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Paiva, J.P. Sucena; Ruivo, Pedro; Romano, Paolo; Rodrigues, L.

doi:10.1145/2641573

Cited by 26 publications

(4 citation statements)

References 36 publications

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“…For the state-to-NF assignment, we included the master instance as a shadow NF (line 34), as it is considered to be writing the slaves. The third and final step was to find the target hosts for all master/slave instances (line [36][37][38][39]. We calculated the optimal host initially for the master, based on its weighted total distance from the hosts of the assigned NFs.…”

Section: Proposed Heuristic Placement Algorithmmentioning

confidence: 99%

“…In ref. [37], the authors present a reactive optimizer for data placement that leverages locality patterns in data accesses, to minimize inter-node communication. Because of the reactive nature of their optimization solution, the authors assume that data access frequencies do not change significantly during the entire optimization process.…”

Section: Data/replica Placementmentioning

confidence: 99%

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State Management for Cloud-Native Applications

2021

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The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

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Section: Proposed Heuristic Placement Algorithmmentioning

confidence: 99%

Section: Data/replica Placementmentioning

confidence: 99%

State Management for Cloud-Native Applications

2021

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“…Few studies consider vertical scaling, e.g. [6,18], and configuration tuning [6,12,22,28]. While Horizontal scaling, vertical scaling and configutation tuning approaches are somentime mixed, optimal placement (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…In [22] the authors propose AutoPlacer a mechanisms to self-tune the placement of replicas in distributed key-value stores. Their goal is to minimize the cost of replicas in term of overall latency.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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Optimizing data partition for scaling out NoSQL cluster

Huang

Wang

Zhong

et al. 2015

Concurrency and Computation

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Data partition impacts the performance of Not Only SQL (NoSQL) systems significantly. Nowadays, many of the peer-to-peer NoSQL systems use consistent hashing to partition data automatically. These systems use virtual nodes and random data placement methods to divide the consistent hashing ring, which may lead to imbalanced data partition and degrade the overall system performance. The problem is prominent especially for scaling out heterogeneous clusters. Considering the capacity of each node, an imbalance coefficient of data distribution for a cluster is proposed firstly in this paper. Based on the imbalance coefficient, we propose a dynamic programming algorithm to calculate the position of the new coming node in the consistent hashing ring, which expands the consistent hashing ring more evenly without re-shuffling the entire datasets.Simulations and experiments on Cassandra with Yahoo! Cloud Serving Benchmark (YCSB) benchmark show our algorithm is better than the state-of-the-art work. . load balance, and (3) traffic load balance. The last two dimensions can be solved by replication mechanism. Our focus in this paper is the nodes distribution over the ring space.Virtual node is proposed in [10][11][12] to improve scalability and load balanced. Chen et al.[13] propose a hybrid data partitioning strategy for heterogeneous cluster load balance, and divide a node into many virtual nodes according to the node ability for the data storage and processing. However, although each node can store the amount of data, which is proportional to its capacity

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A uto P lacer

Cited by 26 publications

References 36 publications

State Management for Cloud-Native Applications

State Management for Cloud-Native Applications

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Optimizing data partition for scaling out NoSQL cluster

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