In-memory key-value stores provide consistent low-latency access to all objects which is important for interactive large-scale applications like social media networks or online graph analytics and also opens up new application areas. But, when storing the data in RAM on thousands of servers one has to consider server failures. Only a few in-memory key-value stores provide automatic online recovery of failed servers. The most prominent example of these systems is RAMCloud. Another system with sophisticated fault-tolerance mechanisms is DXRAM which is optimized for small data objects. In this report, we detail the remote replication process which is based on logs, investigate selection strategies for the reorganization of these logs and evaluate the reorganization performance for sequential, random, zipf and hot-and-cold distributions in DXRAM. This is also the first time DXRAM's backup system is evaluated with high speed I O devices, specifically with 56 GBit s InfiniBand interconnect and PCI-e SSDs. Furthermore, we discuss the copyset replica distribution to reduce the probability for data loss and the adaptations to the original approach for DXRAM.
In RAMCloud copysets are created on the coordinator. Whenever a master creates a new in-memory object, it queries a set of backup servers from the coordinator (Figure ). The first backup server is chosen randomly, the others belong to a copyset containing the first backup server. Every primary backups' objects are distributed to the same set of backup servers. But, every masters’ in-memory chunks are scattered across the cluster. Only, if all nodes from one copyset and the master fail simultaneously data loss occurs. The scatter width is important for recovery as every master replicates its chunks to many copysets. Therefore, it is always @math and the recovery time is nearly unaffected (1.1s instead of 0.73s @cite_9 ).
@cite_9
2146033465 2074881976 1521407587 2403712541
Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. We present Copyset Replication, a novel general-purpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node's data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99 to 0.15 . For Facebook's HDFS cluster, it reduces the probability from 22.8 to 0.78 . RAMCloud is a storage system that provides low-latency access to large-scale datasets. To achieve low latency, RAMCloud stores all data in DRAM at all times. To support large capacities (1PB or more), it aggregates the memories of thousands of servers into a single coherent key-value store. RAMCloud ensures the durability of DRAM-based data by keeping backup copies on secondary storage. It uses a uniform log-structured mechanism to manage both DRAM and secondary storage, which results in high performance and efficient memory usage. RAMCloud uses a polling-based approach to communication, bypassing the kernel to communicate directly with NICs; with this approach, client applications can read small objects from any RAMCloud storage server in less than 5μs, durable writes of small objects take about 13.5μs. RAMCloud does not keep multiple copies of data online; instead, it provides high availability by recovering from crashes very quickly (1 to 2 seconds). RAMCloud’s crash recovery mechanism harnesses the resources of the entire cluster working concurrently so that recovery performance scales with cluster size. Storage deduplication has received recent interest in the research community. In scenarios where the backup process has to complete within short time windows, inline deduplication can help to achieve higher backup throughput. In such systems, the method of identifying duplicate data, using disk-based indexes on chunk hashes, can create throughput bottlenecks due to disk I Os involved in index lookups. RAM prefetching and bloom-filter based techniques used by [42] can avoid disk I Os on close to 99 of the index lookups. Even at this reduced rate, an index lookup going to disk contributes about 0.1msec to the average lookup time - this is about 1000 times slower than a lookup hitting in RAM. We propose to reduce the penalty of index lookup misses in RAM by orders of magnitude by serving such lookups from a flash-based index, thereby, increasing inline deduplication throughput. Flash memory can reduce the huge gap between RAM and hard disk in terms of both cost and access times and is a suitable choice for this application. To this end, we design a flash-assisted inline deduplication system using ChunkStash, a chunk metadata store on flash. ChunkStash uses one flash read per chunk lookup and works in concert with RAM prefetching strategies. It organizes chunk metadata in a log-structure on flash to exploit fast sequential writes. It uses an inmemory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores (2-byte) compact key signatures instead of full chunk-ids (20-byte SHA-1 hashes) so as to strike tradeoffs between RAM usage and false flash reads. Further, by indexing a small fraction of chunks per container, ChunkStash can reduce RAM usage significantly with negligible loss in deduplication quality. Evaluations using real-world enterprise backup datasets show that ChunkStash outperforms a hard disk index based inline deduplication system by 7x-60x on the metric of backup throughput (MB sec). Many proposals for managing replicated data use sites running the Snapshot Isolation (SI) concurrency control mechanism, and provide 1-copy SI or something similar, as the global isolation level. This allows good scalability, since only ww-conflicts need to be managed globally. However, 1-copy SI can lead to data corruption and violation of integrity constraints [5]. 1-copy serializability is the global correctness condition that prevents data corruption. We propose a new algorithm Replicated Serializable Snapshot Isolation (RSSI) that uses SI at each site, and combines this with a certification algorithm to guarantee 1-copy serializable global execution. Management of ww-conflicts is similar to what is done in 1-copy SI. But unlike previous designs for 1-copy serializable systems, we do not need to prevent all rw-conflicts among concurrent transactions. We formalize this in a theorem that shows that many rw-conflicts are indeed false positives that do not risk non-serializable behavior. Our proposed RSSI algorithm will only abort a transaction when it detects a well-defined pattern of two consecutive rw-edges in the serialization graph. We have built a prototype that integrates our RSSI with the existing open-source PostgresR(SI) system. Our performance evaluation shows that there is a worst-case overhead of about 15 for getting full 1copy serializability as compared to 1-copy SI in a cluster of 8 nodes, with our proposed RSSI clearly outperforming the previous work [6] for update-intensive workloads.