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diff --git a/content/documentation/working_documents/load_balancing.md b/content/documentation/working_documents/load_balancing.md new file mode 100644 index 0000000..99271ad --- /dev/null +++ b/content/documentation/working_documents/load_balancing.md @@ -0,0 +1,199 @@ +# 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) +``` |