A Survey of Fault-Tolerance and Fault-Recovery Techniques in Parallel Systems

Supercomputing systems today often come in the form of large numbers of commodity systems linked together into a computing cluster. These systems, like any distributed system, can have large numbers of independent hardware components cooperating or collaborating on a computation. Unfortunately, any of this vast number of components can fail at any time, resulting in potentially erroneous output. In order to improve the robustness of supercomputing applications in the presence of failures, many techniques have been developed to provide resilience to these kinds of system faults. This survey provides an overview of these various fault-tolerance techniques.Comment: 11 page

Fine-Grain Checkpointing with In-Cache-Line Logging

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cost of ensuring the order in which memory writes reach NVM. Often, this requires flushing data to NVM and waiting a full memory round-trip time. In this paper, we introduce two new techniques: Fine-Grained Checkpointing, which ensures a consistent, quickly recoverable data structure in NVM after a system failure, and In-Cache-Line Logging, an undo-logging technique that enables recovery of earlier state without requiring cache-line flushes in the normal case. We implemented these techniques in the Masstree data structure, making it persistent and demonstrating the ease of applying them to a highly optimized system and their low (5.9-15.4\%) runtime overhead cost.Comment: In 2019 Architectural Support for Programming Languages and Operating Systems (ASPLOS 19), April 13, 2019, Providence, RI, US

CHECKPOINTING AND RECOVERY IN DISTRIBUTED AND DATABASE SYSTEMS

A transaction-consistent global checkpoint of a database records a state of the database which reflects the effect of only completed transactions and not the re- sults of any partially executed transactions. This thesis establishes the necessary and sufficient conditions for a checkpoint of a data item (or the checkpoints of a set of data items) to be part of a transaction-consistent global checkpoint of the database. This result would be useful for constructing transaction-consistent global checkpoints incrementally from the checkpoints of each individual data item of a database. By applying this condition, we can start from any useful checkpoint of any data item and then incrementally add checkpoints of other data items until we get a transaction- consistent global checkpoint of the database. This result can also help in designing non-intrusive checkpointing protocols for database systems. Based on the intuition gained from the development of the necessary and sufficient conditions, we also de- veloped a non-intrusive low-overhead checkpointing protocol for distributed database systems. Checkpointing and rollback recovery are also established techniques for achiev- ing fault-tolerance in distributed systems. Communication-induced checkpointing algorithms allow processes involved in a distributed computation take checkpoints independently while at the same time force processes to take additional checkpoints to make each checkpoint to be part of a consistent global checkpoint. This thesis develops a low-overhead communication-induced checkpointing protocol and presents a performance evaluation of the protocol

Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks

Tachyon is a distributed file system enabling reliable data sharing at memory speed across cluster computing frameworks. While caching today improves read workloads, writes are either network or disk bound, as replication is used for fault-tolerance. Tachyon eliminates this bottleneck by pushing lineage, a well-known technique, into the storage layer. The key challenge in making a long-running lineage-based storage system is timely data recovery in case of failures. Tachyon addresses this issue by introducing a checkpointing algorithm that guarantees bounded recovery cost and resource allocation strategies for recomputation under commonly used resource schedulers. Our evaluation shows that Tachyon outperforms in-memory HDFS by 110x for writes. It also improves the end-to-end latency of a realistic workflow by 4x. Tachyon is open source and is deployed at multiple companies.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)Lawrence Berkeley National Laboratory (Award 7076018)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331

Tenant Level Checkpointing of Meta-data for Multi-tenancy SaaS

Traditional checkpointing techniques are facing a grave challenge when applied to multi-tenancy software-as-a-service (SaaS) systems due to the huge scale of the system state and the diversity of users' requirements on the quality of services. This paper proposes the notion of tenant level checkpointing and an algorithm that exploits Big Data techniques to checkpoint tenant's meta-data, which are widely used in configuring SaaS for tenant-specific features. The paper presents a prototype implementation of the proposed technique using NoSQL database Couchbase and reports the experiments that compare it with traditional implementation of checkpointing using file systems. Experiments show that the Big Data approach has a significantly lower latency in comparison with the traditional approach

Positional Delta Trees to reconcile updates with read-optimized data storage

We investigate techniques that marry the high readonly analytical query performance of compressed, replicated column storage (“read-optimized” databases) with the ability to handle a high-throughput update workload. Today’s large RAM sizes and the growing gap between sequential vs. random IO disk throughput, bring this once elusive goal in reach, as it has become possible to buffer enough updates in memory to allow background migration of these updates to disk, where efficient sequential IO is amortized among many updates. Our key goal is that read-only queries always see the latest database state, yet are not (significantly) slowed down by the update processing. To this end, we propose the Positional Delta Tree (PDT), that is designed to minimize the overhead of on-the-fly merging of differential updates into (index) scans on stale disk-based data. We describe the PDT data structure and its basic operations (lookup, insert, delete, modify) and provide an in-detail study of their performance. Further, we propose a storage architecture called Replicated Mirrors, that replicates tables in multiple orders, storing each table copy mirrored in both column- and row-wise data formats, and uses PDTs to handle updates. Experiments in the MonetDB/X100 system show that this integrated architecture is able to achieve our main goals