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1. TransKV: A Networking Support for Transaction Processing in Distributed Key-value Stores

   https://doi.org/10.1109/BigDataService52369.2021.00011  

   Eldakiky, Hebatalla; Du, David Hung-Chang (August 2021, IEEE International Conference on Big Data Computing Service and Applications (BigDataService))

   IEEE (Ed.)

   Through the massive use of mobile devices, data clouds, and the rise of Internet of Things, enormous amount of data has been generated and analyzed for the benefit of society. NoSQL Databases and specially key-value stores be­ come the backbone in managing these large amounts of data. Most of key-value stores ignore transactions due to their ef­fect on degrading key-value store's performance. Meanwhile, programmable switches with the software-defined networks and the Programming Protocol-Independent Packet Processor (P4) lead to a programmable network where in-network computa­ tion can help accelerating the performance of applications. In this paper, we proposed a networking support for transaction processing in distributed key-value stores. Our system leverages the programmable switch to act as a transaction coordinator. Using a variation of the time stamp ordering concurrency control approach, the programmable switch can decide to proceed in transaction processing or abort the transaction directly from the network. Our experimental results on an initial prototype show that our proposed approach, while supporting transactions, improves the throughput by up to 4X and reduces the latency by 35% when compared to the existing architectures. 

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2. Pegasus: Tolerating Skewed Workloads in Distributed Storage with In-Network Coherence Directories

   Li, Jialin; Nelson, Jacob; Michael, Ellis; Jin, Xin; Ports, Dan R. (November 2020, 14th USENIX Symposium on Operating Systems Design and Implementation)

   null (Ed.)

   High performance distributed storage systems face the challenge of load imbalance caused by skewed and dynamic workloads. This paper introduces Pegasus, a new storage system that leverages new-generation programmable switch ASICs to balance load across storage servers. Pegasus uses selective replication of the most popular objects in the data store to distribute load. Using a novel in-network coherence directory, the Pegasus switch tracks and manages the location of replicated objects. This allows it to achieve load-aware forwarding and dynamic rebalancing for replicated keys, while still guaranteeing data coherence and consistency. The Pegasus design is practical to implement as it stores only forwarding metadata in the switch data plane. The resulting system improves the throughput of a distributed in-memory key-value store by more than 10x under a latency SLO -- results which hold across a large set of workloads with varying degrees of skew, read/write ratio, object sizes, and dynamism. 

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3. Modular Switch Programming Under Resource Constraints

   Hogan, Mary; Feibish, Shir Landau; Arashloo, Mina Tahmasbi; Rexford, Jennier; Walker, David (April 2022, USENIX Symposium on Networked Systems Design and Implementation)

   Programmable networks support a wide variety of applications, including access control, routing, monitoring, caching, and synchronization. As demand for applications grows, so does resource contention within the switch data plane. Cramming applications onto a switch is a challenging task that often results in non-modular programming, frustrating “trial and error” compile-debug cycles, and suboptimal use of resources. In this paper, we present P4All, an extension of P4 that allows programmers to define elastic data structures that stretch automatically to make optimal use of available switch resources. These data structures are defined using symbolic primitives (that parameterize the size and shape of the structure) and objective functions (that quantify the value gained or lost as that shape changes). A top-level optimization function specifies how to share resources amongst data structures or applications. We demonstrate the inherent modularity and effectiveness of our design by building a range of reusable elastic data structures including hash tables, Bloom filters, sketches, and key-value stores, and using those structures within larger applications. We show how to implement the P4All compiler using a combination of dependency analysis, loop unrolling, linear and non-linear constraint generation, and constraint solving. We evaluate the compiler’s performance, showing that a range of elastic programs can be compiled to P4 in few minutes at most, but usually less. 

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4. Achieving high throughput and elasticity in a larger-than-memory store

   https://doi.org/10.14778/3457390.3457406  

   Kulkarni, Chinmay; Chandramouli, Badrish; Stutsman, Ryan (April 2021, Proceedings of the VLDB Endowment)

   Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest events over the network and scale across cloud resources elastically. We present Shadowfax, a new distributed KVS based on FASTER, that transparently spans DRAM, SSDs, and cloud blob storage while serving 130 Mops/s/VM over commodity Azure VMs using conventional Linux TCP. Beyond high single-VM performance, Shadowfax uses a unique approach to distributed reconfiguration that avoids any server-side key ownership checks or cross-core coordination both during normal operation and migration. Hence, Shadowfax can shift load in 17 s to improve system throughput by 10 Mops/s with little disruption. Compared to the state-of-the-art, it has 8x better throughput (than Seastar+memcached) and avoids costly I/O to move cold data during migration. On 12 machines, Shadowfax retains its high throughput to perform 930 Mops/s, which, to the best of our knowledge, is the highest reported throughput for a distributed KVS used for large-scale data ingestion and indexing. 

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5. Poster: A Fast Monitor for Slow Network Attacks

   https://doi.org/10.1109/Cloud-Summit61220.2024.00032  

   Wei, Cuidi; Tu, Shaoyu; Hasegawa, Toru; Koizumi, Yuki; Ramakrishnan, K K; Takemasa, Junji; Wood, Timothy (June 2024, IEEE)

   Recent work has demonstrated how programmable switches can effectively detect attack traffic, such as denial-of- service attacks in the midst of high-volume network traffic. However, these techniques primarily rely on sampling- or sketch- based data structures that can only be used to approximate the characteristics of dominant flows in the network. As a result, such techniques are unable to effectively detect slow attacks such as SYN port scans, SSH brute forcing, or HTTP connection exploits, which do so by stealthily adding only a few packets to the network. In this work we explore how the combination of programmable switches, Smart network interface cards (sNICs), and hosts can enable fine-grained analysis of every flow in a cloud network, even those with only a small number of packets. We focus on analyzing packets at the start of each flow, as those packets often can help indicate whether a flow is benign or suspicious, e.g., by detecting an attack which fails to complete the TCP handshake in order to waste server connection resources. Our approach leverages the high-speed processing of a programmable switch while overcoming its primary limitation - very limited memory capacity - by judiciously sending some state for processing to the sNIC or the host which typically has more memory, but lower bandwidth. Achieving this requires careful design of data structures on the switch, such as a bloom filter and flow logs, and communication protocols between the switch, sNIC , and host, to coordinate state. 

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