Checkpointing is a technique that provides fault tolerance for computing systems. It basically consists of saving a snapshot of the application's state, so that applications can restart from that point in case of failure. This is particularly important for long running applications that are executed in failure-prone computing systems.

Checkpointing in distributed systems

In the distributed computing environment, checkpointing is a technique that helps tolerate failures that otherwise would force long-running application to restart from the beginning. The most basic way to implement checkpointing, is to stop the application, copy all the required data from the memory to reliable storage (e.g., parallel file system) and then continue with the execution.[1] In case of failure, when the application restarts, it does not need to start from scratch. Rather, it will read the latest state ("the checkpoint") from the stable storage and execute from that. While there is ongoing debate on whether checkpointing is the dominating I/O workload on distributed computing systems, there is general consensus that checkpointing is one of the major I/O workloads.[2][3]

There are two main approaches for checkpointing in the distributed computing systems: coordinated checkpointing and uncoordinated checkpointing. In the coordinated checkpointing approach, processes must ensure that their checkpoints are consistent. This is usually achieved by some kind of two-phase commit protocol algorithm. In the uncoordinated checkpointing, each process checkpoints its own state independently. It must be stressed that simply forcing processes to checkpoint their state at fixed time intervals is not sufficient to ensure global consistency. The need for establishing a consistent state (i.e., no missing messages or duplicated messages) may force other processes to roll back to their checkpoints, which in turn may cause other processes to roll back to even earlier checkpoints, which in the most extreme case may mean that the only consistent state found is the initial state (the so-called domino effect).[4][5]

Implementations for applications

Save State

One of the original and now most common means of application checkpointing was a "save state" feature in interactive applications, in which the user of the application could save the state of all variables and other data to a storage medium at the time they were using it and either continue working, or exit the application and at a later time, restart the application and restore the saved state. This was implemented through a "save" command or menu option in the application. In many cases it became standard practice to ask the user if they had unsaved work when exiting the application if they wanted to save their work before doing so.

This sort of functionality became extremely important for usability in applications where the particular work could not be completed in one sitting (such as playing a video game expected to take dozens of hours, or writing a book or long document amounting to hundreds or thousands of pages) or where the work was being done over a long period of time such as data entry into a document such as rows in a spreadsheet.

The problem with save state is it requires the operator of a program to request the save. For non-interactive programs, including automated or batch processed workloads, the ability to checkpoint such applications also had to be automated.

Checkpoint/Restart

As batch applications began to handle tens to hundreds of thousands of transactions, where each transaction might process one record from one file against several different files, the need for the application to be restartable at some point without the need to rerun the entire job from scratch became imperative. Thus the "checkpoint/restart" capability was born, in which after a number of transactions had been processed, a "snapshot" or "checkpoint" of the state of the application could be taken. If the application failed before the next checkpoint, it could be restarted by giving it the checkpoint information and the last place in the transaction file where a transaction had successfully completed. The application could then restart at that point.

Checkpointing tends to be expensive, so it was generally not done with every record, but at some reasonable compromise between the cost of a checkpoint vs. the value of the computer time needed to reprocess a batch of records. Thus the number of records processed for each checkpoint might range from 25 to 200, depending on cost factors, the relative complexity of the application and the resources needed to successfully restart the application.

Fault Tolerance Interface (FTI)

FTI is a library that aims to provide computational scientists with an easy way to perform checkpoint/restart in a scalable fashion.[6] FTI leverages local storage plus multiple replications and erasures techniques to provide several levels of reliability and performance. FTI provides application-level checkpointing that allows users to select which data needs to be protected, in order to improve efficiency and avoid space, time and energy waste. It offers a direct data interface so that users do not need to deal with files and/or directory names. All metadata is managed by FTI in a transparent fashion for the user. If desired, users can dedicate one process per node to overlap fault tolerance workload and scientific computation, so that post-checkpoint tasks are executed asynchronously.

Berkeley Lab Checkpoint/Restart (BLCR)

The Future Technologies Group at the Lawrence National Laboratories are developing a hybrid kernel/user implementation of checkpoint/restart called BLCR. Their goal is to provide a robust, production quality implementation that checkpoints a wide range of applications, without requiring changes to be made to application code.[7] BLCR focuses on checkpointing parallel applications that communicate through MPI, and on compatibility with the software suite produced by the SciDAC Scalable Systems Software ISIC. Its work is broken down into 4 main areas: Checkpoint/Restart for Linux (CR), Checkpointable MPI Libraries, Resource Management Interface to Checkpoint/Restart and Development of Process Management Interfaces.

DMTCP

DMTCP (Distributed MultiThreaded Checkpointing) is a tool for transparently checkpointing the state of an arbitrary group of programs spread across many machines and connected by sockets.[8] It does not modify the user's program or the operating system. Among the applications supported by DMTCP are Open MPI, Python, Perl, and many programming languages and shell scripting languages. With the use of TightVNC, it can also checkpoint and restart X Window applications, as long as they do not use extensions (e.g. no OpenGL or video). Among the Linux features supported by DMTCP are open file descriptors, pipes, sockets, signal handlers, process id and thread id virtualization (ensure old pids and tids continue to work upon restart), ptys, fifos, process group ids, session ids, terminal attributes, and mmap/mprotect (including mmap-based shared memory). DMTCP supports the OFED API for InfiniBand on an experimental basis.[9]

Collaborative checkpointing

Some recent protocols perform collaborative checkpointing by storing fragments of the checkpoint in nearby nodes.[10] This is helpful because it avoids the cost of storing to a parallel file system (which often becomes a bottleneck for large-scale systems) and it uses storage that is closer. This has found use particularly in large-scale supercomputing clusters. The challenge is to ensure that when the checkpoint is needed when recovering from a failure, the nearby nodes with fragments of the checkpoints are available.

Docker

Docker and the underlying technology contain a checkpoint and restore mechanism.[11]

CRIU

CRIU is a user space checkpoint library.

Implementation for embedded and ASIC devices

Mementos

Mementos is a software system that transforms general-purpose tasks into interruptible programs for platforms with frequent interruptions such as power outages. It was designed for batteryless embedded devices such as RFID tags and smart cards which rely on harvesting energy from ambient background sources. Mementos frequently senses the available energy in the system and decides whether to checkpoint the program due to impending power loss versus continuing computation. If checkpointing, data will be stored in a non-volatile memory. When the energy becomes sufficient for reboot, the data is retrieved from non-volatile memory and the program continues from the stored state. Mementos has been implemented on the MSP430 family of microcontrollers. Mementos is named after Christopher Nolan's Memento.[12]

Idetic

Idetic is a set of automatic tools which helps application-specific integrated circuit (ASIC) developers to automatically embed checkpoints in their designs. It targets high-level synthesis tools and adds the checkpoints at the register-transfer level (Verilog code). It uses a dynamic programming approach to locate low overhead points in the state machine of the design. Since the checkpointing in hardware level involves sending the data of dependent registers to a non-volatile memory, the optimum points are required to have minimum number of registers to store. Idetic is deployed and evaluated on energy harvesting RFID tag device.[13]

See also

References

  1. ↑ Plank, J. S., Beck, M., Kingsley, G., & Li, K. (1994). Libckpt: Transparent checkpointing under unix. Computer Science Department.
  2. ↑ Wang, Teng; Snyder, Shane; Lockwood, Glenn; Carns, Philip; Wright, Nicholas; Byna, Suren (Sep 2018). "IOMiner: Large-Scale Analytics Framework for Gaining Knowledge from I/O Logs". 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE. pp. 466–476. doi:10.1109/CLUSTER.2018.00062. ISBN 978-1-5386-8319-4. S2CID 53235850.
  3. ↑ "Comparative I/O workload characterization of two leadership class storage clusters Logs" (PDF). ACM. Nov 2015.
  4. ↑ Bouteiller, B., Lemarinier, P., Krawezik, K., & Capello, F. (2003, December). Coordinated checkpoint versus message log for fault tolerant MPI. In Cluster Computing, 2003. Proceedings. 2003 IEEE International Conference on (pp. 242-250). IEEE.
  5. ↑ Elnozahy, E. N., Alvisi, L., Wang, Y. M., & Johnson, D. B. (2002). A survey of rollback-recovery protocols in message-passing systems. ACM Computing Surveys, 34(3), 375-408.
  6. ↑ Bautista-Gomez, L., Tsuboi, S., Komatitsch, D., Cappello, F., Maruyama, N., & Matsuoka, S. (2011, November). FTI: high performance fault tolerance interface for hybrid systems. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (p. 32). ACM.
  7. ↑ Hargrove, P. H., & Duell, J. C. (2006, September). Berkeley lab checkpoint/restart (blcr) for linux clusters. In Journal of Physics: Conference Series (Vol. 46, No. 1, p. 494). IOP Publishing.
  8. ↑ Ansel, J., Arya, K., & Cooperman, G. (2009, May). DMTCP: Transparent checkpointing for cluster computations and the desktop. In Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on (pp. 1-12). IEEE.
  9. ↑ "GitHub - DMTCP/DMTCP: DMTCP: Distributed MultiThreaded CheckPointing". GitHub. 2019-07-11.
  10. ↑ Walters, J. P.; Chaudhary, V. (2009-07-01). "Replication-Based Fault Tolerance for MPI Applications". IEEE Transactions on Parallel and Distributed Systems. 20 (7): 997–1010. CiteSeerX 10.1.1.921.6773. doi:10.1109/TPDS.2008.172. ISSN 1045-9219. S2CID 2086958.
  11. ↑ "Docker - CRIU".
  12. ↑ Benjamin Ransford, Jacob Sorber, and Kevin Fu. 2011. Mementos: system support for long-running computation on RFID-scale devices. ACM SIGPLAN Notices 47, 4 (March 2011), 159-170. DOI=10.1145/2248487.1950386 http://doi.acm.org/10.1145/2248487.1950386
  13. ↑ Mirhoseini, A.; Songhori, E.M.; Koushanfar, F., "Idetic: A high-level synthesis approach for enabling long computations on transiently-powered ASICs," Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on , vol., no., pp.216,224, 18–22 March 2013 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6526735&isnumber=6526701

Further reading

  • Yibei Ling, Jie Mi, Xiaola Lin: A Variational Calculus Approach to Optimal Checkpoint Placement. IEEE Trans. Computers 50(7): 699-708 (2001)
  • R.E. Ahmed, R.C. Frazier, and P.N. Marinos, " Cache-Aided Rollback Error Recovery (CARER) Algorithms for Shared-Memory Multiprocessor Systems", IEEE 20th International Symposium on Fault-Tolerant Computing (FTCS-20), Newcastle upon Tyne, UK, June 26–28, 1990, pp. 82–88.
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