What is thrashing and how can you optimize memory management algorithms to avoid it?

The term thrashing is a generic term which is not limited to memory management, although as indicated above it can be related to memory handling. It can also apply to other forms of system context changes. This is where the context change is proportionally not economic. This could apply to things such high device Input/Output where the handling of the IO becomes a high overhead compared to the actual act of input/output, these are challenges that virtualisation often need to manage and perform well

Thrashing is the excessively and inefficiently use of computer resource swap between Physical memory and virtual memory on a SSD drive.

Thrashing in the context of computing refers to a situation where excessive paging or swapping activity occurs in a virtual memory system, leading to a significant slowdown in system performance. This typically happens when a system is overburdened with too many active processes, each requiring more memory than is available. As a result, the system spends more time moving data between RAM and the disk (paging) than executing actual processes, severely degrading overall system efficiency.

Thrashing in computer systems occurs when the demand for memory exceeds what's available, leading to constant swapping of data between the main memory and storage. This results in a high rate of page faults and significantly degrades system performance. To prevent thrashing, it's essential to either reduce the number of active processes or increase the available physical memory.

Trashing occurs in memory management when the system spends more time swapping data between the physical memory (RAM) and the storage (usually hard disk) than executing actual tasks. To optimize memory management and prevent trashing, efficient algorithms must be employed. One approach is to use a well-designed page replacement policy, such as Least Recently Used (LRU) or Optimal Page Replacement. From your perspective, understanding the memory requirements of the software you use is crucial. Regularly monitoring system resources can help identify potential trashing issues and guide proactive measures to maintain optimal performance.

A poorly designed/written program can also cause thrashing. In the "good old days" IBM FORTRAN (how many of you remember it?) stored arrays in Column Major order (1,1; 2,1; 3,1; ... n,1). If you don't (know to) program for this implementation, you could be causing multiple page faults by accessing the array in "normal human" order (1,1; 1,2; 1,3; ... 1,n). So this isn't a problem on systems with large memory, right? WRONG. You may have terabytes of memory, but the CPU chip has to fetch memory into its cache before it can be used. The chip prestages what it thinks is the next piece of data to be referenced, but it isn't. In this case, the prestaged cached data has to be thrown away. The chip, in effect thrashes.

Thrashing occurs when a computer's operating system is overwhelmed by too many tasks, leading to excessive swapping of data between RAM and disk storage. This happens when the memory is overloaded with more processes than it can handle, causing frequent page faults and significantly slowing down system performance. Essentially, the system is caught in a cycle of swapping data without making much progress in executing tasks, due to limited memory resources and inefficient management of those resources.

Existem vários algoritmos de gerenciamento de memória que podem ser usados para evitar o goleamento, como o algoritmo de alocação de memória First-Fit, Best-Fit e Worst-Fit. O algoritmo First-Fit aloca o primeiro bloco de memória livre que é grande o suficiente para o processo. O algoritmo Best-Fit aloca o bloco de memória livre mais próximo do tamanho do processo. O algoritmo Worst-Fit aloca o maior bloco de memória livre.

When a computer's RAM cannot keep up with the demands of its running programs, thrashing occurs as a result of frequent data switching between RAM and slower storage. The operating system spends too much time moving data in and out of RAM as a result of multiple concurrent processes fighting for the same finite amount of memory. Consequently, frequent swapping overwhelms the system and creates a performance bottleneck. Input/output operations are greatly increased by this continuous data interchange, which sharply reduces system performance as it tries to keep up with the needs of the active activities.

Thrashing happens when a computer system is swamped with too many tasks, and there's not enough physical memory to handle them. It ends up spending more time shuffling data between RAM and the hard disk than actually getting work done, causing a noticeable slowdown in performance. This occurs because the active processes' working set exceeds the available RAM, leading to constant page swapping and a significant drop in overall efficiency. In simpler terms, thrashing is like a computer getting bogged down because it's trying to do much with too little memory.

When it comes to performance analysis, high CPU utilization isn't necessarily a bad thing. One thing I find useful though, is that in the context of thrashing, high CPU utilization must be interpreted carefully. If it's coupled with a high rate of page faults, it might indicate that the CPU is busy with inefficient tasks like handling these faults rather than executing useful work.

Thrashing can be measured by monitoring the system's page fault rate, CPU utilization, and disk I/O. A high page fault rate indicates excessive swapping between RAM and storage. Elevated CPU usage handling page faults instead of executing tasks and increased disk I/O activities denote frequent data transfers between RAM and disk. These metrics collectively indicate the occurrence and intensity of thrashing based on the frequency of page faults and resource utilization.

Measure trashing by monitoring key performance indicators like high disk I/O rates, excessive page fault rates, and increased CPU usage. Tools like system monitors or performance counters can provide insights into these metrics. Additionally, observe a decrease in overall system responsiveness and increased response times, indicating potential thrashing. Regularly assessing these factors helps identify and address trashing issues promptly.

Algoritmos de gerenciamento de memória adaptativos previnem debulha ajustando alocações e multiprogramação. Utilizam feedback, como modelos de conjunto de trabalho, para alocar memória eficientemente e manter a multiprogramação ideal.

Another important aspect is reducing the number of programs running concurrently can help prevent thrashing. This approach is about finding the right balance between underutilization and overloading of system resources.

Thrashing can be prevented by optimizing memory use, using smart page algorithms, leveraging data access patterns, proactive paging, prioritizing processes, configuring swap wisely, and regularly monitoring the system performance. Always check the status of the hardware regularly

Prevent trashing by optimizing memory management. Increase physical memory, use efficient page replacement algorithms (e.g., LRU), and monitor system resources. Close unnecessary applications, optimize code for memory efficiency, and be mindful of overcommitting memory. These measures collectively reduce the likelihood of excessive swapping, mitigating the impact of trashing on system performance.

There are many possible approaches, especially with today's CPUs and SoCs: check the capabilities of your hardware and how much physical memory you're able to allocate before running too many applications. On the other hand, in MCUs and SoCs, a hardware MMU (Memory Management Unit) is commonplace. One of the simplest ways of preventing thrashing is to make sure your hardware has enough extra capacity to handle the tasks you need it to handle, without excessive sub-utilization.

To recover from trashing, allocate more physical memory, optimize existing memory usage, and adjust page replacement policies. Identify memory-intensive processes and prioritize or optimize them. Consider upgrading hardware or redistributing system resources. Restarting applications or the system can provide a temporary solution. Regular monitoring and proactive adjustments help mitigate trashing and restore optimal system performance.

Terminate non-critical and unused processes in runtime (e. g. applications you don't currently use but are sitting in the background). Optimize page sizing. Create a checklist of processes that run automatically after booting and are more RAM-intensive than others, processes that mostly run in the background, their runtime priorities, and so forth. It might be important to remember that when the sooner a "bulky" process is terminated, the lesser the risk of allowing a bigger page size and the need for more physical memory (RAM) allocation in runtime and the "provisioned" amount in cache is reduced.

Let us not forget the basics too. Sometimes, the simplest solution is to increase the physical memory (RAM) of the system, which directly reduces the need for swapping and paging. It might be less expensive than implementing highly customized software workarounds.

It depends on the hardware to be approached, whether is a normal computer or an embedded system. If your computer has the upgrading options and you have the curiosity to do so, consider upgrading your RAM and SSD storage capacities, while maintaining a similar workload. Also, consider reducing the quantity of processes and applications to be executed after booting your OS. Try to avoid running too many applications simultaneously. In the case of an embedded system with an RTOS, try to allocate some stack sizes which are big enough for each of your tasks, while at the same time, leaving enough room for the heap to operate. Also, try to define which variables are to be allocated to RAM and which to FLASH before initializing.

I think using queues for processes maybe a little helpful. Making a list of processes queues and pause latest processes in it, until CPU or memory is released and have enough space to handle them. Memory/disk page swapping can increase run-time of processes and causes delays on CPU and wasting time. Other hand, applications must manage resource usage programmatically to prevent memory leaks or trashing.

I'd like to add the importance of throughput optimization in embedded systems like MCUs and SoCs, by avoiding thrashing. In embedded systems, data throughput even if optimized by hardware pipelining, naturally has limited bandwidth and possible optimization limitations have to be considered. More aspects of data organization should be thought of: optimize queues and buffers' sizes, freeing data from memory after context switching if possible... In the end, it doesn't matter if it's a full-fledged operating system or an embedded system, it's better to take preventive measure rather than reactive/corrective ones on a constant basis.

You can also cause a data base to "thrash" (do extra, non-productive work). An example of this is sequentially searching reading an element that isn't indexed. Additional indexes on the table/database can help, but there are tradeoffs here - especially when there are a large number of inserts or deletes. But again, poor programming/not understanding the underlining structure can be the culprit. For example, if you want to know if a table is empty you COULD request a COUNT of the number of items, which makes the DBMS search the database. If there are a lot of items, you're forcing the DBMS to do a lot of extra work. A better way: FETCH the 1st item. You'll either get it, or an error code stating the table is empty/element not found.