Keepalives Are Temporarily In Throttle Due To Tcp
If you don't have access to the remote server, you can test by temporarily disabling the tcp_timestamps option on an instance in the private subnet. Then connect to the remote server again. If the connection is successful, the cause of the previous failure is likely because tcp_tw_recycle is enabled on the remote server. If possible, contact the owner of the remote server to verify if this option is enabled and request for it to be disabled.
keepalives are temporarily in throttle due to tcp
Amazon DocumentDB safeguards resources that are needed to run critical processes in the service, such as health checks. To do this, and when an instance is experiencing high memory pressure, Amazon DocumentDB will throttle requests. As a result, some operations may be queued to wait for the memory pressure to subside. If memory pressure continues, queued operations may timeout. You can monitor whether or not the service throttling operations due to low memory with the following CloudWatch metrics: LowMemThrottleQueueDepth, LowMemThrottleMaxQueueDepth, LowMemNumOperationsThrottled, LowMemNumOperationsTimedOut. For more information, see Monitoring Amazon DocumentDB with CloudWatch. If you see sustained memory pressure on your instance as a result of the LowMem CloudWatch metrics, we advise that you scale-up your instance to provide additional memory for your workload.
Firstly, you should be aware that migration jobs to Exchange Online (which is a shared multi-tenancy environment) have a lower priority on our servers and are sometimes throttled to protect the health of the datacenter resources (server performance, disks, databases). Workloads like client connectivity and mail flow have a higher priority as they have direct impact on user functionality. Mailbox migration is not considered as vital because things like mailbox moves are online moves (functionality is not impacted and access to mailboxes is mostly uninterrupted). Please go through this article for more info.
In contrast, for EWS migrations (these would be migrations using 3rd party tools), there is throttling in place on the Office 365 side (and this can be temporarily lifted for migration if necessary, by going to the the Need Help widget in the Microsoft 365 portal.) Native migration tools to Exchange Online do not use EWS.
Best would be to fix the source network latency which is commonly caused by on-premised network devices in front of the Exchange servers. You can try to temporarily bypass your reverse proxy or load balancer and see if you still have high final sync times that can lock the source mailbox for long periods of time. If this is not feasible, you can then try to skip the content verification above, as a quick workaround.
File descriptors are operating system resources used to represent connections and open files, among other things. NGINX can use up to two file descriptors per connection. For example, if NGINX is proxying, it generally uses one file descriptor for the client connection and another for the connection to the proxied server, though this ratio is much lower if HTTP keepalives are used. For a system serving a large number of connections, the following settings might need to be adjusted:
Keepalive connections can have a major impact on performance by reducing the CPU and network overhead needed to open and close connections. NGINX terminates all client connections and creates separate and independent connections to the upstream servers. NGINX supports keepalives for both clients and upstream servers. The following directives relate to client keepalives:
If caching is not enabled and throttling limits have not been applied, then all requests will pass through to your backend service until the account level throttling limits are reached. If throttling limits are in place, then Amazon API Gateway will shed the necessary amount of requests and send only the defined limit to your back-end service. If a cache is configured, then Amazon API Gateway will return a cached response for duplicate requests for a customizable time, but only if under configured throttling limits. This balance between the backend and client ensures optimal performance of the APIs for the applications that it supports. Requests that are throttled will be automatically retried by the client-side SDKs generated by Amazon API Gateway. By default, Amazon API Gateway does not set any cache on your API methods.
Amazon API Gateway acts as a proxy to the backend operations that you have configured. Amazon API Gateway will automatically scale to handle the amount of traffic your API receives. Amazon API Gateway does not arbitrarily limit or throttle invocations to your backend operations and all requests that are not intercepted by throttling and caching settings in the Amazon API Gateway console are sent to your backend operations.
Adafruit IO's MQTT server imposes a rate limit to prevent excessive load on the service. If a user performs too many publish actions in a short period of time then some of the requests will be rejected and an error message will be published on your /throttle topic. The current rate limit is at most 30 requests per minute for free accounts, 60 per minute with an IO+ account, and expandable via Adafruit IO+ Boost applied to your account.
We also limit a few other actions on Adafruit IO's MQTT broker. If you send, within a minute, more than: 100 MQTT SUBSCRIBE requests, 10 failed MQTT SUBSCRIBE requests, or 10 failed MQTT PUBLISH requests; or you send enough messages after passing the rate limit; or you attempt to log in more than 20 times within a minute, your account will be temporarily banned from the MQTT broker.
If you exceed a rate limit, a notice will be sent to the (username)/throttle topic. If your account is temporarily banned, a notice will be sent to the username/errors topic. While developing your project, you should always keep subscriptions to the error topics open so you can see when you're getting close to the Adafruit IO rate limits and when you've been blocked.
This topic publishes throttle warning and error messages to all subscribed clients. We do our best to provide an accurate accounting in the error message of the minimum amount of time you should wait before attempting to publish again.
Defines the idle timeout for remote commands, applying a limit to how long the operation is allowed to execute without either producing output or consuming input. The default value is 61 minutes. This value should be greater than the throttle.resource.mirror-hosting.timeout value set on the upstream.
Additional resource types may be configured by defining a key with the format 'throttle.resource.'. When adding new types, it is strongly recommended to configure their ticket counts explicitly using this approach.
Controls how long threads will wait for Git LFS uploads/downloads to complete when the system is already running the maximum number of concurrent transfers. It is recommended this be set to zero (i.e. don't block) or a few seconds at most. Since waiters still hold a connection, a non-zero wait time defeats the purpose of this throttle.
If 'adaptive' is specified, the maximum number of hosting operations will vary between throttle.resource.scm-hosting.adaptive.min and throttle.resource.scm-hosting.adaptive.max based on how many hosting operations the system believes the machine can support in its current state and given past performance.
If any configured adaptive throttling setting is invalid and reverts to a default but this conflicts with other correctly configured or default settings, the throttling strategy will revert to 'fixed'. E.g. this will occur if throttle.resource.scm-hosting.adaptive.min is set to the same value as throttle.resource.scm-hosting.adaptive.max
Limits the number of ref. advertisement operations, which may be running concurrently. Those are throttled separately from clone operations as they are much more lightweight and much shorter so we can and should allow many more of them running concurrently.
3DES only provides an effective security of 112 bits. You can use this policy to temporarily retain compatibility with an outdated server by enabling 3DES cipher suites in TLS. This is a stopgap measure only and the server must be reconfigured.
You can temporarily revert Chrome browser to the legacy behavior, which is less secure. That way, users can continue to use services that developers have not yet updated, such as single sign-on and internal applications.
Web Components v0 APIs (Shadow DOM v0, Custom Elements v0, and HTML Imports) were deprecated in 2018. They are disabled by default in Chrome version 80 and later. For Chrome browser and devices running Chrome OS version 80 to 84 inclusive, select Re-enable Web Components v0 API to temporarily re-enable the APIs for all sites.
For Chrome browser and devices running Chrome OS version 83 and 84, select Use legacy (pre-M81) form control element for all sites to temporarily revert to legacy form control elements. Otherwise, updated form control elements are used as they are launched in Chrome versions 83 and 84.
The degree of throttling is a linear function of recv queue size and goes from 1.0 (full rate)at gcs.recv_q_soft_limit to gcs.max_throttle at gcs.recv_q_hard_limit Note that full rate, as estimated between 0 and gcs.recv_q_soft_limit is a very imprecise estimate of a regular replication rate.
The actual write rate established by thethrottling is the minimum of this value and the value that the regular throttlecalculation produces, i.e. this option can be used to set a fixed upper boundon the write rate.
Maximum throttle in KiBs per second, per delivery thread. This will bereduced proportionally to the number of nodes in the cluster. (If thereare two nodes in the cluster, each delivery thread will use the maximumrate; if there are three, each will throttle to half of the maximum,since we expect two nodes to be delivering hints simultaneously.)Min unit: KiB
Memtable flushing is more CPU efficient than memtable ingest and a single threadcan keep up with the ingest rate of a whole server on a single fast diskuntil it temporarily becomes IO bound under contention typically with compaction.At that point you need multiple flush threads. At some point in the futureit may become CPU bound all the time.