Deadlock detection in distributed databases

This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.

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Modelling and optimization of information systems with distributed databases

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1155/1/012062 ◽

2021 ◽

Vol 1155 (1) ◽

pp. 012062

Author(s):

I Ya Lvovich ◽

Ya E Lvovich ◽

A P Preobrazhenskiy ◽

Yu P Preobrazhenskiy ◽

O N Choporov

Keyword(s):

Information Systems ◽

Distributed Databases ◽

Modelling And Optimization

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A survey of tasking deadlock detection methods

ACM SIGAda Ada Letters ◽

10.1145/122037.122040 ◽

1991 ◽

Vol XI (1) ◽

pp. 82-91 ◽

Cited By ~ 5

Author(s):

Jingde Cheng

Keyword(s):

Detection Methods ◽

Deadlock Detection

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State Management for Cloud-Native Applications

Electronics ◽

10.3390/electronics10040423 ◽

2021 ◽

Vol 10 (4) ◽

pp. 423

Author(s):

Márk Szalay ◽

Péter Mátray ◽

László Toka

Keyword(s):

Large Scale ◽

Distributed Databases ◽

Access Time ◽

Replica Placement ◽

State Management ◽

Placement Decisions ◽

Dynamic Replication ◽

Cloud Databases ◽

Placement Algorithm ◽

The One

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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