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Comment by scotty79 | Hacker News Reader

> This is for no good reason other than just luck.

There is a good reason. And that reason is that many recently accessed items survived. Then it's only prudent to devote more of the cache for surviving items than for fresh ones in such circumstances.

This continues until the cache is completely filled with recent survivors which moves the hand back to tail and oldest previous survivors get reevaluated for eviction. This, I imagine happens periodically in practice and often enough for the algorithm to perform well.

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The problem is that the more the cache is filled, and the closer the Hand gets to the head of the queue, the closer are newly inserted items to being evicted.

There doesn't seem to be any good reason for this over putting the newly inserted items at a consistent distance away from the Hand, so they are a predictable, controllable number of steps away from the threat of eviction, and so that the Hand movement is just a cyclic treadmill with no spot of the circular buffer being arbitrarily special.

If randomization of the distance is beneficial, let that be explicit! Feed a stream of pseudo-random integers to the cache which is used to select the distance between the Hand and the Insert point. That could be still be nicely controlled as a jitter within a minimum and maximum range.

> ... the closer are newly inserted items to being evicted.

That's a good thing because newly inserted items didn't prove yet that they are worth keeping.

They have less time to proove themselves but if they prove themselves they can have a long lifetime because they will remain when the hand loops back to tail.

End of cache is filled with items of proven utility. New ones are unproven. Shrinking distance between head and hand reflect accumulation of proven items which leaves less space for unproven ones. Which is good. Eventually (when the space for fresh ones ends) you want to reconsider the proven ones again and this is achieved by moving hand back from head to tail.

It might be unfair to new items comming in at the times when there are a lot of proven ones accumulated but it is a good strategy from the point of view of the whole process. Good solutions are not necessarily fair.

> If randomization of the distance is beneficial, let that be explicit!

But it's better than random. It's adaptive to how many of the items already stored were visited recently.

> That's a good thing because newly inserted items didn't prove yet that they are worth keeping.

It isn't good that the probation is random, depending on how far left the Hand is at the time the item is inserted. Or else if it is good for that distance to be randomized, it would be good to make that random choice explicit.

> It's adaptive to how many of the items already stored were visited recently.

It's not adaptive whatsoever. The Hand moves left when there are cache misses, in order to find items to evict, and then wraps back to the rightmost slot. The current position of the Hand bears no relation to a newly observed cache miss, yet affects its retention.

You are probably thinking of block I/O or CPU cache workloads where scans cause the miss ratio to increase. This algorithm targets web workloads where scans are not common, and the miss ratio does not change abruptly.

Yes, SIEVE does not adapt to miss ratio, but it adapts to the inherent popularity in the workloads. You can see the evidence in the results.

> The Hand moves left when there are cache misses

It moves only if there were cache hits. If there are only cache misses it stays at the tail so the whole cache is dedicated for new unproven entries. Once there are some cache hits then they are retained and the area shrinks. It's not random. It reacts to whether it managed to catch something valuable or not.

> The current position of the Hand bears no relation to a newly observed cache miss, yet affects its retention.

Yes, but it is related to recent cache hits. It sacrifices chances of new items getting retained in exchange for retaining items that were recently proven to be worth keeping.

When there is a cache hit, the accessed item is flipped to visited state (if necessary). No eviction takes place and so the Hand stays where it is.

The Hand moves when it has to scan over visited items in search of an unvisited item to evict, when there is a cache miss. The scanned items are flipped to unvisited.

Exactly. And if there are no visited items, evictions happen at hand and hand is at tail and doesn't move. Full cache is dedicated to finding items that will repeat. They are getting the best chance they can.

Once some visited items arrive they are retained and the fraction of cache dedicated to new ones shrinks, so they have less time to get visited again and avoid eviction. But this happens for the benefit of retained items. If they were already seen second time there's a chance they'll occur for the third time and more. They are worth more than fresh ones. It's unfair to new items but beneficial for the cache as a whole.

When many different items are revisited hand moves and eventually reaches the head. Then the cache is full. So it's moved back to tail to see if we can evict some of the old stuff because maybe they weren't re-visted since last time we considered them for eviction.

Can you see it's not random and that it's actually useful?

The algorithm is arbitrarily unfair towards new items based on its state, which has no relation to those items.

It's like being rude to customers whenever the day of year is divisible by five. Sure it's not mathematically even pseudo-random, but it's unrelated to the customers. It's effectively random to them.

It's rude to new customers when restaurant is already full of paying customers.

It's unrelated to the new customers and in that sense unfair to them. But it's related to how successful is the restaurant at the moment.

Fairness is not the goal. Success of the restaurant is.

Objects near the tail are the people who occupy the table but keep ordering new dishes. Objects near the head are people who ordered just one thing and now they are politely asked to leave so new, potentially more generous customers can be accomodated.

If restaurant is more full, new customers have less time to justify their continued occupation of the table by ordering again, but it's fine because it allowes more of the older customers with repeated orders to be served again and again.

There is potential problem with the algorithm if there's very little space left for new items so they are never retained so hand never moves to head. Probaby you could refine the algorithm by moving hand to tail when it gets closer to head than some fixed value, finetuned to the production load. Or just occasionally move hand to tail every n inputs on average (also finetuned).

You're still on a mistaken program whereby because items are inserted at the leftmost slot of the array, that slot is the head. And so the rightmost slot is the tail.

This is not so. The tail of the buffer is the Hand pointer, and it behaves like a circular buffer tail.

This is at odds with the fixed head.

Your full versus empty analysis is flawed, because once the cache warms up, which takes only one cycle, it stays permanently full. At each subsequent cache miss, one item is evicted and one item is inserted. One in, one out. There is no tide-like ebb and flow between empty and full. That is just rhetoric which is not anchored to anything that is real in the algorithm.

Items that are booted out are identified at the circular buffer tail. But, oddly, the new ones are inserted at the physical top of the circular buffer's linear implementation which bears no relation to the circularity.

In a circular buffer, the linear indices have no special meaning.

So it is like a code smell, but for algorithms.

You can look at this cache as circular buffer. But in this interpretation there is not one pointer into that buffer, but two. Insertion pointer and eviction pointer. Their location in memory is irrelevant. What matters is that eviction pointer moves while insertion point doesn't.

Those two pointers split the circular buffer inti two parts. The part after insertion pointer but before eviction pointer you could call staging. It's a space where items that have not yet been considered for eviction are held. When an item gets evicted items in this staging space are moved one spot towards eviction pointer so that under insertion pointer a free space appears which is filled by the newly inserted item.

As items are considered for eviction and rejected (because they had visited status) eviction pointer moves closer and closer to insertion pointer shrinking the staging area. So the new items have less and less time to acquire visited status before the eviction pointer reaches them. This is what's unfair. That the chances of item getting visited and spared from eviction is lower for the items that arrive when staging is small.

This is somewhat alleviated by the fact that after eviction pointer in its motion reaches stationary insertion pointer it passes it so the staging area grows from almost nothing to the full size of the circular buffer. The items which had a bad luck to arrive when the staging is small, if they are repeated, have a later chance to arrive again when staging is large and then have a lot of time to earn visted status and avoid eviction the next time they are considered.

What you are advocating for is that insertion pointer should move too, either to keep fixed distance from the eviction pointer or to keep explicitly randomized distance. Those are of course valid choices when you design your cache algorithm. But the author(s) of this one went with another method. They reduce the distance between insertion and eviction pointer by one only after item is considered for eviction but rejected (because it got visited status since the time it was inserted or considered for eviction the previous time). Once the distance between insertion and eviction point reaches small enough value (for example zero) they increase the distance to be maximum possible (to the size of the circular buffer).

You don't see a reason for this choice and I'm telling you that the reason is that the second part of the buffer (the part starting at eviction point and ending at insertion point) is devoted exclusively to items that since their insertion acquired visited status at least once thus prooving they are worth keeping in cache.

So it might be a good strategy to dedicate larger part of the buffer to those items in times when they are abundant in the input rather than fixed part or random part as you propose.

To address your concern that new items are inserted at the physical top of circular buffer implementation ... It doesn't matter. Insertion pointer could be placed at any spot in the buffer. You can randomize its initial (and ultimate since its not moving) placement and it would change nothing in the performance of the algorithm (after the buffer is initially filled). This cache could be implemented as circular linked list with two pointers into it.

Do you understand all that? Because I'm not sure if I can explain it any more clearly.

> You can look at this cache as circular buffer.

After that, you cannot "unsee" it any more.

> items that since their insertion acquired visited status at least once

> the second part of the buffer (the part starting at eviction point and ending at insertion point) is devoted exclusively to items that since their insertion acquired visited status at least once thus prooving they are worth keeping in cache.

Items can acquire visited status anywhere in the cache, to the left or right of the Hand pointer. There is no special area.

The "reaper" which ages visited items to unvisited, and which collects aged items, moves in a circle.

If an item is inserted while the Hand is in the leftmost position, it will be evicted immediately when another not-present item is accessed. If an item is inserted while the Hand is farthest away at the rightmost slot, and not visited again, it can stay in the cache for a number of miss cycles, until the Hand goes to the top again.

That variability, as such, may be useful and good.

One problem is that it's not actually random, and it's not even a good form of pseudo-random.

I posit that input sequences exist in which two items of equal importance, A and B, are consistently treated unequally, with B enjoying much longer retention in the cache than A.

Moreover, the algorithm doesn't allow for experimentation with randomly varying the trim distance between two arbitrary values. Like say random between 4 and 8 slots in a 64 slot cache. That's a disadvantage, because for some given patterns, it prevents the optimal parameters from being discoverable.

Algorithms like LRU don't have the above weird property. While their hit rate may be good or bad depending on the locality of patterns, you cannot construe a sequence of access patterns under LRU in which two items A and B appear equally frequently, yet enjoy significantly different retention times.

> Items can acquire visited status anywhere in the cache, to the left or right of the Hand pointer. There is no special area.

They can lose visited status only if they are between head and hand pointer. That's the special area. There's no way for item to loose their visited status if they are between hand and tail until the hand reaches head. In this sense area between hand and tail is privileged.

> If an item is inserted while the Hand is in the leftmost position, it will be evicted immediately when another not-present item is accessed.

You see the cost of small head-hand area but you don't see the benefit of large hand-tail area.

> Moreover, the algorithm doesn't allow for experimentation with randomly varying the trim distance between two arbitrary values.

In its basic form it doesn't. However I suggested that you might tweak how far from the head, the hand loops back to tail. It doesn't have to happen at exactly zero distance if you don't want to ever disadvantage new entries too much.

> .. construe a sequence of access patterns under LRU in which two items A and B appear equally frequently, yet enjoy significantly different retention times.

I see no cost of that. Cache doesn't need to be fair. It only needs to be successful. If it can serve item A from cache for 100% of the time and item B 0% of the time despite same patterns its still better than if it serves each of them 25% of time from cache.

> its still better than

* is not worse than

Yeah... It's hard not to read "luck" as, "there's something I don't understand". Luck doesn't really happen with caching.

It's not hard to see that in this algorithm when a new cache miss is handled to insert an item, the current position of the Hand bears no relation to that new item.

Sure, we can contrive access sequences where that is not random, because the whole thing is actually deterministic. That's not what I mean by random; I mean not expected to be related under realistic conditions where the inputs aren't contrived.

A good cache algorithm should be immune against contrived input sequences. With this one, we could devise a degenerate access sequence such that a particular item is always placed just ahead of the Hand, such that it is victimized in the next access cycle. Yet another particular item is given the maximum leeway. And that could be in spite of both items appearing equally often and deserving the same treatment.

Unfortunately most researchers do not consider attack vectors and often consider inputs that do not meet their preferred examples to be invalid. If a hostile user is able to pollute the cache so that it significantly degrades then that exploit becomes an easy denial of service attack. It is somewhat out of scope, but means that more general robust algorithms are needed because most users won't fine tune a system for a bespoke algorithm and instead take an off-the-shelf solution. I am therefore not a big fan of these minor tweaks to historical algorithms by making a one or two line change and therefore only slightly modifying what it is good or bad at. I try for more holistic solutions, even if a small percent worse in some cases, and infuse randomness to avoid deterministic exploits. To me that's more worthwhile and a big factor in whether it is a research toy or production-grade engineering.

I agree with you; any non-random algorithm has the problem

Well, any pseudo-random algorithm. If we avoid pseudo-random behaviors, we can provide certain guarantees that no pattern of access can get around, such as that if an item is freshly inserted into the cache, then it will stay there for (at least) the next N cache misses, for some specific N; that those N misses will evict something else.

If we consider LRU, no contrived pattern of access on LRU will cause it to evict some item Y if there is an item Z present which was less recently touched than Y.

Those guarantees are also what makes the algorithm exploitable and inflexible to handle unknown or adversary workloads. Your LRU example suffers in scan / loop / zigzag patterns, which the author's policies do poorly at as well. They target CDNs workloads where that can be ignored there due to its scale, but that is not a generalizable assumption for other workloads where it more common like databases, search, and analytics.

If the algorithm is known and accepts user facing requests then it becomes a target. A client can use the network speed of a dialup modem to bring down a modern server simply by crafting a request pattern to exploit hash flooding. This is why you generally want some injected noise, adaptivity, and use an algorithm that offers robustly good hit rates. A specialized policy that is superior in a narrow set of workload is still very useful when targeted at an expensive problem, but that's a design assumption that few can reasonably make.

Do you have an example of a non-random algorithm that does not have adversarial workloads?

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