How To Optimize My Blockchain? A Multi-Level Recommendation Approach

Abstract

Aside from the conception of new blockchain architectures, existing blockchain optimizations in the literature primarily focus on system or data-oriented optimizations within prevailing blockchains. However, since blockchains handle multiple aspects ranging from organizational governance to smart contract design, a holistic approach that encompasses all the different layers of a given blockchain system is required to ensure that all optimization opportunities are taken into consideration. In this vein, we define a multi-level optimization recommendation approach that identifies optimization opportunities within a blockchain at the system, data, and user level. Multiple metrics and attributes are derived from a blockchain log and nine optimization recommendations are formalized. We implement an automated optimization recommendation tool, BlockOptR, based on these concepts. The system is extensively evaluated with a wide range of workloads covering multiple real-world scenarios. After implementing the recommended optimizations, we observe an average of 20% improvement in the success rate of transactions and an average of 40% improvement in latency.

Transaction Conflict Control in Hyperledger Fabric: A Taxonomy, Gaps, and Design for Conflict Prevention

“…The state-of-the-art of MVCC conflict control (which, as a concept, we will define shortly) primarily focuses on the clients emitting transactions in an unfortunate manner and the transactions being ordered into blocks in a conflicting way. Only limited research (only the recent publication [9] seems to formulate an initial systematic approach) addresses the appropriateness of the way we map application concepts to the ledger key-value store to support a given business logic and workload profile setting. As some of our contributions investigate this latter aspect, we demonstrate this duality in a simple example, as depicted on Figure 2.…”

“…For Hyperledger Fabric performance optimization, [9] proposes a multi-level optimization model, distinguishing the user level, the data level and the system level and describes the BlockOptR tool for event log and process mining based optimization. While the user-data-system trichotomy is a good framework for the performance optimization of an already established system, we propose a taxonomy better suited to designing for conflict control, based on the widely accepted classic concepts of the dependability of computer and communication systems [12].…”

“…2) Follow-up reference chaining: for this paper, using Google Scholar again, we performed forward snowballing on the reference set of [5] for 2022 and 2023. 3) Cross-referencing with systematic analysis: we became aware of [9] through Google Scholar recommendations on its publication. We cross-referenced it with our references (not finding significant relevant deficiencies on our side).…”

“…Client workload Rate control [9] Fully transparent Business process User activity reordering [9] Process model pruning [9] Data model and storage mapping Bitcoin-style UTXO [3] Readless writes and aggregation [15] Delta writes [9] "Data model alterations" [9] Chaincode software architecture Smart contract partitioning [9] Network config. many approaches are naturally composite, especially those which provide conflict tolerance for some conflicting requests and perform early aborts for the remainder.…”

“…Conflict prevention can be performed from the point of view of numerous aspects, which we synthesized based on [5] and [9]. Although this is where the potential for some genuinely robust solutions lies from a system engineering point of view, important avenues of conflict prevention are far less developed than the techniques addressing potential and actual conflicts.…”