Hello, I would like to discuss a generalized data "temperature" estimation framework. [PROBLEM DECLARATION] Efficient data placement policy is a Holy Grail for data storage and file system engineers. Achieving this goal is equally important and really hard. Multiple data storage and file system technologies have been invented to manage the data placement policy (for example, COW, ZNS, FDP, etc). But these technologies still require the hints related to nature of data from application side. [DATA "TEMPERATURE" CONCEPT] One of the widely used and intuitively clear idea of data nature definition is data "temperature" (cold, warm, hot data). However, data "temperature" is as intuitively sound as illusive definition of data nature. Generally speaking, thermodynamics defines temperature as a way to estimate the average kinetic energy of vibrating atoms in a substance. But we cannot see a direct analogy between data "temperature" and temperature in physics because data is not something that has kinetic energy. [WHAT IS GENERALIZED DATA "TEMPERATURE" ESTIMATION] We usually imply that if some data is updated more frequently, then such data is more hot than other one. But, it is possible to see several problems here: (1) How can we estimate the data "hotness" in quantitative way? (2) We can state that data is "hot" after some number of updates. It means that this definition implies state of the data in the past. Will this data continue to be "hot" in the future? Generally speaking, the crucial problem is how to define the data nature or data "temperature" in the future. Because, this knowledge is the fundamental basis for elaboration an efficient data placement policy. Generalized data "temperature" estimation framework suggests the way to define a future state of the data and the basis for quantitative measurement of data "temperature". [ARCHITECTURE OF FRAMEWORK] Usually, file system has a page cache for every inode. And initially memory pages become dirty in page cache. Finally, dirty pages will be sent to storage device. Technically speaking, the number of dirty pages in a particular page cache is the quantitative measurement of current "hotness" of a file. But number of dirty pages is still not stable basis for quantitative measurement of data "temperature". It is possible to suggest of using the total number of logical blocks in a file as a unit of one degree of data "temperature". As a result, if the whole file was updated several times, then "temperature" of the file has been increased for several degrees. And if the file is under continous updates, then the file "temperature" is growing. We need to keep not only current number of dirty pages, but also the number of updated pages in the near past for accumulating the total "temperature" of a file. Generally speaking, total number of updated pages in the nearest past defines the aggregated "temperature" of file. And number of dirty pages defines the delta of "temperature" growth for current update operation. This approach defines the mechanism of "temperature" growth. But if we have no more updates for the file, then "temperature" needs to decrease. Starting and ending timestamps of update operation can work as a basis for decreasing "temperature" of a file. If we know the number of updated logical blocks of the file, then we can divide the duration of update operation on number of updated logical blocks. As a result, this is the way to define a time duration per one logical block. By means of multiplying this value (time duration per one logical block) on total number of logical blocks in file, we can calculate the time duration of "temperature" decreasing for one degree. Finally, the operation of division the time range (between end of last update operation and begin of new update operation) on the time duration of "temperature" decreasing for one degree provides the way to define how many degrees should be subtracted from current "temperature" of the file. [HOW TO USE THE APPROACH] The lifetime of data "temperature" value for a file can be explained by steps: (1) iget() method sets the data "temperature" object; (2) folio_account_dirtied() method accounts the number of dirty memory pages and tries to estimate the current temperature of the file; (3) folio_clear_dirty_for_io() decrease number of dirty memory pages and increases number of updated pages; (4) folio_account_dirtied() also decreases file's "temperature" if updates hasn't happened some time; (5) file system can get file's temperature and to share the hint with block layer; (6) inode eviction method removes and free the data "temperature" object. Thanks, Slava.
