Distributed caching with memcached

By B. Fitzpatrick. 2004

803 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d05a830e71e39d403919d760722c1961225ced1b

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Citations in the corpus

CloudVisor: retrofitting protection of virtual machines in multi-tenant cloud with nested virtualization (2011, background): “5 [19] for memory and network I/O intensive workloads; 4) dbench 3.”
Using One-Sided RDMA Reads to Build a Fast, CPU-Efficient Key-Value Store (2013, result): “We compare Pilaf to two existing popular key-value systems, Memcached [7] and Redis [25].”
TF-Replicator: Distributed Machine Learning for Researchers (2019, methodology): “The parameter server itself was inspired largely by memcached (Fitzpatrick, 2004), exposing push(k,v) and pull(k) functions that can be used for applying gradient-based updates to shared neural network weights.”
Scaling Memcache at Facebook (2013, unknown): “We describe how we improved the open source version of memcached [14] and used it as a building block to construct a distributed key-value store for the largest social network in the world.”
SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale (2019, background): “There is a growing trend towards microservice implementation models [2–6], wherein a complex application is decomposed into distributed microservices [7–10] that each provide specialized functionality [11], such as HTTP connection termination, key-value serving [12], protocol routing [13,14], or ad ”
Trinity: a distributed graph engine on a memory cloud (2013, background): “[17] to cache large volume of long-lived small objects.”
Workload analysis of a large-scale key-value store (2012, background): “Examples include: Dynamo at Amazon [15]; Redis at GitHub, Digg, and Blizzard Interactive [27]; Mem cached at Facebook, Zynga and Twitter [18, 26]; and Volde mort at Linkedin [1].”
Quasar: resource-efficient and QoS-aware cluster management (2014, background): “of memory-based memcached [22] and disk-based Cassandra [14], two latency-critical NoSQL services.”
PARTIES: QoS-Aware Resource Partitioning for Multiple Interactive Services (2019, methodology): “We characterize six open-source interactive applications: • Memcached [27] is a high-performance memory object caching system.”
Online Machine Learning in Big Data Streams (2018, background): “m designer can be ﬂexible in choosing a consistency model. Various parameter server systems are summarized best in [157]. The ﬁrst generation [209] uses distributed key-value stores such as memcached [81] to store the parameters. More recent solutions [67, 159] implement speciﬁc parameter access int”
Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory (2011, methodology): “Examples of the latter include memcached [18] clusters over 200 TB in size [28], memory-backed systems such as RAMCloud [38], inmemory databases [47] [52], and NoSQL systems such as Redis [41].”
SGXBOUNDS: Memory Safety for Shielded Execution (2017, methodology): “15 [40] using the memaslap benchmark shipped together with libmemcached v1.”
IncBricks: Toward In-Network Computation with an In-Network Cache (2017, background): “In this section, we demonstrate how IncBricks impacts a latency-critical datacenter application—an in-memory key-value store similar to Memcached [23].”
This paper is included in the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). (unknown): “Containers use OS-level virtualization [35] and have become increasingly popular for packaging, deploying and managing services such as key/value stores [46, 23] and web servers [47, 25].”
A Fast Distributed Stochastic Gradient Descent Algorithm for Matrix Factorization (2014, methodology): “According to the limitation of Memcached that the maximum size of its storing value is 1M map; Fitzpatrick (2004), we will compress those exceeded value to put them into Memcached.”
Accelerometer: Understanding Acceleration Opportunities for Data Center Overheads at Hyperscale (2020, background): “There is a growing trend towards microservice implementation models [1, 5, 13, 15, 124], wherein a complex application is decomposed into distributed microservices [64, 90, 110, 112, 113] that each provide specialized functionality [115], such as HTTP connection termination, key-value serving [46], ”
Horton Tables: Fast Hash Tables for In-Memory Data-Intensive Computing (2016, background): “Hash tables are fundamental data structures that are ubiquitous in high-performance and big-data applications such as in-memory relational databases [12, 17, 40] and key-value stores [20, 24].”