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Women are more likely to develop fibromyalgia than are men. Many people who have fibromyalgia also have tension headaches, temporomandibular joint TMJ disorders, irritable bowel syndrome, anxiety and depression. While there is no cure for fibromyalgia, a variety of medications can help control symptoms. Exercise, relaxation and stress-reduction measures also may help.

Doctors don't know what causes fibromyalgia, but it most likely involves a variety of factors working together. These may include:. Researchers believe repeated nerve stimulation causes the brains of people with fibromyalgia to change. This change involves an abnormal increase in levels of certain chemicals in the brain that signal pain neurotransmitters. In addition, the brain's pain receptors seem to develop a sort of memory of the pain and become more sensitive, meaning they can overreact to pain signals. The pain and lack of sleep associated with fibromyalgia can interfere with your ability to function at home or on the job.

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The frustration of dealing with an often-misunderstood condition also can result in depression and health-related anxiety. Mayo Clinic does not endorse companies or products. Advertising revenue supports our not-for-profit mission. This content does not have an English version. This content does not have an Arabic version. Overview Fibromyalgia is a disorder characterized by widespread musculoskeletal pain accompanied by fatigue, sleep, memory and mood issues.

More Information Fibromyalgia or not? Fibromyalgia: Linked to other health problems? Request an Appointment at Mayo Clinic.


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References Fibromyalgia. Similar to the concept of RAID levels 4, 5, 6, etc. In the case of DSF, the data block is an extent group and each data block must be on a different node and belong to a different vDisk. The number of data and parity blocks in a strip is configurable based upon the desired failures to tolerate. Pre-existing EC containers will not immediately change to block aware placement after being upgraded to 5.

New EC containers will build block aware EC strips. This eliminates any computation overhead on reads once the strips have been rebuilt automated via Curator. The previous table follows this best practice. The encoding is done post-process and leverages the Curator MapReduce framework for task distribution. In this scenario, we have a mix of both RF2 and RF3 data whose primary copies are local and replicas are distributed to other nodes throughout the cluster. When a Curator full scan runs, it will find eligible extent groups which are available to become encoded.

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Eligible extent groups must be "write-cold" meaning they haven't been written to for awhile. After the eligible candidates are found, the encoding tasks will be distributed and throttled via Chronos. Once the data has been successfully encoded strips and parity calculation , the replica extent groups are then removed. Erasure Coding pairs perfectly with inline compression which will add to the storage savings. Currently compression is one of the key features of the COE to perform data optimization.

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This includes data draining from OpLog as well as sequential data skipping it. This will allow for a more efficient utilization of the OpLog capacity and help drive sustained performance. When drained from OpLog to the Extent Store the data will be decompressed, aligned and then re-compressed at a 32K aligned unit size as of 5. Offline compression will initially write the data as normal in an un-compressed state and then leverage the Curator framework to compress the data cluster wide. Prior to AOS 5. Normal data will be compressed using LZ4 which provides a very good blend between compression and performance.

For cold data, LZ4HC will be leveraged to provide an improved compression ratio. This will also increase the usable size of the SSD tier increasing effective performance and allowing more data to sit in the SSD tier. Also, for larger or sequential data that is written and compressed inline, the replication for RF will be shipping the compressed data, further increasing performance since it is sending less data across the wire.

After the compression delay configurable is met, the data is eligible to become compressed. Compression can occur anywhere in the Extent Store. Offline compression uses the Curator MapReduce framework and all nodes will perform compression tasks. Compression tasks will be throttled by Chronos. Deduplicated data is pulled into the unified cache at a 4K granularity. Contrary to traditional approaches which utilize background scans requiring the data to be re-read, Nutanix performs the fingerprint inline on ingest. For duplicate data that can be deduplicated in the capacity tier, the data does not need to be scanned or re-read, essentially duplicate copies can be removed.

To make the metadata overhead more efficient, fingerprint refcounts are monitored to track dedupability.

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Fingerprints with low refcounts will be discarded to minimize the metadata overhead. To minimize fragmentation full extents will be preferred for capacity tier deduplication. In most other cases compression will yield the highest capacity savings and should be used instead. In cases where fingerprinting is not done during ingest e. As duplicate data is determined, based upon multiple copies of the same fingerprints, a background process will remove the duplicate data using the DSF MapReduce framework Curator.

Any subsequent requests for data having the same fingerprint will be pulled directly from the cache. Prior to 4. This was done to maintain a smaller metadata footprint and since the OS is normally the most common data.

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  • However, unless the data is dedupable conditions explained earlier in section , stick with compression. The Disk Balancing section above talked about how storage capacity was pooled among all nodes in a Nutanix cluster and that ILM would be used to keep hot data local. The SSD tier will always offer the highest performance and is a very important thing to manage for hybrid arrays.

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    Specific types of resources e. This means that any node within the cluster can leverage the full tier capacity, regardless if it is local or not. As mentioned in the Disk Balancing section, a key concept is trying to keep uniform utilization of devices within disk tiers.

    This will free up space on the local SSD to allow the local node to write to SSD locally instead of going over the network. The data for down-migration is chosen using last access time. DSF is designed to be a very dynamic platform which can react to various workloads as well as allow heterogeneous node types: compute heavy , etc.