diff options
| -rw-r--r-- | doc/Load_Balancing.md | 23 |
1 files changed, 17 insertions, 6 deletions
diff --git a/doc/Load_Balancing.md b/doc/Load_Balancing.md index 1d508fa0..a348ebc4 100644 --- a/doc/Load_Balancing.md +++ b/doc/Load_Balancing.md @@ -42,7 +42,7 @@ The ring construction that selects `n_token` random positions for each nodes giv 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 second method for partitionning our dataset: +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, @@ -50,7 +50,9 @@ To solve this, we want to apply a second method for partitionning our dataset: proportionnal to its capacity (which `n_tokens` represented in the first method) -I have studied two ways to do the attribution, in a way that is deterministic: +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/) @@ -67,7 +69,7 @@ for large values), however in both cases: - 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: +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 @@ -143,12 +145,21 @@ 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 custom method +on average: 62.94% 27.89% 8.61% 0.57% <-- WORSE THAN PREVIOUSLY ``` #### The magical solution: multi-DC aware MagLev -(insert algorithm description here, in the meantime refer to `method4` in the simulation script) +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 ##### @@ -180,5 +191,5 @@ 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 +on average: 64.94% 33.33% 1.72% 0.01% <-- VERY GOOD (VERY LOW VALUES FOR 2 AND 3 NODES) ``` |