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Performance Modeling & Analysis of H...
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Sukhwani, Harish.
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Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network).
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network)./
作者:
Sukhwani, Harish.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
178 p.
附註:
Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
Contained By:
Dissertations Abstracts International80-07B.
標題:
Computer Engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10976818
ISBN:
9780438803268
Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network).
Sukhwani, Harish.
Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network).
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 178 p.
Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
Thesis (Ph.D.)--Duke University, 2019.
This item must not be sold to any third party vendors.
A blockchain is an immutable record of transactions (called ledger) between a distributed set of mutually untrusting peers. Although blockchain networks provide tremendous benefits, there are concerns about whether their performance would be a hindrance to its adoption. Our research is focused on Hyperledger Fabric (HLF), which is an open-source implementation of a distributed ledger platform for running smart contracts in a modular architecture. This thesis presents our research on performance modeling of Hyperledger Fabric using a Stochastic Petri Nets modeling formalism known as Stochastic Reward Nets (SRN). We capture the key system operations and complex interactions between them. We focus on two different releases of HLF, viz. v0.6 and v1.0+ (V1). HLF v0.6 follows a traditional state-machine replication architecture followed by many other blockchain platforms, whereas HLF V1 follows a novel execute-order-validate architecture. We parameterize and validate our models with data collected from a real-world Fabric network setup. Our models provide a quantitative framework that helps compare different deployment configurations of Fabric and make design trade-off decisions. It also enables us to compute performance for a system with proposed architectural improvements before they are implemented. From our analysis, we recommend design improvements along with the estimates of performance improvement. Overall, our models provide a stepping stone to the Hyperledger Fabric community towards achieving optimal performance of Fabric in the real-world deployments.
ISBN: 9780438803268Subjects--Topical Terms:
1567821
Computer Engineering.
Performance Modeling & Analysis of Hyperledger Fabric (Permissioned Blockchain Network).
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A blockchain is an immutable record of transactions (called ledger) between a distributed set of mutually untrusting peers. Although blockchain networks provide tremendous benefits, there are concerns about whether their performance would be a hindrance to its adoption. Our research is focused on Hyperledger Fabric (HLF), which is an open-source implementation of a distributed ledger platform for running smart contracts in a modular architecture. This thesis presents our research on performance modeling of Hyperledger Fabric using a Stochastic Petri Nets modeling formalism known as Stochastic Reward Nets (SRN). We capture the key system operations and complex interactions between them. We focus on two different releases of HLF, viz. v0.6 and v1.0+ (V1). HLF v0.6 follows a traditional state-machine replication architecture followed by many other blockchain platforms, whereas HLF V1 follows a novel execute-order-validate architecture. We parameterize and validate our models with data collected from a real-world Fabric network setup. Our models provide a quantitative framework that helps compare different deployment configurations of Fabric and make design trade-off decisions. It also enables us to compute performance for a system with proposed architectural improvements before they are implemented. From our analysis, we recommend design improvements along with the estimates of performance improvement. Overall, our models provide a stepping stone to the Hyperledger Fabric community towards achieving optimal performance of Fabric in the real-world deployments.
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