PopRing: A Popularity-aware Replica Placement for Distributed Key-Value Store

Cavalcante, Denis M.; Farias, Victor A. E. de; Sousa, Flávio R. C.; Paula, Manoel Rui P.; Machado, Javam C.; Souza, Neuman

doi:10.5220/0006703504400447

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…(7) The request is executed by a worker thread, which reads data (in memory, if present in the cache, or on disk). (8) The replica sends data to the coordinator. (9) The coordinator responds to the client.…”

Section: Scheduling In Persistent and Distributed Key-value Storesmentioning

confidence: 99%

“…It is often observed, for instance, that even if most requests are successfully handled with low latency, some of them suffer from high delays globally impacting responsiveness: this is the famous tail latency problem [10]. The optimization of these metrics in persistent key-value stores has been the subject of extensive research, from the maximization of throughput through careful optimization of internal data structures [25] or CPU overhead minimization [21], to tail latency mitigation through redundant requests [26], popularity-aware data replication [8], or internal I/O scheduling [6]. Among these optimization techniques, the proper scheduling of client requests has received significant attention.…”

Section: Introductionmentioning

confidence: 99%

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Hector: A Framework to Design and Evaluate Scheduling Strategies in Persistent Key-Value Stores

Canon,

Dugois,

Marchal

et al. 2023

Proceedings of the 52nd International Conference on Parallel Processing

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Key-value stores distribute data across several storage nodes to handle large amounts of parallel requests. Proper scheduling of these requests impacts the quality of service, as measured by achievable throughput and (tail) latencies. In addition to scheduling, performance heavily depends on the nature of the workload and the deployment environment. It is, unfortunately, difficult to evaluate different scheduling strategies consistently under the same operational conditions. Moreover, such strategies are often hard-coded in the system, limiting flexibility. We present Hector, a modular framework for implementing and evaluating scheduling policies in Apache Cassandra. Hector enables users to select among several options for key components of the scheduling workflow, from the request propagation via replica selection to the local ordering of incoming requests at a storage node. We demonstrate the capabilities of Hector by comparing strategies in various settings. For example, we find that leveraging cache locality effects may be of particular interest: we propose a new replica selection strategy, called Popularity-Aware, that can support 6 times the maximum throughput of the default algorithm under specific key access patterns. We also show that local scheduling policies have a significant effect when parallelism at each storage node is limited.

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Section: Scheduling In Persistent and Distributed Key-value Storesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hector: A Framework to Design and Evaluate Scheduling Strategies in Persistent Key-Value Stores

Canon,

Dugois,

Marchal

et al. 2023

Proceedings of the 52nd International Conference on Parallel Processing

View full text Add to dashboard Cite

show abstract

“…In another previous work, Cavalcante et al improved the load‐balancing for workloads with data access skew. They defined the replica placement as a multi‐objective optimization that takes into account data access distribution, data redundancy and data movement.…”

Section: Related Workmentioning

confidence: 99%

“…Distributed key‐value stores (KVS) are a well‐established approach for cloud data‐intensive applications mainly because they are capable of successfully managing huge data traffic driven by the explosive growth of different applications such as social networks, e‐commerce, and enterprise. In this work, the focus is on a particular type of KVS, also known as Object Store, which can store and serve any type of data (eg, photo, image, and video) .…”

Section: Introductionmentioning

confidence: 99%

An adaptive replica placement approach for distributed key‐value stores

Filho

Cavalcante

Moreira

et al. 2020

Concurrency and Computation

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The use of distributed key-value stores (KVS) has experienced fast adoption by various applications in recent years due to key advantages such as hypertext transfer protocol-based RESTful application programming interface, high availability and elasticity. Due to great scalability characteristics, KVS systems commonly use consistent hashing as data placement mechanism. Although KVS systems offer many advantages, they were not designed to dynamically adapt to changing workloads which often include data access skew. Furthermore, the underlying physical storage nodes may be heterogeneous and do not expose their performance capabilities to higher level data placement layers. In this paper, we address those issues and propose an essential step toward a dynamic autonomous solution by leveraging deep reinforcement learning.We design a self-learning approach that incrementally changes the data placement, improving the load balancing. Our approach is dynamic in the sense that is capable of avoiding hot spots, that is, overloaded storage nodes when facing different workloads.Also, we design our solution to be pluggable. It assumes no previous knowledge of the storage nodes capabilities, thus different KVS deployments may make use of it. Our experiments show that our method performs well on changing workloads including data access skew aspects. We demonstrate the effectiveness of our approach through experiments in a distributed KVS deployment. K E Y W O R D Sconsistent hashing, deep reinforcement learning, key-value stores, load balancing, replica placement INTRODUCTIONDistributed key-value stores (KVS) are a well-established approach for cloud data-intensive applications 1 mainly because they are capable of successfully managing huge data traffic driven by the explosive growth of different applications such as social networks, e-commerce, and enterprise.In this work, the focus is on a particular type of KVS, also known as Object Store, which can store and serve any type of data (eg, photo, image, and video). 2 Object Store such as Dynamo 3 and OpenStack-Swift 4 have become widely accepted due to its scalability, high capacity, cost-effective storage and reliable REST programming interface. These systems take advantage of peer-to-peer architecture 5 and replication techniques in order to guarantee high availability and scalability. The data placement of KVS systems are commonly based on a distributed hash table (DHT) and consistent hashing (CHT) 6 with virtual nodes. Consistent hashing is a type of hashing that minimizes the amount of data that needs to move when adding or removing storage nodes. Using only the hash of the id of the data one can determine exactly where that data should be. This mapping of hashes to locations is usually known as "ring. " While this strategy provides item-balancing guarantees, it may be not very efficient in balancing the actual workload of the system for the following reasons.Concurrency Computat Pract Exper. 2020;32:e5675. wileyonlinelibrary.com/journal/cpe

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“…However, this eligibility constraint of each task to specific machines prevents achieving optimal performance in current systems. Moreover, loads between machines tend to be heterogeneous [8], [9] due to varying popularities between the keys, which constitutes an additional challenge. Finally, requests vary in size and the moment they are performed cannot be predicted precisely, leading to a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Bounding the Flow Time in Online Scheduling with Structured Processing Sets

Canon

Dugois

Marchal

2022

2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Replication in distributed key-value stores makes scheduling more challenging, as it introduces processing set restrictions, which limits the number of machines that can process a given task. We focus on the online minimization of the maximum response time in such systems, that is, we aim at bounding the latency of each task. When processing sets have no structure, Anand et al. (Algorithmica, 2017) derive a strong lower bound on the competitiveness of the problem: no online scheduling algorithm can have a competitive ratio smaller than Ω(m), where m is the number of machines. In practice, data replication schemes are regular, and structured processing sets may make the problem easier to solve. We derive new lower bounds for various common structures, including inclusive, nested or interval structures. In particular, we consider fixed sized intervals of machines, which mimic the standard replication strategy of key-value stores. We prove that EFT (Earliest Finish Time) scheduling is (3−2/k)-competitive when optimizing maxflow on disjoint intervals of size k. However, we show that the competitive ratio of EFT is at least m − k + 1 when these intervals overlap, even when unit tasks are considered. We compare these two replication strategies in simulations and assess their efficiency when popularity biases are introduced, i.e., when some machines are accessed more frequently than others because they hold popular data. Even though overlapping intervals suffer from a bad worst-case in theory, they enable clusters to reach a maximum load that is up to 50% higher than with disjoint sets.

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PopRing: A Popularity-aware Replica Placement for Distributed Key-Value Store

Cited by 6 publications

References 7 publications

Hector: A Framework to Design and Evaluate Scheduling Strategies in Persistent Key-Value Stores

Hector: A Framework to Design and Evaluate Scheduling Strategies in Persistent Key-Value Stores

An adaptive replica placement approach for distributed key‐value stores

Bounding the Flow Time in Online Scheduling with Structured Processing Sets

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