trimaran scheduler

KEP - Trimaran: Real Load Aware Scheduling #

Table of contents #.

• Notes/Constraints/Caveats
• Risks and Mitigations
• Metrics Provider
• Load Watcher/Load Analyser
• Scheduler Plugins
• Score plugin
• Bad Metrics
• Load Watcher API
• Scheduled Pods State
• Scalability
• Troubleshooting
• Implementation History
• Appendix - Load Watcher JSON schema

Minimizing machine costs by utilizing all nodes is the main objective for efficient cluster management. To achieve this goal, we can make the Kubernetes scheduler aware of the gap between resource allocation and actual resource utilization. Taking advantage of the gap may help pack pods more efficiently, while the default scheduler that only considers pod requests and allocable resources on nodes cannot.

Motivation #

Kubernetes provides a declarative resource model that core components (scheduler and kubelet) honor to behave consistently and satisfy QoS guarantees. However, using this model can lead to low-utilization of the cluster for the following reasons:

• It is hard for users to estimate accurate resource usage for applications. Also, users may not understand the resource model and not set it at all.
• The default in-tree scheduling plugins (Score) that Kubernetes provides don’t consider live node utilization values.

This proposal utilizes real time resource usage to schedule pods with proposed plugins. The eventual goal is to increase cluster utilization and decrease costs of cluster management without breaking the contract of Kubernetes Resource Model.

• Provide configurable scheduling plugins to increase cluster utilization.
• Expected CPU Utilisation per node should not go beyond X% (constraint).
• To not affect the behavior of default score plugins unless necessary.
• Implement the above features as Score plugins

Non-Goals #

• Implement the above constraint 2 as Filter plugins.
• Descheduling due to unexpected outcome (hot nodes, fragmentation etc.) of past scoring by plugins is not addressed in the initial design.
• Memory, Network, and Disk utilization are not considered in the initial design.

User Stories #

As a company relying on the public cloud, we would like to minimize machine costs by efficiently utilizing the nodes leased.

As a company-owned data center, we would like to minimize the hardware and maintenance associated with clusters by getting the maximum possible juice out of existing machines.

Increasing resource utilization as much as possible may not be the right solution for all clusters. As it always takes some time to scale up a cluster to handle the sudden spikes of load, cluster admins would like to leave adequate room for the bursty load, so there is enough time to add more nodes to the cluster.

Notes/Constraints/Caveats #

Enabling our plugin(s) will cause conflict with 2 default scoring plugins: “NodeResourcesLeastAllocated” and “NodeResourcesBalancedAllocation” plugins. So it is strongly advised to disable them when enabling plugin(s) mentioned in this proposal. If you choose to enable “NodeResourcesMostAllocated” in tree plugin which is not enabled by default, then it would conflict with our scoring too.

Risks and Mitigations #

If utilization metrics are not available for a long time, we will fall back to the best fit bin pack based on allocations. There is no user action needed for this. To achieve X% utilization, it is recommended to set the value as X - 10 in practice. Refer to TargetLoadPacking Score Plugin below for more details. For more details on risks and mitigations associated with live metrics, refer to “Bad Metrics”.

Design Details #

Our design consists of the following components as outlined in the diagram and described below. We propose two plugins namely “TargetLoadPacking” and “LoadVariationRiskBalancing”. Both of them use metrics from load watcher for scoring nodes with different algorithms.

Metrics Provider #

This service provides metrics backed by a time-series database — for example, Prometheus, InfluxDB, Kubernetes Metrics Server etc.

Load Watcher/Load Analyser #

The load watcher and analyzer both run in a single process. The watcher is responsible for retrieving cluster-wide resource usage metrics like CPU, memory, network, and IO stats over windows of a specified duration from metrics providers above. It stores these in its local cache and persists several aggregations in the host local DB for fault tolerance. The analyzer is responsible for the detection of bad metrics and any remediation. Bad metrics could be due to missing metrics or metrics with considerable errors, making them anomalies. It can also be extended in the future to use ML models for analyzing metrics.

Load watcher would cache metrics in the last 15-minute, 10-minute, and 5-minute windows, which can be queried via REST API exposed. A generic interface will be provided that can support fetching metrics from any metrics provider. Example is provided under Load Watcher API.

This is a localhost database stored as a file. This DB aims to populate the load watcher’s cache quickly if it is lost due to a crash and perform scheduling decisions without any impact on latency. The file will be stored in host file system, so it will be persisted across pod failures on the same node. For HA setups, each load watcher process will maintain its corresponding file locally.

Scheduler Plugins #

This uses the scheduler framework of K8s to incorporate our customized real load aware scheduler plugins without modifying the core scheduler code. The plugins we proposed mainly include the following two.

• TargetLoadPacking Plugin: It is best fit variant of bin pack algorithm that scores nodes by their actual resource utilization in a way that all utilized nodes have around x% of utilization. Once all nodes reach x% utilization it moves to least fit variant.
• LoadVariationRiskBalancing Plugin: It is a node sorting plugin that sorts nodes base on both the mean and the standard deviation of node resource utilization. It aims to balance not only the average load but also the risk caused by load variations.

TargetLoadPacking Plugin #

Score plugin #.

This plugin would extend the Score extension point. K8s scheduler framework calls the Score function for each node separately when scheduling a pod.

Following is the algorithm:

• Get the utilization of the current node to be scored. Call it A.
• Calculate the current pod’s total CPU requests and overhead. Call it B.
• Calculate the expected utilization if the pod is scheduled under this node by adding i.e. U = A + B.
• If U <= X%, return (100 - X)U/X + X as the score
• If X% < U <= 100%, return X(100 - U)/(100 - X)
• If U > 100%, return 0

For example, let’s say we have three nodes X, Y, and Z, with four cores each and utilization 1, 2, and 3 cores respectively. For simplicity, let’s assume our pod to be scheduled has 0 cores CPU requests and overhead. Let X = 50%.

Utilization of each node:

The score of each node :

In the algorithm above, 50% is the target utilization rate we want to achieve on all nodes. We can be less aggressive by reducing it to 40% so that it has much lesser chances of going over 50% during spikes or unexpected loads. So in general to achieve X% utilisation, X - 10 value is recommended in practice.

In the 2nd step of the algorithm, one variant uses the current pod’s total CPU limits instead of requests, to have a stronger upper bound of expected utilization.

The X% threshold value for utilisation will be made configurable via plugin argument.

Algorithm Analysis

The above is a plot of the piecewise function outlined in the algorithm. The key observations here are manifold:

• As the utilization goes from 0 to 50%, we pack pods on those nodes by favoring them.
• The nodes are penalized linearly as the utilization goes beyond 50%, to spread the pod amongst those “hot” nodes.
• The positive slope begins from 50 and not from 0 because the range of score is from 0-100, and we want to maximize our score output for nodes we wish to favor so that the score is more substantial and other plugins do not affect it much.
• There is a break in the graph with a high drop due to the “penalty” we have.

Plugin Config Args

LoadVariationRiskBalancing Plugin #

Balancing load based on average would be risky sometimes, as it does not consider the bursty variations. A LoadVariationRiskBalancing plugin balances not only the average load but also the risk caused by load variations. Suppose we take the mean (M) and standard deviation (V) of all nodes’ utilization into a mu-sigma plot below. In that case, the LoadVariationRiskBalancing plugin will make placements such that all nodes’ utilization are aligned on the diagonal line, which is V + M = c. Here, c is a constant indicating the overall cluster utilization average plus the standard deviation. In summary, considering load on all nodes fluctuates dynamically over time, the LoadVariationRiskBalancing plugin favors nodes with lower risks of the load exceeding capacity.

• Get the requested resource for the pod to be scheduled as, $r$.
• Get the sliding window average $M$ and standard deviation $V$ of resource utilization fraction (range from 0 to 1) for all types of resources (CPU, Memory, GPU, etc.) of the current node to be scored.
• Calculate the score of the current node for each type of resource: $S_i = M + r + V$
• Get a score for each type of resource and bound it to [0,1]: $S_i = min(S_i, 1.0)$
• Calculate the node priority score per resource as: $U_i = (1 - S_i) \times MaxPriority$
• Get the final node score as: $U = min(U_i)$

For example, let’s say we have three nodes N1 , N2 , and N3 , and the pod to be scheduled have CPU and Memory requests as 500 milicores and 1 GB. All nodes have a capacity of 4 cores and 8 GB.

The pod request fraction can be computed as $r_{cpu} = \frac{1}{8}, r_{memory} = \frac{1}{8}$.

Then according to steps 2 ~ 4, the mean and standard deviation of CPU and memory fraction utilization can be computed as follows:

The score for each type of resource and each node are as follows according to step 5 ~ 6:

According to the scores we have, node N3 will be selected. The utilization fraction of nodes before and after the placement is as follows.

If we plot these in mu-sigma plots, we can see the placement automatically pushes the utilization of nodes toward the diagonal line sigma = 1 - mu. The 1 here indicates 100% of utilization. What can be configured is a coefficient ita , which indicates mu + ita x sigma <= 100 % and we choose ita = 1 here. ita here models the confidence of usage not exceeding the node capacity under the assumption that the actual usage follows Gaussian distribution and follows the 68-96-99.5 rule .

So when ita gets different values, we get different confidence of not exceeding the capacity.

• ita = 1 , we have a 16% risk that the actual usage exceeds the node capacity.
• ita = 2 , we have a 2.5% chance that the actual usage exceeds the node capacity.
• ita = 3 , we have a 0.15% chance that the actual usage exceeds the node capacity. By default, we choose ita = 1 as we would like to improve the overall utilization. ita can be configured via the SafeVarianceMargin for the plugin.

Bad Metrics #

Since load watcher is a major component needed by the plugin to find out utilization values, metrics are essential for the plugin’s correctness. Bad metrics can be due to several issues. A few of them being are given below, along with their remediation. Detection of bad metrics is a problem in its own, other than its remediation.

• Short inter-arrival times of pods: In this case, we predict utilization for the recent pods that got scheduled on the node based on its request values and a multiplier, and add it to the current utilization. If the pods belong to best-effort QoS, i.e. don’t have requests, we assume a number like 1 milicore which is configurable.
• Failure of Metric reporting agent running in node: In this case, the metrics would be missing for a particular time. We can use the latest available utilization window (not older than 5 minutes) along with predicted utilization of pods scheduled on that node after the window end time. If this is not available we avoid the node by scoring it minimum.
• Metrics Provider failure: The metrics provider can fail to serve requests for several reasons like OOM, unresponsive components, etc. In this case, every node’s metrics are unavailable, and we fall back to the best fit based on allocations. However, we do not plan to target the fall back in first implementation.
• Addition of new nodes: It is easy to handle this if no pods have been scheduled on them, so utilization must be 0. If any recent new pods are running already then we can use the method above for prediction. If there are long-running pods, we can avoid this node until metrics are available.
• Incorrect metrics (due to misconfiguration, bugs, etc.): It is challenging to detect such metrics. For the first implementation, we would like to ignore this case by assuming metrics are correct. However, we plan to add metrics from multiple providers to corroborate data points and minimize the effect of incorrect metrics on scheduling in the future.

Load Watcher API #

JSON Schema - see Appendix

JSON payload example:

Scheduled Pods State #

A controller will be added as a go routine watching events on .spec.nodeName . This will maintain a time ordered state of node → pod mappings for pods scheduled successfully in last 5 minutes. This state will be maintained across scheduling cycles and used from TargetLoadPacking/LoadVariationRiskBalancing Score plugin. It will help to predict utilisation based on allocations when metrics are bad, especially in case 1 above (Unavailability of metrics).

Test Plan #

Unit tests and Integration tests will be added.

Production Readiness Review Questionnaire #

Scalability #.

Will enabling / using this feature result in any new API calls?

Will enabling / using this feature result in introducing new API types?

Will enabling / using this feature result in any new calls to the cloud provider?

Will enabling / using this feature result in increasing size or count of the existing API objects?

Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs ?

It can affect scheduler latency, however since 2 plugins need to be disabled the overall latency would be likely lesser than before. This will be confirmed with some benchmarking.

Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, …) in any components?

No - the metrics are cached at load watcher and our plugins will only pull them when needed, and this shouldn’t be a non-negligible increase in resource usage. Moreover, the algorithms provided run in linear time for number of nodes.

Troubleshooting #

How does this feature react if the API server and/or etcd is unavailable?

Running pods are not affected. Any new submissions would be rejected by scheduler.

What are other known failure modes?

What steps should be taken if SLOs are not being met to determine the problem?

Implementation History #

Load watcher json schema #.

Trimaran: Load-Aware Scheduling for Power Efficiency and Performance Stability

If you're experiencing a cluster where some nodes are stubbornly congested and others are not, or some nodes are spiky in their utilization, or some pods are able to burst freely yet others are not, then you may need to use a Trimaran scheduler. In this talk, we will provide an overview of the Trimaran scheduler plugins and demonstrate their utility. Basically, Trimaran plugins are load-aware schedulers which place pods on nodes based on actual measured node resource utilization, while considering requests and limits specifications of resources. Having utilization as an objective helps (1) minimize power consumption by targeting an optimal range of utilization, (2) avoid congestion and interference among multiple containers running on the same node, and (3) lower the risk of overcommitment when containers burst their usage to the specified limits.

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Open hybrid cloud, original shows, how to bring your own scheduler into openshift with the secondary scheduler operator.

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To understand the Secondary Scheduler Operator, we need to understand the Kubernetes scheduler. The Kubernetes scheduler is an enterprise grade stable component in Kubernetes that decides where to place the incoming pods by a two step operation of filtering and scoring. The Kubernetes scheduler works well for applications that need to schedule pods in a sequence, for example a web application. Now the customer sees the benefits of Kubernetes, like DevOps and portability. They want to transform their specialized workload like HPC, Telco into containers and run it in Kubernetes. The default scheduler that works well for web servers does not work well for these specialized applications because these applications have special needs from the scheduler. For example:

• Coscheduling :  Start, execute and finish all pods at same time.
• Topology Aware Scheduler : Schedule pods based on node topology.
• Load Aware Scheduler : Schedule pods based on load of the nodes.

 So there is a need for new schedulers to run specialized workloads.

OpenShift is Red Hat’s enterprise Kubernetes distribution. This means OpenShift has the same scheduler as upstream Kubernetes, which is stable and enterprise grade but best suited for applications that need to schedule pods in a sequence. We in Red hat always listen to our customers and partners that there is a need to bring in their own scheduler, which can best run their application.

The Secondary Scheduler Operator allows customers or partners to bring in their own scheduler in OpenShift and run their application with that customized scheduler.OpenShift 4.x has been re-architected to be a self hosted platform which uses the same OpenShift constructs that any workload running on OpenShift uses. In order to safeguard the control components from the custom scheduler that the end user (customer) can bring in, we decided to have a secondary scheduler which does not have any impact on the control plane components.  

Architecturally the default scheduler is responsible for schedu  ling all the workloads including control plane components; however if the customer chooses to bring their own scheduler, they can leverage secondary scheduler operator to manage the workloads of their choice but the control plane components would still use the default scheduler shipped with OpenShift.

The following diagram explains the overall flow of how to add your scheduler as payload via the Secondary Scheduler Operator provided by Red Hat.

  There is a separation of responsibility when a customer installs their own scheduler via this operator. Customers are responsible for maintaining their custom scheduler and Red Hat is responsible for the operator.

In this example we will walk you through how easy it is to install the Load aware scheduling plugin  via the Secondary Scheduler Operator.

Step 1: Install the Secondary Scheduler Operator

• Create a namespace   openshift-secondary-scheduler-operator   to install the Secondary Scheduler Operator first.
• Open the OperatorHub console to search for the Secondary Scheduler Operator.
• Click on Install to install the Secondary Scheduler Operator.
• Choose the project namespace as openshift-secondary-scheduler-operator .
• Now the operator is ready to use.

Step 2: Configure the Secondary Scheduler Operator to install Trimaran scheduler as a secondary scheduler

• Create a  config.yaml  to define KubeSchedulerConfiguration  for the Trimaran scheduler  that runs  TargetLoadPacking  plugin. The schedulerName should be set as secondary-scheduler .
• Run the following commands to obtain the ${PROM_URL} and ${PROM_TOKEN} , and replace these with the real Prometheus endpoint and token in  config.yaml .
• Create a ConfigMap secondary-scheduler-config for the Trimaran KubeSchedulerConfiguration under the openshift-secondary-scheduler-operator namespace .  
• Click on Create SecondaryScheduler to create an instance of SecondaryScheduler and configure the SecondaryScheduler YAML accordingly.
• Configure the  schedulerConfig  to point to the secondary-scheduler-config  ConfigMap.
• Configure the  schedulerImage  to the default scheduler-plugin image  that includes Trimaran plugins.
• Then click Create to install a secondary scheduler instance that runs the Trimaran scheduler.
• Finally, the operator will install a  secondary-scheduler  deployment running under openshift-secondary-scheduler-operator  namespace. Check the logs to verify if the Trimaran scheduler is successfully running.

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targetloadpacking

This package is not in the latest version of its module.

This folder holds the TargetLoadPacking plugin implementation based on Trimaran: Real Load Aware Scheduling .

Maturity Level

• 💡 Sample (for demonstrating and inspiring purpose)
• 👶 Alpha (used in companies for pilot projects)
• 👦 Beta (used in companies and developed actively)
• 👨 Stable (used in companies for production workloads)

TargetLoadPacking Plugin

TargetLoadPacking depends on Load Watcher . It uses load-watcher in two modes.

Using load-watcher as a service. You can run load-watcher service separately to provide real time node resource usage metrics for TargetLoadPacking to consume. Instructions to build and deploy load watcher can be found here . In this way, you just need to configure watcherAddress: http://xxxx.svc.cluster.local:2020 to your load-watcher service. You can also deploy load-watcher as a service in the same scheduler pod, following the tutorial here .

Using load-watcher as a library to fetch metrics from other providers, such as Prometheus, SignalFx and Kubernetes metric server. In this mode, you need to configure three parameters: metricProviderType , metricProviderAddress and metricProviderToken if authentication is needed.

By default, metricProviderType is KubernetesMetricsServer if not set. Now it supports KubernetesMetricsServer , Prometheus and SignalFx .

• metricProviderType: KubernetesMetricsServer use load-watcher as a client library to retrieve metrics from Kubernetes metric server.
• metricProviderType: Prometheus use load-watcher as a client library to retrieve metrics from Prometheus directly.
• metricProviderType: SignalFx use load-watcher as a client library to retrieve metrics from SignalFx directly.

metricProviderAddress and metricProviderToken should be configured according to metricProviderType .

• You can ignore metricProviderAddress when using metricProviderType: KubernetesMetricsServer
• Configure the prometheus endpoint for metricProviderAddress when using metricProviderType: Prometheus . An example could be http://prometheus-k8s.monitoring.svc.cluster.local:9090 .

Apart from watcherAddress , you can configure the following in TargetLoadPackingArgs :

• targetUtilization : CPU Utilization % target you would like to achieve in bin packing. It is recommended to keep this value 10 less than what you desire. Default if not specified is 40.
• defaultRequests : This configures CPU requests for containers without requests or limits i.e. Best Effort QoS. Default is 1 core.
• defaultRequestsMultiplier : This configures multiplier for containers without limits i.e. Burstable QoS. Default is 1.5

The following is an example config to use load-watcher as a library to retrieve metrics from pre-installed prometheus, achieve around 80% CPU utilization, with default CPU requests as 2 cores and requests multiplier as 2.

Alternatively, you can use the load-watcher as a service in the config below.

Documentation ¶

• func New(_ context.Context, obj runtime.Object, handle framework.Handle) (framework.Plugin, error)
• func PredictUtilisation(container *v1.Container) int64
• type TargetLoadPacking
• func (pl *TargetLoadPacking) Name() string
• func (pl *TargetLoadPacking) NormalizeScore(context.Context, *framework.CycleState, *v1.Pod, framework.NodeScoreList) *framework.Status
• func (pl *TargetLoadPacking) Score(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod, ...) (int64, *framework.Status)
• func (pl *TargetLoadPacking) ScoreExtensions() framework.ScoreExtensions

Constants ¶

Variables ¶.

This section is empty.

Functions ¶

Func predictutilisation ¶.

PredictUtilisation predict utilization for a container based on its requests/limits

type TargetLoadPacking ¶

Func (*targetloadpacking) name ¶, func (*targetloadpacking) normalizescore ¶, func (*targetloadpacking) score ¶, func (*targetloadpacking) scoreextensions ¶, source files ¶.

• targetloadpacking.go

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Install the secondary scheduler operator and deploy the trimaran scheduler.

• No translations currently exist.
• How to install the Secondary Scheduler Operator and deploy the loadaware Trimaran scheduler image
• There is a separation of responsibility when a customer installs their own scheduler via this operator.
• Customers are responsible for maintaining their custom scheduler and Red Hat is responsible for the operator.
• This example uses the loadaware Trimaran scheduler image, which Red Hat does not support

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Santosh Abraham is a Member of the Technical Staff at Hewlett Packard Laboratories. Prior to joining HP Labs, Santosh taught at the University of Michigan, Ann Arbor. Santosh co-authored over 40 papers in multiprocessor systems, optimizing compilers, performance evaluation, memory hierarchy simulation and design. Santosh co-developed the widely used Cheetah cache simulator. Santosh received a B.Tech degree from the Indian Institute of Technology, Bombay, M.S. from the State University of New York, Stony Brook, and Ph.D. from the University of Illinois, Urbana, all in Electrical Engineering. Memory structures of custom embedded systems contain buffer memories and local memories, in lieu of conventional caches. Santosh's current research is in automating the design and compilation for such memory structures. Santosh was the primary designer and implementer of the Trimaran scheduler. The Trimaran scheduler supports meld scheduling and a variety of scheduling algorithms, including back-tracking. Santosh also developed a module to utilize memory dependence profiling information. Within Elcor, Santosh designed a module that reduces cache stalls by increasing the scheduling distance between loads that are likely to miss and their uses. Santosh co-designed a module to utilize effectively the local memory ports in architectures that have a local memory in addition to a conventional memory hierarchy. In the overall Pico framework, Santosh designed and implemented the Memory Spacewalker that automatically and efficiently evaluates a specified range of memory hierarchies using the Cheetah simulator and produces paretos for each level of the memory hierarchy as well as for the overall system.

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Sign in to your account How to use scheduler-plugins in Trimaran Scheduler? #362 Chenhait commented Mar 29, 2022 I followed the tutorial the first step is build a kube-scheduler image as follows: git clone git checkout cd scheduler-plugins env GOARCH=amd64 GOOS=linux go build -o kube-scheduler main.go however, when i run env GOARCH=amd64 GOOS=linux go build -o kube-scheduler main.go it shows Thank you very much if any advance!!! k8s-triage-robot commented Jun 27, 2022 The Kubernetes project currently lacks enough contributors to adequately respond to all issues and PRs. This bot triages issues and PRs according to the following rules: is applied was applied, is applied was applied, the issue is closed You can: Please send feedback to sig-contributor-experience at . /lifecycle stale Sorry, something went wrong. Huang-Wei commented Jun 27, 2022 is placed under . k8s-triage-robot commented Jul 27, 2022 The Kubernetes project currently lacks enough active contributors to adequately respond to all issues and PRs. This bot triages issues and PRs according to the following rules: is applied was applied, is applied was applied, the issue is closed You can: Please send feedback to sig-contributor-experience at . /lifecycle rotten k8s-triage-robot commented Aug 26, 2022 The Kubernetes project currently lacks enough active contributors to adequately respond to all issues and PRs. This bot triages issues and PRs according to the following rules: is applied was applied, is applied was applied, the issue is closed You can: Please send feedback to sig-contributor-experience at . /close k8s-ci-robot commented Aug 26, 2022 : Closing this issue. : This bot triages issues and PRs according to the following rules: is applied was applied, is applied was applied, the issue is closed You can: Please send feedback to sig-contributor-experience at . /close Instructions for interacting with me using PR comments are available . If you have questions or suggestions related to my behavior, please file an issue against the repository. No branches or pull requests Crash of a Tupolev TU-104B in Moscow: 11 killed 278 IMAGES How to Bring your own Scheduler into OpenShift with the Secondary How to Bring your own Scheduler into OpenShift with the Secondary How to use scheduler-plugins in Trimaran Scheduler? · Issue #362 scheduler-plugins/pkg/trimaran/loadvariationriskbalancing/analysis.go Trimaran Rapido 40: ultra-fast cruising in complete safety on three hulls VIDEO v7 vs trimaran review 💥💥💥 Trimaran ventures into harbor at 2024 Wooden Boat Festival #sailing #trimaran #sailcoachmakingyoubetter #sailwithus #fishing #TRYMAGIC Semi-Trimaran Workboat. Silacraft 715ac with twin 140hp Tohatsu Outboard Sailing Trio -one of Ian Farrier’s first production folding Trimarans Trimaran Scheduler COMMENTS scheduler-plugins/pkg/trimaran/README.md at master Trimaran: Load-aware scheduling plugins. Trimaran is a collection of load-aware scheduler plugins described in Trimaran: Real Load Aware Scheduling. Currently, the collection consists of the following plugins. TargetLoadPacking: Implements a packing policy up to a configured CPU utilization, then switches to a spreading policy among the hot nodes. Trimaran Trimaran: Load-aware scheduling plugins # Trimaran is a collection of load-aware scheduler plugins described in Trimaran: Real Load Aware Scheduling. Currently, the collection consists of the following plugins. TargetLoadPacking: Implements a packing policy up to a configured CPU utilization, then switches to a spreading policy among the hot nodes. Trimaran: Real Load Aware Scheduling KEP - Trimaran: Real Load Aware Scheduling # Table of Contents # Summary Motivation Goals Non-Goals Proposal User Stories Story 1 Story 2 Notes/Constraints/Caveats Risks and Mitigations Design Details Metrics Provider Load Watcher/Load Analyser DB Scheduler Plugins TargetLoadPacking Plugin Score plugin LoadVariationRiskBalancing Plugin Score plugin Bad Metrics Load Watcher API Scheduled Pods ... Improving the Resource Efficiency for OpenShift Clusters Via Trimaran With the Trimaran scheduler plug-ins, users can achieve basic load-aware scheduling that is not implemented in the default Kubernetes scheduler. Trimaran plug-ins can either balance the usage on nodes so that all nodes reach a certain percentage of utilization, or it can prioritize nodes that have lower risk when overcommitting the pods. ... Trimaran: Load-aware scheduling plugins Trimaran is a collection of load-aware scheduler plugins described in Trimaran: Real Load Aware Scheduling. \n. Currently, the collection consists of the following plugins. \n \n; TargetLoadPacking: Implements a packing policy up to a configured CPU utilization, then switches to a spreading policy among the hot nodes. (Supports CPU resource.) scheduler-plugins/kep/61-Trimaran-real-load-aware-scheduling ... Minimizing machine costs by utilizing all nodes is the main objective for efficient cluster management. To achieve this goal, we can make the Kubernetes scheduler aware of the gap between resource allocation and actual resource utilization. Taking advantage of the gap may help pack pods more ... Trimaran: Load-Aware Scheduling for Power Efficiency and Performance In this talk, we will provide an overview of the Trimaran scheduler plugins and demonstrate their utility. Basically, Trimaran plugins are load-aware schedulers which place pods on nodes based on actual measured node resource utilization, while considering requests and limits specifications of resources. Having utilization as an objective helps ... sigs.k8s.io/scheduler-plugins/pkg/trimaran Trimaran: Load-aware scheduling plugins. Trimaran is a collection of load-aware scheduler plugins described in Trimaran: Real Load Aware Scheduling. Currently, the collection consists of the following plugins. TargetLoadPacking: Implements a packing policy up to a configured CPU utilization, then switches to a spreading policy among the hot nodes. Trimaran Scheduler The recorded demo to showcase the Trimaran TargetLoadPacking. scheduler plugin at KubeCon EU 2024 in Paris. The code repo is here: https://github.com/wangche... Trimaran: Real Load Aware Scheduling in Kubernetes Don't miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The confe... How to Bring your own Scheduler into OpenShift with the Secondary Then click Create to install a secondary scheduler instance that runs the Trimaran scheduler. Finally, the operator will install a secondary-scheduler deployment running under openshift-secondary-scheduler-operator namespace. Check the logs to verify if the Trimaran scheduler is successfully running. apiVersion: kubescheduler.config.k8s.io/v1beta1 targetloadpacking package Overview. This folder holds the TargetLoadPacking plugin implementation based on Trimaran: Real Load Aware Scheduling.. Maturity Level. 💡 Sample (for demonstrating and inspiring purpose) 👶 Alpha (used in companies for pilot projects) Install the Secondary Scheduler Operator and deploy the Trimaran How to install the Secondary Scheduler Operator and deploy the loadaware Trimaran scheduler image There is a separation of responsibility when a customer installs their own scheduler via this operator. Customers are responsible for maintaining their custom scheduler and Red Hat is responsible for the operator. This example uses the loadaware Trimaran scheduler image, which Red Hat does not support Santosh Abraham The Trimaran scheduler supports meld scheduling and a variety of scheduling algorithms, including back-tracking. Santosh also developed a module to utilize memory dependence profiling information. Within Elcor, Santosh designed a module that reduces cache stalls by increasing the scheduling distance between loads that are likely to miss and ... Repository for out-of-tree scheduler plugins based on ... Repository for out-of-tree scheduler plugins based on the scheduler framework. This repo provides scheduler plugins that are exercised in large companies. These plugins can be vendored as Golang SDK libraries or used out-of-box via the pre-built images or Helm charts. Additionally, this repo ... 为 Kubernetes Scheduler 启用 Trimaran 插件 为 Kubernetes Scheduler 启用 Trimaran 插件. 在没有为整个集群的 Pods 良好 设置 Requests/Limits 的情况下，Scheduler 无法针对负载所使用的资源进行 Node 节点分配。. 此时，我们需要一个额外的打分插件，将资源更为空闲的 Node 节点评分拉高，使 Pod 更积极的调度在这些节点 ... Accident Poisk-06P RA-1711G, 04 Aug 2020 MAK. Narrative: An ultralight trike, owned by the Moscow Oblast State Department of Defense, crashed under unknown circumstances. According to available information, the pilot and the passenger on board were killed, the aircraft was destroyed. Crash of a Tupolev TU-104B in Moscow: 16 killed The descent to Domodedovo Airport was initiated by night and marginal weather conditions with limited visibility due to snow falls. On final, the airplane was unstable and not properly aligned on the glide so the captain decided to make a turn to the left when the airplane banked left to 35-40°, causing the left wing to struck the ground 135 meters short of runway threshold and 25 meters to ... Jewish Calendar 2024 Nemchinovka, Moscow Oblast, Russia Israel holiday schedule. Major Holidays. Minor Holidays . Rosh Chodesh. Minor Fasts . Special Shabbatot . Modern Holidays . Days of the Omer. Yom Kippur Katan . Weekly Torah portion on Saturdays Event titles. Show Hebrew date for dates with some event ... How to use scheduler-plugins in Trimaran Scheduler? #362 How to use scheduler-plugins in Trimaran Scheduler? #362. Closed Chenhait opened this issue Mar 29, 2022 · 5 comments Closed How to use scheduler-plugins in Trimaran Scheduler? #362. Chenhait opened this issue Mar 29, 2022 · 5 comments Labels. lifecycle/rotten Denotes an issue or PR that has aged beyond stale and will be auto-closed. Crash of a Tupolev TU-104B in Moscow: 11 killed The aircraft was involved in a post-maintenance test flight and was carrying six technicians and five crew members. Following a normal takeoff roll of 1,550 - 1,600 meters on runway 25, the pilot completed the rotation and a normal initial climb. catamaran gulet motorboat powerboat riverboat sailboat trimaran yacht