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

A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented

VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

Server Structure Proposal and Automatic Verification Technology on IaaS Cloud of Plural Type Servers

In this paper, we propose a server structure proposal and automatic performance verification technology which proposes and verifies an appropriate server structure on Infrastructure as a Service (IaaS) cloud with baremetal servers, container based virtual servers and virtual machines. Recently, cloud services have been progressed and providers provide not only virtual machines but also baremetal servers and container based virtual servers. However, users need to design an appropriate server structure for their requirements based on 3 types quantitative performances and users need much technical knowledge to optimize their system performances. Therefore, we study a technology which satisfies users' performance requirements on these 3 types IaaS cloud. Firstly, we measure performances of a baremetal server, Docker containers, KVM (Kernel based Virtual Machine) virtual machines on OpenStack with virtual server number changing. Secondly, we propose a server structure proposal technology based on the measured quantitative data. A server structure proposal technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology which executes necessary performance tests automatically on provisioned user environments according to the template.Comment: Evaluations of server structure proposal were insufficient in section