NetCache

Jin, Xin; Li, Xiaozhou; Zhang, Haoyu; Soulé, Robert; Lee, Jeongkeun; Foster, Nate; Kim, Changhoon; Stoica, Ion

doi:10.1145/3132747.3132764

Cited by 355 publications

(31 citation statements)

References 25 publications

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“…For this reason, we naturally assume that to execute a transaction, a client can communicate with the servers (but not with other clients) and a server communicates with a client only to respond to a client's read or write request. We find evidence of the relevance of this assumption in large-scale production systems, such as Facebook's data platform [45], and in emerging systems [23,29,37] and architectures [36] for fast query processing, where no per-client states are maintained to avoid the corresponding overheads and to achieve the lowest latency. System model.…”

Section: Model and Definitionsmentioning

confidence: 99%

Distributed Transactional Systems Cannot Be Fast

Didona

Fatourou

Guerraoui

et al. 2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

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We prove that no fully transactional system can provide fast read transactions (including read-only ones that are considered the most frequent in practice). Specifically, to achieve fast read transactions, the system has to give up support of transactions that write more than one object. We prove this impossibility result for distributed storage systems that are causally consistent, i.e., they do not require to ensure any strong form of consistency. Therefore, our result holds also for any system that ensures a consistency level stronger than causal consistency, e.g., strict serializability. The impossibility result holds even for systems that store only two objects (and support at least two servers and at least four clients). It also holds for systems that are partially replicated. Our result justifies the design choices of state-of-the-art distributed transactional systems and insists that system designers should not put more effort to design fullyfunctional systems that support both fast read transactions and ensure causal or any stronger form of consistency.

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Section: Model and Definitionsmentioning

confidence: 99%

Distributed Transactional Systems Cannot Be Fast

Didona

Fatourou

Guerraoui

et al. 2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

show abstract

“…Zipf has a skewness parameter β, a higher value of which denotes a more acute imbalance. The most common value for β in the literature is 0.99 [5,8,9,11]; this value is also used in evaluations for KVSwitch (Section 5).…”

Section: Skew and Load Imbalance Of Kvsmentioning

confidence: 99%

“…We generated the query traces following the Zipf distribution as skewed workloads, and the items in KVS are partitioned across storage servers. Three different skewness parameters are used for generating, 0.9, 0.95, and 0.99, which are commonly used in the literature [8,9,12]. In our experiments, the client keeps sending queries for 5 min at a maximum sending rate of about 30 MQPS (Million Queries Per Second) for every test.…”

Section: Experiments Setupsmentioning

confidence: 99%

“…The baseline we compare to is the KVS system without any balancing techniques, marked as Original. Moreover, we also compare the balancing performance between KVSwitch and NetCache [8], with which the hot items are cached in ASIC pipeline in programmable switches to balance the KVS.…”

Section: Experiments Setupsmentioning

confidence: 99%

“…A number of techniques have been proposed to mitigate this well-established skew problem. These techniques can be broadly divided into two classes: the first class [8][9][10] uses a dedicated cache to store hot items and to absorb the associated queries, while the second [3,11,12] replicates hot items across multiple servers and redirects queries to these servers to avoid imbalance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting

Shi¹,

Fei²,

Wen³

et al. 2019

Electronics

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Today’s cloud-based online services are underpinned by distributed key-value stores (KVSs). Keys and values are distributed across back-end servers in such scale-out systems. One primary real-life performance bottleneck occurs when storage servers suffer from load imbalance under skewed workloads. In this paper, we present KVSwitch, a centralized self-managing load balancer that leverages the power and flexibility of emerging programmable switches. The balance is achieved by dynamically predicting the hot items and by creating replication strategies according to KVS loading. To overcome the challenges in realizing KVSwitch given the limitations of the switch hardware, we decompose KVSwitch’s functions and carefully design them for the heterogeneous processors inside the switch. We prototype KVSwitch in a Tofino switch. Experimental results show that our solution can effectively keep the KVS servers balanced even under highly skewed workloads. Furthermore, KVSwitch only replicates 70 % of hot items and consumes 9.88 % of server memory rather than simply replicating all hot items to each server.

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Computing in the Network

Montpetit

Crespi

2021

Shaping Future 6G Networks

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NetCache

Cited by 355 publications

References 25 publications

Distributed Transactional Systems Cannot Be Fast

Distributed Transactional Systems Cannot Be Fast

Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting

Computing in the Network

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