Adding Capacity-Aware Storage Indirection to Homogeneous Distributed Hash Tables

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

Section: Quantify Node's Capacitymentioning

confidence: 99%

“…The papers of Suselbeck et al . and Wetti and Graffi use the bottleneck rate at which data can be sent into the network as a node capacity. In this paper, we focus on the NoSQL node's performance.…”

Section: Optimizing Data Placementmentioning

confidence: 99%

Optimizing data partition for scaling out NoSQL cluster

Huang

Wang

Zhong

et al. 2015

“…For that the responsible node maintains a list of receivers and forwards the file request to individual nodes from this list. 56 Which previous receiver is selected can be chosen based on various factors. However, for popular files, many receivers are available to serve the file even further.…”

Section: Traffic Load Balancingmentioning

confidence: 99%

“…We extended PAST to harness, in such cases, the large group of file receivers to spread out the data themselves. For that the responsible node maintains a list of receivers and forwards the file request to individual nodes from this list 56 . Which previous receiver is selected can be chosen based on various factors.…”

Section: The P2p Frameworkmentioning

confidence: 99%

LibreSocial: A peer‐to‐peer framework for online social networks

Graffi

Masinde

2020

Distributed online social networks (DOSNs) were first proposed to solve the problem of privacy, security, and scalability. A significant amount of research was undertaken to offer viable DOSN solutions that were capable of competing with the existing centralized OSN applications such as Facebook, LinkedIn, and Instagram. This research led to the emergence of the use of peer-to-peer (P2P) networks as a possible solution, upon which several OSNs such as LifeSocial.KOM, Safebook, PeerSoN among others were based. In this article, we define the basic requirements for an P2P OSN. We then revisit one of the first P2P-based OSNs, LifeSocial.KOM, that is now called LibreSocial, which evolved in the past years to address the challenges of running a completely decentralized social network. Over the course of time, several essential new technologies have been incorporated within LibreSocial for better functionalities. In this article we describe the architecture and each individual component of LibreSocial and point out how LibreSocial meets the basic requirements for a fully functional distributed OSN.

“…For unstructured overlays, Gnutella 0.4 , Gnutella 0.6 , and Gia are implemented as representatives. For the structured variant, Kademlia , Pastry , Content addressable Network (CAN) , and multiple Chord alternatives (EpiChord , ReChord , Ca‐ReChord , and Adaptive Chord ) are ready to use. Furthermore, the simulator comes with two centralized overlays for comparison: one with a centralized index and one with a centralized storage.…”

Section: Simulator Architecturementioning

confidence: 99%

Systematic evaluation of peer‐to‐peer systems using PeerfactSim.KOM

Feldotto

Graffi

2015

Summary Comparative evaluations of peer‐to‐peer protocols through simulations are a viable approach to judge the performance and costs of the individual protocols in large‐scale networks. In order to support this work, we present the peer‐to‐peer system simulator PeerfactSim.KOM, which we extended over the last years. PeerfactSim.KOM comes with an extensive layer model to support various facets and protocols of peer‐to‐peer networking. In this article, we describe PeerfactSim.KOM and show how it can be used for detailed measurements of large‐scale peer‐to‐peer networks. We enhanced PeerfactSim.KOM with a fine‐grained analyzer concept, with exhaustive automated measurements and gnuplot generators as well as a coordination control to evaluate sets of experiment setups in parallel. Thus, by configuring all experiments and protocols only once and starting the simulator, all desired measurements are performed, analyzed, evaluated, and combined, resulting in a holistic environment for the comparative evaluation of peer‐to‐peer systems. An immediate comparison of different configurations and overlays under different aspects is possible directly after the execution without any manual post‐processing. Copyright © 2015 John Wiley & Sons, Ltd.