KCSS: Kubernetes container scheduling strategy

Menouer, Tarek

doi:10.1007/s11227-020-03427-3

Cited by 68 publications

(28 citation statements)

References 18 publications

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“…It can combine the set scheduling algorithm to establish the association between the pod and the specific host node in the cluster and write the associated information in the etcd, so that each part maintains a normal operating state, so it has the function of connecting the previous and the next. e entire scheduling process is actually to deploy specific pod tasks in the host node using the scheduling algorithm and then use kubelet to detect the pod binding events, and after the event list is obtained, the resources are bound and the corresponding ports are opened, thereby completing the start of the container [5].…”

Section: 11mentioning

confidence: 99%

[Retracted] Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology

Wang

2022

Journal of Mathematics

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Aiming at the problems of slow teaching resource sharing rate, long platform response time, and low student learning efficiency in traditional ideological and political learning platforms, a research on the construction of intelligent media ideological and political learning platforms based on artificial intelligence technology is proposed. We build an artificial intelligence open source development platform framework based on the cloud platform and use the Ceph method to optimize the storage of artificial intelligence training platform data. Under this framework, we design the business process and business module service architecture of the intelligent media ideological and political learning platform. Based on the K-means algorithm, the intelligent media ideological and political learning platform resource management module is designed, the teaching resource database is constructed, and the teaching resource sharing model component module is designed to realize the construction of the intelligent media ideological and political learning platform. The experimental results show that the sharing rate of ideological, political, and educational learning resources on the platform is relatively fast. The response time of the platform is 0.08 s when the amount of ideological and political teaching resources is 16000 MB. Students who are interested and very interested in the teaching account for 89% of the total.

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Section: 11mentioning

confidence: 99%

[Retracted] Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology

Wang

2022

Journal of Mathematics

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“…In the future, we plan to evaluate our multi-container deployment schemes on multiple hosts that communicate through different network fabrics and protocols (e.g., TCP/IP on Ethernet, TCP/IP on Infiniband (IPoIB), and RDMA on Infiniband). Also, we will use insights about the performance of multi-container deployments, especially those regarding the impact of the container granularity and the CPU and memory affinity, to derive placement policies when deploying HPC workloads which can get better utilization of the resources while maintaining application performance [26].…”

Section: Discussionmentioning

confidence: 99%

Performance comparison of multi-container deployment schemes for HPC workloads: an empirical study

Liu

Guitart

2020

J Supercomput

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The High-Performance Computing (HPC) community has recently started to use containerization to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads. Previous work showed that deploying an HPC workload into a single container can keep bare-metal performance. However, there is a lack of research on multi-container deployments that partition the processes belonging to each application into different containers. Partitioning HPC applications have shown to improve their performance on virtual machines by allowing them to be set affinity to a NUMA (Non-Uniform Memory Access) domain. Consequently, it is essential to understand the performance implications of distinct multi-container deployment schemes for HPC workloads, focusing on the impact of the container granularity and its combination with processor and memory affinity. This paper presents a systematic performance comparison and analysis of multi-container deployment schemes for HPC workloads on a single-node platform, which considers different containerization technologies (including Docker and Singularity), two different platform architectures (UMA and NUMA), and two application subscription modes (exactly-subscription and over-subscription). Our results indicate that finer-grained multi-container deployments, on one side, can benefit the performance of some applications with low inter-process communication, especially in over-subscribed scenarios and when combined with affinity but, on the other side, they can incur some performance degradation for communicationintensive applications when using containerization technologies that deploy isolated network namespaces.

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“…The need to reduce power consumption can be also a part of a scheduling strategy that considers multiple other criteria at the same time, as proposed in [76]. The considered criteria include the utilization rate of the CPU, memory and disk on the nodes, the nodes' power consumption, the number of running containers and the time needed for transmitting the container image.…”

Section: Environmental Impact (O3)mentioning

confidence: 99%

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

Rejiba

Chamanara

2022

ACM Comput. Surv.

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Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. In order to determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by the academia or industry to drive further research and development activities in the area of custom scheduling in Kubernetes.

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KCSS: Kubernetes container scheduling strategy

Cited by 68 publications

References 18 publications

[Retracted] Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology

[Retracted] Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology

Performance comparison of multi-container deployment schemes for HPC workloads: an empirical study

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

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