the algorithms scale well with the growing number of nodes in a cloud

EdgeMORE: improving resource allocation with multiple options from tenants

International audienceUnder the paradigm of Edge Computing (EC), a Network Operator (NO) deploys computational resources at the network edge and let third-party Service Providers (SPs) run on top of them, as tenants. Besides the clear advantages for SPs and final users thanks to the vicinity of computation nodes, a NO aims to allocate edge resources in order to increase its own utility, including bandwidth saving, operational cost reduction, QoE for its users, etc. However, while the number of third-party services competing for edge resources is expected to dramatically grow, the resources deployed cannot increase accordingly, due to physical limitations. Therefore, smart strategies are needed to fully exploit the potential of EC, despite its constrains. To this aim, we propose to leverage service adaptability, a dimension that has mainly been neglected so far: each service can adapt to the amount of resources that the NO has allocated to it, balancing the fraction of service computation performed at the edge and relying on remote servers, e.g., in the Cloud, for the rest. We propose EdgeMORE, a resource allocation strategy in which SPs express their capabilities to adapt to different resource constraints, by declaring the different configurations under which they are able to run, specifying the resources needed and the utility provided to the NO. The NO then chooses the most convenient option per each SP, in order to maximize the total utility. We formalize EdgeMORE as a Integer Linear Program. We show via simulation that EdgeMORE greatly improves EC utility with respect to the standard where no multiple options for running services are allowed