container management
Recently Published Documents

The container has several advantages over the traditional virtual machine technology such as light-weight, fast booting time, and fast recovery. Kubernetes is one the most outstanding container management and deployment platforms. The Kubernetes provides autoscaling function, which will increase and decrease the hardware resources to adapt with the current traffic load situation to keep the user experience. Two popular autoscaling methods are horizontal autoscaling and vertical autoscaling. Based on the monitoring resource utilization, horizontal autoscaling will increase the number of PoDs (point of deployment) or vertical autoscaling will increase the hardware resources of each PoD to achieve the target utilization. In this paper, we present a hybrid solution that combines the advantages of both autoscaling solutions and proposes a bandwidth-efficient scheduler strategy. By numerical analysis, our hybrid approach is better than the normal HPA approach in terms of bandwidth cost and has lower autoscaling latency than the VPA approach

Every year, thousands of tons of plant protection product (PPP) containers are indiscriminately discharged into the environment as toxic waste that has a negative impact on the land, water, and air environment as well as public health. This study surveyed the use of PPPs in rice cultivation, and the generation of hazardous waste (HW) when using pesticides, specifically pesticide packaging and containers in Long Kien and Long Dien B communes, Cho Moi district, An Giang province, Vietnam.  Data collection was conducted through direct interviews, mainly collecting personal information of farmers in the surveyed area, the current situation with regard to pesticide use, container management, environmental awareness, and proposals for hazardous waste management from the farmers’ perspectives. The results show that local farmers are aware of the harmful effects of pesticide containers, but they are not able to make use of effective methods of collecting and treating the waste containers properly. Based on the survey results, several solutions are proposed for managing HW in order to reduce environmental pollution from the use of pesticides, minimize the impacts of HW on people's health, and contribute to local sustainable development. 

Due to the repeated bearing of mechanical operations and natural factors, the container will suffer various types of damage during use. Adopting effective container damage detection methods plays a vital role in prolonging the service life and using function. This paper proposes a multitype damage detection model for containers based on transfer learning and MobileNetV2. In addition, a data set containing nine typical types of container damage is established. To ensure the validity and practicability of the model, we conducted tests and verifications in the actual port environment. The results show that the model can identify multiple types of container damage. Compared with the existing models, the damage detection model proposed in this paper can ensure the identification effect of various types of container damage, which is more suitable for the actual container detection situation. This method can provide a new idea of damage detection for container management in ports.

Thanks to the recent development in the technology number of IoT devices increased dramatically. Therefore,industries have been started to use IoT devices for their business processes. Many systems can be done automatically thanks to them. For this purpose, there is a server to process sensors data. Transferring these data to the server without any loss has crucial importance for the accuracy of IoT applications. Therefore, in this thesis a scalable broker for real time streaming data is proposed. Open source technologies, which are NoSql and in-memory databases, queueing, fulltext index search, virtualization and container management orchestration algorithms, are used to increase efficiency of the broker. Firstly, it is planned to be used for the biggest airport in Turkey to determine the staff location. Considering the experiment analysis, proposed system is good enough to transfer data produced by devices in that airport. In addition to this, the system can adapt to device increase, which means if number of devices increasing in time, number of nodes can be increased to capture more data.

Now-a-days, in the world of enterprise, machine learning workloads have become mainstream. However, there is an abundance of choices that can be made around multi-cloud infrastructure and machine learning toolkits, making it complex to balance their costs and performance. Microservices architecture has been the preferred architecture style for a few years now and there’s been rapid growth in its adoption, never failing to provide exceptionally testable & maintainable services. To have a lot more simplified services management, deployment and to orchestrate tools, Kubernetes is recommended. Kubeflow, a known and widely adopted open source container management platform that manages machine learning stack on Kubernetes. This paper discusses the development and validation of Kubeflow components such as PyTorch, TensorFlow, & Notebook Servers. It includes PodDefault functionalities for notebooks and container builder API to build docker images using Kaniko. Using Helm, Kubeflow upgrade operation is performed to enhance the configured resources whenever required for the distributed training jobs & workloads. Hence, providing data scientists a scalable platform to run machine learning workloads without having to worry about resources, costs, time, and portability.

AbstractContainerisation demonstrates its efficiency in application deployment in Cloud Computing. Containers can encapsulate complex programs with their dependencies in isolated environments making applications more portable, hence are being adopted in High Performance Computing (HPC) clusters. Singularity, initially designed for HPC systems, has become their de facto standard container runtime. Nevertheless, conventional HPC workload managers lack micro-service support and deeply-integrated container management, as opposed to container orchestrators. We introduce a Torque-Operator which serves as a bridge between HPC workload manager (TORQUE) and container orchestrator (Kubernetes). We propose a hybrid architecture that integrates HPC and Cloud clusters seamlessly with little interference to HPC systems where container orchestration is performed on two levels.