Coordinated checkpointing simplifies failure recovery and eliminates domino effects in case of failures by preserving a consistent global checkpoint on stable storage. However, the approach suffers from high overhead associated with the checkpointing process. Two approaches are used to reduce the overhead: first is to minimize the number of synchronization messages and the number of checkpoints, the other is to make the checkpointing process nonblocking. These two approaches were orthogonal in previous years until the Prakash-Singhal algorithm combined them. In other words, the Prakash-Singhal algorithm forces only a minimum number of processes to take checkpoints and it does not block the underlying computation. However, we found two problems in this algorithm. In this paper, we identify these problems and prove a more general result: there does not exist a nonblocking algorithm that forces only a minimum number of processes to take their checkpoints. Based on this general result, we propose an efficient algorithm that neither forces all processes to take checkpoints nor blocks the underlying computation during checkpointing. Also, we point out future research directions in designing coordinated checkpointing algorithms for distributed computing systems.

/pdf/on-coordinated-checkpointing-in-distributed-systems-3h4h6glcfm.pdf

On coordinated checkpointing in distributed systems

Mobile computing raises many new issues such as lack of stable storage, low bandwidth of wireless channel, high mobility, and limited battery life. These new issues make traditional checkpointing algorithms unsuitable. Coordinated checkpointing is an attractive approach for transparently adding fault tolerance to distributed applications since it avoids domino effects and minimizes the stable storage requirement. However, it suffers from high overhead associated with the checkpointing process in mobile computing systems. Two approaches have been used to reduce the overhead: First is to minimize the number of synchronization messages and the number of checkpoints; the other is to make the checkpointing process nonblocking. These two approaches were orthogonal previously until the Prakash-Singhal algorithm combined them. However, we found that this algorithm may result in an inconsistency in some situations and we proved that there does not exist a nonblocking algorithm which forces only a minimum number of processes to take their checkpoints. In this paper; we introduce the concept of "mutable checkpoint," which is neither a tentative checkpoint nor a permanent checkpoint, to design efficient checkpointing algorithms for mobile computing systems. Mutable checkpoints can be saved anywhere, e.g., the main memory or local disk of MHs. In this way, taking a mutable checkpoint avoids the overhead of transferring large amounts of data to the stable storage at MSSs over the wireless network. We present techniques to minimize the number of mutable checkpoints. Simulation results show that the overhead of taking mutable checkpoints is negligible. Based on mutable checkpoints, our nonblocking algorithm avoids the avalanche effect and forces only a minimum number of processes to take their checkpoints on the stable storage.

/pdf/mutable-checkpoints-a-new-checkpointing-approach-for-mobile-3vhy1qqa78.pdf

Mutable checkpoints: a new checkpointing approach for mobile computing systems

Mobile computing raises many new issues, such as lack of stable storage, low bandwidth of wireless channel, high mobility, and limited battery life. These new issues make traditional checkpointing algorithms unsuitable. R. Prakash and M. Singhal (1996) proposed the first coordinated checkpointing algorithm for mobile computing systems. However we showed that their algorithm may result in an inconsistency. In this paper, we prove a more general result about coordinated checkpointing: there does not exist a non-blocking algorithm that forces only a minimum number of processes to take their checkpoints. Based on the proof, we propose an efficient algorithm for mobile computing systems, which forces only a minimum number of processes to take checkpoints and dramatically reduces the blocking time during the checkpointing process. Correctness proofs and performance analysis of the algorithm are provided.

On the impossibility of min-process non-blocking checkpointing and an efficient checkpointing algorithm for mobile computing systems

Mobile computing raises many new issues such as lack of stable storage, low bandwidth of wireless channel, high mobility, and limited battery life. These new issues make traditional checkpointing algorithms unsuitable. Coordinated checkpointing is an attractive approach for transparently adding fault tolerance to distributed applications since it avoids domino effects and minimizes the stable storage requirement. However, it suffers from high overhead associated with the checkpointing process in mobile computing systems. Two approaches have been used to reduce the overhead: First is to minimize the number of synchronization messages and the number of checkpoints; the other is to make the checkpointing process nonblocking. These two approaches were orthogonal previously until the Prakash-Singhal algorithm (28) combined them. However, we (8) found that this algorithm may result in an inconsistency in some situations and we proved that there does not exist a nonblocking algorithm which forces only a minimum number of processes to take their checkpoints. In this paper, we introduce the concept of "mutable checkpoint," which is neither a tentative checkpoint nor a permanent checkpoint, to design efficient checkpointing algorithms for mobile computing systems. Mutable checkpoints can be saved anywhere, e.g., the main memory or local disk of MHs. In this way, taking a mutable checkpoint avoids the overhead of transferring large amounts of data to the stable storage at MSSs over the wireless network. We present techniques to minimize the number of mutable checkpoints. Simulation results show that the overhead of taking mutable checkpoints is negligible. Based on mutable checkpoints, our nonblocking algorithm avoids the avalanche effect and forces only a minimum number of processes to take their checkpoints on the stable storage.

/pdf/mutable-checkpoints-a-new-checkpointing-approach-for-mobile-33agjslu7a.pdf

http://mcn.cse.psu.edu/paper/gcao/TCS03.pdf

Yong Deng

Papers

Checkpointing and rollback-recovery algorithms in distributed systems