# Index

### Architecture 

#### Compute + Overall

* [The Datacenter as a Computer](http://pages.cs.wisc.edu/~akella/CS744/S19/838-CloudPapers/DCAsAComputer.pdf): An Introduction to the Design of Warehouse-Scale Machines , L.A. Barroso, U. Holzle, Synthesis Lectures on Computer Architecture, 2009. Chapter 1 and 2. 

#### Networks

* [VL2: A Scalable and Flexible Data Center Network](https://www.microsoft.com/en-us/research/publication/vl2-a-scalable-and-flexible-data-center-network/?from=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F80693%2Fvl2-sigcomm09-final.pdf), Greenberg et al., SIGCOMM 2009.
* [Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/jupiter_rising.pdf), Singh et al., SIGCOMM 2015.

#### Storage (in a bit detailed fashion) 

* [The Hadoop Distributed File System](http://pages.cs.wisc.edu/~akella/CS744/S19/838-CloudPapers/hdfs.pdf), Schvachko et al, MSST, 2010
* [The Google File System](http://pages.cs.wisc.edu/~shivaram/cs744-readings/GFS.pdf), Ghemawat et al, SOSP, 2003.
* [NFS: Sun's Network File System](http://pages.cs.wisc.edu/~remzi/OSTEP/dist-nfs.pdf)
* [Flat Datacenter Storage](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/FDS.pdf). Nightingale et. al, OSDI, 2012.
* [EC-Cache: Load-balanced, Low-latency Cluster Caching with Online Erasure Coding.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/ec-cache.pdf) Rashmi et. al, OSDI, 2016
* [f4: Facebook’s Warm BLOB Storage System. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/f4-facebook.pdf)Muralidhar et. al, OSDI, 2014.
* [Bigtable: A Distributed Storage System for Structured Data. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/bigtable.pdf)Chang et. al, OSDI, 2006.
* [Dynamo: Amazon’s Highly Available Key-value Store. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/dynamo.pdf)DeCandia et. al, SOSP, 2007.
* [Spanner: Google’s Globally-Distributed Database.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/spanner.pdf) Corbett et. al, OSDI, 2012.
* [An Analysis of Facebook Photo Caching. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/facebook_photo.pdf)Huang et. al, SOSP, 2013.
* [Scaling Memcache at Facebook.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/memcached_facebook.pdf) Nishtala et. al, NSDI, 2013.
* [The Chubby lock service for loosely-coupled distributed systems.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/chubby.pdf) Mike Burrows, OSDI, 2006.

### Execution Engines, Resource Negotiators, Schedulers 

#### Execution Engines 

* [MapReduce: Simplified Data Processing on Large Clusters, Dean and Ghemawat](http://static.googleusercontent.com/media/research.google.com/en/archive/mapreduce-osdi04.pdf), OSDI, 2004
* [Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/dryad.pdf) Isard et. al, EuroSys, 2007.
* [CIEL: a universal execution engine for distributed data-flow computing](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/ciel.pdf). Murray et. al, NSDI, 2011.
* [Reining in the Outliers in Map-Reduce Clusters using Mantri,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/mantri.pdf) Ananthanarayanan et al, OSDI, 2010.
* [DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/dryadlinq.pdf) Yu et. al, OSDI, 2008.
* [Encapsulation of parallelism in the Volcano query processing system.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/volcano.pdf) Goetz Graefe, SIGMOD, 1990.
* [PACMan: Coordinated Memory Caching for Parallel Jobs,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/PACMan.pdf) Ananthanarayanan et. al, NSDI, 2012.
* [Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing](https://www.usenix.org/system/files/conference/nsdi12/nsdi12-final138.pdf), Zaharia et al, NSDI, 2012.
* [Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications](https://dl.acm.org/doi/10.1145/2723372.2742790), Saha et al, SIGMOD, 2015.
* [Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/flare.pdf) Essertel et. al, OSDI, 2018.
* Transaction: [Obladi: Oblivious Serializable Transactions in the Cloud.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/obladi.pdf) Crooks et. al, OSDI, 2018.
* Load balancing 
  * [Ananta: Cloud Scale Load Balancing](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/anata.pdf). Patel et. al, SIGCOMM, 2013.
  * [Duet: Cloud Scale Load Balancing with Hardware and Software. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/duet.pdf)Gandhi et. al, SIGCOMM, 2014.
  * [SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/silkroad.pdf), Miao et. al, SIGCOMM, 2017.

#### Resource Negotiator 

* [Apache Hadoop YARN: Yet Another Resource Negotiator](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/YARN.pdf), Vavilapalli et al, SOCC, 2013.
* [Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Mesos.pdf) Hindman et al, NSDI, 2011.
* [Dominant Resource Fairness: Fair Allocation of Multiple Resource Types,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/DRF.pdf) Ghodsi et al, NSDI, 2011.
* [Borg: Large-scale cluster management at Google with Borg.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/borg.pdf) Verma et. al, EuroSys, 2015.

#### Scheduling

* Packing
  * [Altruistic Scheduling in Multi-Resource Clusters. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Carbyne.pdf)Grandl et. al, OSDI, 2016.
  * [Multi-Resource Packing for Cluster Schedulers, ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/tetris.pdf)Grandl et. al, SIGCOMM, 2014.
  * [Quincy: Fair Scheduling for Distributed Computing Clusters.](https://www.sigops.org/s/conferences/sosp/2009/papers/isard-sosp09.pdf) Isard et. al, SOSP, 2009.
* Re-Planning
  * [Dynamic Query Re-Planning using QOOP. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/qoop.pdf)Mahajan et. al, OSDI, 2018.
* Threads 
  * [Arachne: Core-Aware Thread Management. ](https://www.usenix.org/system/files/osdi18-qin.pdf)Qin et. al, OSDI, 2018.
* Cache  
  * [RobinHood: Tail Latency Aware Caching – Dynamic Reallocation from Cache-Rich to Cache-Poor. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/robinhood.pdf)Berger et. al, OSDI, 2018.

### Applications: Machine Learning 

* [Scaling Distributed Machine Learning with the Parameter Server](http://www.cs.cmu.edu/~muli/file/parameter_server_osdi14.pdf), Li et al, OSDI, 2014.
* [STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning](https://www.pdl.cmu.edu/PDL-FTP/Storage/strads-kim-eurosys16.pdf), Kim et al, EuroSys, 2016.
* [SLAQ: Quality-Driven Scheduling for Distributed Machine Learning](https://www.cs.princeton.edu/~mfreed/docs/slaq-socc17.pdf), Zhang et al, SoCC, 2017.
* [TensorFlow: A System for Large-Scale Machine Learning](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf), Abadi et al, OSDI, 2016.
* [Pytorch Distributed: Experiences on Accelerating Data Parallel Training](https://arxiv.org/pdf/2006.15704.pdf), Shen et al, VLDB, 2020 
* [Gandiva: Introspective Cluster Scheduling for Deep Learning,](https://www.usenix.org/system/files/osdi18-xiao.pdf) Xiao et al, OSDI, 2018.
* [Clipper: A Low-Latency Online Prediction Serving System](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-crankshaw.pdf), Crankshaw et al, NSDI, 2017.
* [PipeDream: Generalized Pipeline Parallelism for DNN Training. ](https://cs.stanford.edu/~matei/papers/2019/sosp_pipedream.pdf)Narayanan et al, SOSP 2019.  
* [TVM: An Automated End-to-End Optimizing Compiler for Deep Learning](https://www.usenix.org/system/files/osdi18-chen.pdf), Chen et al, OSDI, 2018 
* [Ray: A Distributed Framework for Emerging AI Applicationss](https://www.usenix.org/system/files/osdi18-moritz.pdf), Moritz et al, OSDI, 2018.
* [Towards a Unified Architecture for in-RDBMS Analytics](http://pages.cs.wisc.edu/~shivaram/cs744-readings/bismarck.pdf), Feng et al, SIGMOD, 2012 
* [DeepCPU: Serving RNN-based Deep Learning Models 10x Faster.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/deepcpu.pdf) Zhang et. al, USENIX ATC, 2018.
* [PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/pretzel.pdf). Lee et. al, OSDI, 2018.
* [Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Applied-ML-Facebook.pdf), Hazelwood et. al, HPCA, 2018.
* [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/mxnet.pdf)Chen et. al, Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015.
* [Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/graphlab.pdf), Low et al, VLDB, 2012.
* [Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters](https://i.cs.hku.hk/~cwu/papers/yhpeng-eurosys18.pdf). Peng et. al, EuroSys, 2018.
* [Tiresias: A GPU Cluster Manager for Distributed Deep Learning.](https://www.usenix.org/system/files/nsdi19-gu.pdf) Gu et. al, NSDI, 2019.
* [Janus: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs](https://www.usenix.org/system/files/nsdi19-jeong.pdf). Jeong et. al, 2018.
* [KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analystics](https://arxiv.org/pdf/1610.09451.pdf), Sparks et al, ICDE, 2017.
* [Project Adam: Building an Efficient and Scalable Deep Learning Training System](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-chilimbi.pdf), Chilimbi et al, OSDI, 2014.
* [DimmWitted: A Study of Main-Memory Statistical Analytics.](https://arxiv.org/pdf/1403.7550.pdf) Zhang and Re, VLDB, 2014.

### Applications: Batch Analytics and SQL Frameworks   

* [Spark SQL: Relational Data Processing in Spark](http://pages.cs.wisc.edu/~shivaram/cs744-readings/SparkSQL.pdf), Armburst et al, SIGMOD, 2015.
* [Major technical advancements in Apache Hive](https://dl.acm.org/doi/pdf/10.1145/2588555.2595630?casa_token=ZlvOATG83ygAAAAA:djEfbpxdc2Sg7gUELmsqv4EFFles2skxhxw7sgZOeaBq5zxkKL_RYFlZwry62hAYdVA4eQi3xT3d), Huai et al, SIGMOD, 2014.
* [Clarinet: WAN-Aware Optimization for Analytics Queries](http://pages.cs.wisc.edu/~akella/CS744/S19/838-CloudPapers/Clarinet.pdf), Viswanathan et al, OSDI, 2016.
* [Global Analytics in the Face of Bandwidth and Regulatory Constraints,](https://www.usenix.org/system/files/conference/nsdi15/nsdi15-paper-vulimiri.pdf) Vulimiri et al, NSDI, 2015.
* [SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets](http://www.vldb.org/pvldb/vol1/1454166.pdf). Chaiken et al, VLDB 
* [The Snowflake Elastic Data Warehouse.](http://info.snowflake.net/rs/252-RFO-227/images/Snowflake_SIGMOD.pdf) Dageville et al, SIGMOD 2016. 
* [Building an Elastic Query Engine on Disaggregated Storage](https://www.usenix.org/system/files/nsdi20-paper-vuppalapati.pdf). Vuppalapati et al, NSDI 2020. 
* [Impala: A Modern, Open-Source SQL Engine for Hadoop.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/impala.pdf) Kornacker et. al, CIDR, 2015.
* [Dremel: Interactive Analysis of Web-Scale Datasets.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/dremel.pdf) Melnik et. al, VLDB, 2010.
* [Trill: A High-Performance Incremental Query Processor for Diverse Analytics. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/trill.pdf)Chandramouli et. al, VLDB, 2014.
* [Rethinking SIMD Vectorization for In-Memory Databases.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/rethink-simd.pdf) Polychroniou et. al, SIGMOD, 2015.
* [Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/revisit-joins.pdf)Balkesen et. al, VLDB, 2013.
* [TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/tag.pdf). Madden et. al, OSDI, 2002.

### Applications: Stream Processing 

* [Storm @Twitter](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Storm-Twitter.pdf) , Toshniwal et al, SIGMOD, 2014.
* [Twitter Heron: Stream Processing at Scale](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Heron.pdf), Kulkarni et al, SIGMOD, 2015.
* [Realtime Data Processing at Facebook](https://dl.acm.org/doi/pdf/10.1145/2882903.2904441). Chen et. al, SIGMOD, 2016.
* [Discretized Streams: Fault-Tolerant Streaming Computation at Scale](http://pages.cs.wisc.edu/~shivaram/cs744-readings/dstreams.pdf), Zaharia et al, SOSP, 2013.
* Reading: [Spark Structured Streaming ](https://databricks.com/blog/2016/07/28/structured-streaming-in-apache-spark.html)
* [Apache Flink: Stream and Batch Processing in a Single Engine,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/flink.pdf) Carbone et al, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2015.
* [Kafka Distributed Messaging System for Log Processing,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/kafka.pdf) Kreps et al, NetDB Workshop, 2011.
  * Also this document of comparison of widely used [Queuing Messaging Processing Systems](https://docs.google.com/document/d/1d5ZWLMJb7WfjHDDu21VVGpp4QN9IGSzYkVkETluwU70/edit). 
* [StreamScope: Continuous Reliable Distributed Processing of Big Data Streams](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/StreamScope.pdf), Lin et al, NSDI, 2016.
* [Drizzle: Fast and Adaptable Stream Processing at Scale](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/drizzle.pdf). Venkataraman et. al, SOSP, 2017.
* [Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/chi.pdf). Mai et. al, PVLDB, 2018.
* [Gloss: Seamless Live Reconfiguration and Reoptimization of Stream Programs.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/gloss.pdf) Rajadurai et. al, ASPLOS, 2018.
* [Aurora: a new model and architecture for data stream management.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/aurora.pdf) Abadi et. al, VLDB, 2003.
* [Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/scaling-dataflows.pdf) Kalavri et. al, OSDI, 2018.
* [Naiad: A Timely Dataflow System](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/Naiad.pdf), Murray et al, SOSP, 2013.
* [The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/dataflow.pdf)Akidau et. al, VLDB, 2015.

### Applications: Graph Processing 

* [Pregel: A System for Large-Scale Graph Processing,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/pregel.pdf) Malewicz et al, SIGMOD, 2010.
* [TAO: Facebook’s Distributed Data Store for the Social Graph](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/tao.pdf). Bronson et. al, USENIX ATC, 2013.
* [PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/PowerGraph.pdf), Gonzalez et al, OSDI, 2012.
* [GraphX: Graph Processing in a Distributed Dataflow Framework,](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/GraphX.pdf) Gonzalez et al, OSDI, 2014.
* [PyTorch-BigGraph: A Large-Scale Graph Embedding System. ](https://arxiv.org/pdf/1903.12287.pdf)Lerer et al, Proceedings of the 2nd SysML Conference, 2019. 
* [Scalability! But at what COST?](http://pages.cs.wisc.edu/~shivaram/cs744-readings/COST.pdf) McSherry et al, HOTOS 2015. 
* [Arabesque: A System for Distributed Graph Mining. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/arabesque.pdf)Teixeira et. al, SOSP, 2015.
* [Fast and Concurrent RDF Queries with RDMA-based Distributed Graph Exploration.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/RDF.pdf) Shi et. al, OSDI, 2016.
* [ASAP: Fast, Approximate Pattern Mining at Scale.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/ASAP.pdf) Iyer et. al, OSDI, 2018.
* [Grappa: A Latency-Tolerant Runtime for Large-Scale Irregular Applications.](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/grappa.pdf) Nelson et. al, USENIX ATC, 2015.
* [One Trillion Edges: Graph Processing at Facebook-Scale. ](https://www2.cs.uic.edu/~brents/cs494-cdcs/papers/trillion-edges.pdf)Ching et. al, VLDB, 2015.

### Potpourri: Runtime, New Hardware Models, Serverless, and Approximation 

* Runtime
  * [Weld: A Commom Runtime for High Performance Data Analytics](https://people.csail.mit.edu/malte/pub/papers/2017-cidr-weld.pdf), Palkar et al, CIDR, 2017.
* Hardware 
  * [In-Datacenter Performance Analysis of a Tensor Processing Unit](http://pages.cs.wisc.edu/~shivaram/cs744-readings/tpu.pdf), Jouppi et al, CIDR, 2017.
  * [A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Catapult_ISCA_2014.pdf) Putnam et. al, ISCA, 2014.
  * [Strata: A Cross Media File System](https://www.cs.utexas.edu/users/witchel/pubs/kwon17sosp-strata.pdf). Kwon et. al, SOSP, 2017.
* Serverless 
  * [Occupy the Cloud: Distributed Computing for the 99%](https://shivaram.org/publications/pywren-socc17.pdf), Jonas et al, SoCC, 2017.
  * [Serverless Computation with OpenLambda. ](https://www.usenix.org/system/files/conference/hotcloud16/hotcloud16_hendrickson.pdf)Hendrickson et. al, HotCloud, 2016.
  * [Pocket: Elastic Ephemeral Storage for Serverless Analytics.](https://www.usenix.org/system/files/osdi18-klimovic.pdf) Klimovic et. al, OSDI, 2018.
  * [Peeking Behind the Curtains of Serverless Platforms,](https://www.usenix.org/system/files/conference/atc18/atc18-wang-liang.pdf) Wang et. al, USENIX ATC, 2018.
  * [SOCK: Rapid Task Provisioning with Serverless-Optimized Containers](https://www.usenix.org/system/files/conference/atc18/atc18-oakes.pdf), Oakes et. al, USENIX ATC, 2018.
* Approximation
  * [BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data,](https://sameeragarwal.github.io/blinkdb_eurosys13.pdf) Agarwal et al, Eurosys, 2013.
  * [BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees](https://dl.acm.org/doi/pdf/10.1145/3299869.3300077). Park et. al, SIGMOD, 2019.
  * [Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/quickr-2.pdf). Kandula et. al, SIGMOD, 2016.
* Other: RDMA 
  * [FaRM: Fast Remote Memory. ](https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-dragojevic.pdf)Dragojevic et. al, NSDI, 2014.
  * [No compromises: distributed transactions with consistency, availability, and performance. ](https://pdos.csail.mit.edu/6.824/papers/farm-2015.pdf)Dragojevic et. al, SOSP, 2015.
  * [FaSST: Fast, Scalable and Simple Distributed Transactions with Two-Sided (RDMA) Datagram RPCs.](https://www.usenix.org/system/files/conference/osdi16/osdi16-kalia.pdf) Kalia et. al, OSDI, 2016.
  * [Datacenter RPCs can be General and Fast. ](https://www.usenix.org/system/files/nsdi19-kalia.pdf)Kalia et. al, NSDI, 2019.
  * [Distributed Lock Management with RDMA: Decentralization without Starvation. ](https://dl.acm.org/doi/pdf/10.1145/3183713.3196890)Yoon et. al, SIGMOD, 2018.
  * [Efficient Memory Disaggregation with Infiniswap. ](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-gu.pdf)Gu et. al, NSDI, 2017.
  * [Accelerating Relational Databases by Leveraging Remote Memory and RDMA. ](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/p416-li-1.pdf)Li et. al, SIGMOD, 2016.
  * [Remote Memory in the Age of Fast Networks. ](https://nadav.amit.zone/publications/aguilera2017remote.pdf)Aguilera et. al, SoCC, 2017.
* Other: Offload 

  * [Floem: A Programming System for NIC-Accelerated Network Applications.](https://www.usenix.org/system/files/osdi18-phothilimthana.pdf) Phothilimthana et. al, OSDI, 2018.
  * [iPipe: A Framework for Building Datacenter Applications Using In-networking Processors.](https://dada.cs.washington.edu/research/tr/2018/09/UW-CSE-18-09-02.pdf) Liu et. al, 2018.
  * [Direct Universal Access: Making Data Center Resources Available to FPGA. ](https://www.usenix.org/system/files/nsdi19-shu.pdf)Shu et. al, NSDI, 2019.