Distribution-Free Checkpoint Placement Algorithms Based on Min-Max Principle

Ozaki, T.; Okamura, Hiroyuki; Kaio, Naoto

doi:10.1109/tdsc.2006.22

Cited by 51 publications

(46 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A large body of work has studied periodic coordinated checkpointing for a single divisible application. Given the simplicity of the divisible model, a wide range of results are available including first order formulas for the checkpointing period that minimizes execution time [2,3] or more accurate formulas for Weibull failure distributions [14,15,16]. The optimal checkpointing period is known only for exponential failure distributions [17].…”

Section: Related Workmentioning

confidence: 99%

Checkpointing Strategies for Scheduling Computational Workflows

Aupy

Benoît

Casanova

et al. 2016

IJNC

View full text Add to dashboard Cite

We study the scheduling of computational workflows on compute resources that experience exponentially distributed failures. When a failure occurs, rollback and recovery is used to resume the execution from the last checkpointed state. The scheduling problem is to minimize the expected execution time by deciding in which order to execute the tasks in the workflow and deciding for each task whether to checkpoint it or not after it completes. We give a polynomialtime optimal algorithm for fork DAGs (Directed Acyclic Graphs) and show that the problem is NP-complete with join DAGs. We also investigate the complexity of the simple case in which no task is checkpointed. Our main result is a polynomial-time algorithm to compute the expected execution time of a workflow, with a given task execution order and specified to-be-checkpointed tasks. Using this algorithm as a basis, we propose several heuristics for solving the scheduling problem. We evaluate these heuristics for representative workflow configurations.

show abstract

Section: Related Workmentioning

confidence: 99%

Checkpointing Strategies for Scheduling Computational Workflows

Aupy

Benoît

Casanova

et al. 2016

IJNC

View full text Add to dashboard Cite

show abstract

“…Many models are available to understand the behavior of checkpoint/restart [19,20,21,22], and thereby to define an optimal checkpoint period. [23] proposes a scalability model to evaluate the impact of failures on application performance.…”

Section: Related Workmentioning

confidence: 99%

Composing resilience techniques: ABFT, periodic and incremental checkpointing

Bosilca

Bouteiller

Hérault

et al. 2015

IJNC

View full text Add to dashboard Cite

Algorithm Based Fault Tolerant (ABFT) approaches promise unparalleled scalability and performance in failure-prone environments. Thanks to recent advances in the understanding of the involved mechanisms, a growing number of important algorithms (including all widely used factorizations) have been proven ABFT-capable. In the context of larger applications, these algorithms provide a temporal section of the execution, where the data is protected by its own intrinsic properties, and can therefore be algorithmically recomputed without the need of checkpoints. However, while typical scientific applications spend a significant fraction of their execution time in library calls that can be ABFT-protected, they interleave sections that are difficult or even impossible to protect with ABFT. As a consequence, the only practical fault-tolerance approach for these applications is checkpoint/restart. In this paper we propose a model to investigate the efficiency of a composite protocol, that alternates between ABFT and checkpoint/restart for the effective protection of an iterative application composed of ABFTaware and ABFT-unaware sections. We also consider an incremental checkpointing composite approach in which the algorithmic knowledge is leveraged by a novel optimal dynamic programming to compute checkpoint dates. We validate these models using a simulator. The model and simulator show that the composite approach drastically increases the performance delivered by an execution platform, especially at scale, by providing the means to increase the interval between checkpoints while simultaneously decreasing the volume of each checkpoint.

show abstract

“…In [10], the authors proposed an approach for checkpoint placement under incomplete failures information and when the failures distribution is unknown. The min-max principle has been employed to this extent.…”

Section: Related Workmentioning

confidence: 99%

On the Optimum Checkpointing Interval Selection for Variable Size Checkpoint Dumps

Sadi

Yagoubi

2015

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. Checkpointing is a technique that is often employed for granting fault tolerance for applications executing in failure-prone environments. It consists on regularly saving the application's state in another and fault independent storage such that if the application fails, it can be continued without necessarily restarting it. In this context, fixing the checkpointing frequency is an important topic which we address in this paper. We particularly address this issue considering hybrid fault tolerance and variable size checkpoint dumps. We then evaluate our solution and compare it with state of the art models, and show that our solution brings better results.

show abstract

Distribution-Free Checkpoint Placement Algorithms Based on Min-Max Principle

Cited by 51 publications

References 26 publications

Checkpointing Strategies for Scheduling Computational Workflows

Checkpointing Strategies for Scheduling Computational Workflows

Composing resilience techniques: ABFT, periodic and incremental checkpointing

On the Optimum Checkpointing Interval Selection for Variable Size Checkpoint Dumps

Contact Info

Product

Resources

About