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diff --git a/content/documentation/working_documents/load_balancing.md b/content/documentation/working_documents/load_balancing.md deleted file mode 100644 index e6fa3e8..0000000 --- a/content/documentation/working_documents/load_balancing.md +++ /dev/null @@ -1,204 +0,0 @@ -+++ -title="Doc Post" -date=2018-08-20 -+++ - -# Load Balancing Data (planned for version 0.2) - -**This is being yet improved in release 0.5. The working document has not been updated yet, it still only applies to Garage 0.2 through 0.4.** - -I have conducted a quick study of different methods to load-balance data over different Garage nodes using consistent hashing. - -## Requirements - -- *good balancing*: two nodes that have the same announced capacity should receive close to the same number of items - -- *multi-datacenter*: the replicas of a partition should be distributed over as many datacenters as possible - -- *minimal disruption*: when adding or removing a node, as few partitions as possible should have to move around - -- *order-agnostic*: the same set of nodes (each associated with a datacenter name - and a capacity) should always return the same distribution of partition - replicas, independently of the order in which nodes were added/removed (this - is to keep the implementation simple) - -## Methods - -### Naive multi-DC ring walking strategy - -This strategy can be used with any ring-like algorithm to make it aware of the *multi-datacenter* requirement: - -In this method, the ring is a list of positions, each associated with a single node in the cluster. -Partitions contain all the keys between two consecutive items of the ring. -To find the nodes that store replicas of a given partition: - -- select the node for the position of the partition's lower bound -- go clockwise on the ring, skipping nodes that: - - we halve already selected - - are in a datacenter of a node we have selected, except if we already have nodes from all possible datacenters - -In this way the selected nodes will always be distributed over -`min(n_datacenters, n_replicas)` different datacenters, which is the best we -can do. - -This method was implemented in the first version of Garage, with the basic -ring construction from Dynamo DB that consists in associating `n_token` random positions to -each node (I know it's not optimal, the Dynamo paper already studies this). - -### Better rings - -The ring construction that selects `n_token` random positions for each nodes gives a ring of positions that -is not well-balanced: the space between the tokens varies a lot, and some partitions are thus bigger than others. -This problem was demonstrated in the original Dynamo DB paper. - -To solve this, we want to apply a better second method for partitionning our dataset: - -1. fix an initially large number of partitions (say 1024) with evenly-spaced delimiters, - -2. attribute each partition randomly to a node, with a probability - proportionnal to its capacity (which `n_tokens` represented in the first - method) - -For now we continue using the multi-DC ring walking described above. - -I have studied two ways to do the attribution of partitions to nodes, in a way that is deterministic: - -- Min-hash: for each partition, select node that minimizes `hash(node, partition_number)` -- MagLev: see [here](https://blog.acolyer.org/2016/03/21/maglev-a-fast-and-reliable-software-network-load-balancer/) - -MagLev provided significantly better balancing, as it guarantees that the exact -same number of partitions is attributed to all nodes that have the same -capacity (and that this number is proportionnal to the node's capacity, except -for large values), however in both cases: - -- the distribution is still bad, because we use the naive multi-DC ring walking - that behaves strangely due to interactions between consecutive positions on - the ring - -- the disruption in case of adding/removing a node is not as low as it can be, - as we show with the following method. - -A quick description of MagLev (backend = node, lookup table = ring): - -> The basic idea of Maglev hashing is to assign a preference list of all the -> lookup table positions to each backend. Then all the backends take turns -> filling their most-preferred table positions that are still empty, until the -> lookup table is completely filled in. Hence, Maglev hashing gives an almost -> equal share of the lookup table to each of the backends. Heterogeneous -> backend weights can be achieved by altering the relative frequency of the -> backends’ turns… - -Here are some stats (run `scripts/simulate_ring.py` to reproduce): - -``` -##### Custom-ring (min-hash) ##### - -#partitions per node (capacity in parenthesis): -- datura (8) : 227 -- digitale (8) : 351 -- drosera (8) : 259 -- geant (16) : 476 -- gipsie (16) : 410 -- io (16) : 495 -- isou (8) : 231 -- mini (4) : 149 -- mixi (4) : 188 -- modi (4) : 127 -- moxi (4) : 159 - -Variance of load distribution for load normalized to intra-class mean -(a class being the set of nodes with the same announced capacity): 2.18% <-- REALLY BAD - -Disruption when removing nodes (partitions moved on 0/1/2/3 nodes): -removing atuin digitale : 63.09% 30.18% 6.64% 0.10% -removing atuin drosera : 72.36% 23.44% 4.10% 0.10% -removing atuin datura : 73.24% 21.48% 5.18% 0.10% -removing jupiter io : 48.34% 38.48% 12.30% 0.88% -removing jupiter isou : 74.12% 19.73% 6.05% 0.10% -removing grog mini : 84.47% 12.40% 2.93% 0.20% -removing grog mixi : 80.76% 16.60% 2.64% 0.00% -removing grog moxi : 83.59% 14.06% 2.34% 0.00% -removing grog modi : 87.01% 11.43% 1.46% 0.10% -removing grisou geant : 48.24% 37.40% 13.67% 0.68% -removing grisou gipsie : 53.03% 33.59% 13.09% 0.29% -on average: 69.84% 23.53% 6.40% 0.23% <-- COULD BE BETTER - --------- - -##### MagLev ##### - -#partitions per node: -- datura (8) : 273 -- digitale (8) : 256 -- drosera (8) : 267 -- geant (16) : 452 -- gipsie (16) : 427 -- io (16) : 483 -- isou (8) : 272 -- mini (4) : 184 -- mixi (4) : 160 -- modi (4) : 144 -- moxi (4) : 154 - -Variance of load distribution: 0.37% <-- Already much better, but not optimal - -Disruption when removing nodes (partitions moved on 0/1/2/3 nodes): -removing atuin digitale : 62.60% 29.20% 7.91% 0.29% -removing atuin drosera : 65.92% 26.56% 7.23% 0.29% -removing atuin datura : 63.96% 27.83% 7.71% 0.49% -removing jupiter io : 44.63% 40.33% 14.06% 0.98% -removing jupiter isou : 63.38% 27.25% 8.98% 0.39% -removing grog mini : 72.46% 21.00% 6.35% 0.20% -removing grog mixi : 72.95% 22.46% 4.39% 0.20% -removing grog moxi : 74.22% 20.61% 4.98% 0.20% -removing grog modi : 75.98% 18.36% 5.27% 0.39% -removing grisou geant : 46.97% 36.62% 15.04% 1.37% -removing grisou gipsie : 49.22% 36.52% 12.79% 1.46% -on average: 62.94% 27.89% 8.61% 0.57% <-- WORSE THAN PREVIOUSLY -``` - -### The magical solution: multi-DC aware MagLev - -Suppose we want to select three replicas for each partition (this is what we do in our simulation and in most Garage deployments). -We apply MagLev three times consecutively, one for each replica selection. -The first time is pretty much the same as normal MagLev, but for the following times, when a node runs through its preference -list to select a partition to replicate, we skip partitions for which adding this node would not bring datacenter-diversity. -More precisely, we skip a partition in the preference list if: - -- the node already replicates the partition (from one of the previous rounds of MagLev) -- the node is in a datacenter where a node already replicates the partition and there are other datacenters available - -Refer to `method4` in the simulation script for a formal definition. - -``` -##### Multi-DC aware MagLev ##### - -#partitions per node: -- datura (8) : 268 <-- NODES WITH THE SAME CAPACITY -- digitale (8) : 267 HAVE THE SAME NUM OF PARTITIONS -- drosera (8) : 267 (+- 1) -- geant (16) : 470 -- gipsie (16) : 472 -- io (16) : 516 -- isou (8) : 268 -- mini (4) : 136 -- mixi (4) : 136 -- modi (4) : 136 -- moxi (4) : 136 - -Variance of load distribution: 0.06% <-- CAN'T DO BETTER THAN THIS - -Disruption when removing nodes (partitions moved on 0/1/2/3 nodes): -removing atuin digitale : 65.72% 33.01% 1.27% 0.00% -removing atuin drosera : 64.65% 33.89% 1.37% 0.10% -removing atuin datura : 66.11% 32.62% 1.27% 0.00% -removing jupiter io : 42.97% 53.42% 3.61% 0.00% -removing jupiter isou : 66.11% 32.32% 1.56% 0.00% -removing grog mini : 80.47% 18.85% 0.68% 0.00% -removing grog mixi : 80.27% 18.85% 0.88% 0.00% -removing grog moxi : 80.18% 19.04% 0.78% 0.00% -removing grog modi : 79.69% 19.92% 0.39% 0.00% -removing grisou geant : 44.63% 52.15% 3.22% 0.00% -removing grisou gipsie : 43.55% 52.54% 3.91% 0.00% -on average: 64.94% 33.33% 1.72% 0.01% <-- VERY GOOD (VERY LOW VALUES FOR 2 AND 3 NODES) -``` |