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Storage Trie Optimization Based on Ethereum Transaction Data
http://doi.org/10.5626/JOK.2024.51.2.110
Interest in blockchain has grown with the increased usage of Ethereum, thus the blockchain state data has exploded, making it difficult for users to participate in the network. In this paper, we propose a method of optimizing the storage trie, which accounts for a significant portion of state data, based on pas transaction data of real Ethereum. By deleting storage trie that never appeared during 1 million blocks from a massive 14 million block storage tire, we reduced the storage space by 19.6%, which is 10.8GB. Based on the research results of this paper, it is expected that we can propose a more effective storage trie optimization based on data.
Performance Analysis of Instruction Priority Functions using a List Scheduling Simulator
Changhoon Chung, Soo-Mook Moon
http://doi.org/10.5626/JOK.2023.50.12.1048
Instruction scheduling is an important compiler optimization technique, for reducing the execution time of a program by parallel processing. However, existing scheduling techniques show limited performance, because they rely on heuristics. This study examines the effect of instruction priority functions on list scheduling, through simulation. As a result, using a priority function based on the overall structure of the dependency graph can reduce schedule length by up to 4%, compared to using a priority function based on the original instruction order. Furthermore, the result gives a direction on which input features should be used when implementing a reinforcement learning-based scheduling model.
Parallel Optimization of Deep Learning Computation Offloading in Edge Computing Environment
Kwang Yong Shin, Soo-Mook Moon
http://doi.org/10.5626/JOK.2022.49.3.256
Computation offloading to edge servers has been proposed as a solution to performing computation-intensive deep learning applications on devices with low hardware capabilities. However, the deep learning model has to be uploaded to the edge server before computation offloading is possible, a non-trivial assumption in the edge server environment. Incremental offloading of neural networks was proposed as a solution as it can simultaneously upload model and offload computation [1]. Although it reduced the model upload time required for computation offloading, it did not properly handle the model creation overhead, increasing the time required to upload the entire model. This work solves this problem by parallel optimization of model uploading and creation, decreasing the model upload time by up to 30% compared to the previous system.
Snapshot-Based Offloading for Web Applications with HTML5 Canvas
InChang Jeong, Hyuk-Jin Jeong, Soo-Mook Moon
http://doi.org/10.5626/JOK.2017.44.9.871
A vast amount of research has been carried out for executing compute-intensive applications on resource-constrained mobile devices. Computation offloading is a method in which heavy computations are dynamically migrated from a mobile device to a server, exploiting the powerful hardware of the server to perform complex computations. An important issue for offloading is the complexity of reconciling the execution state of applications between the server and the client. To address this issue, snapshot-based offloading has recently been proposed, which utilizes the snapshot of a web app as the portable description of the execution state. However, for web applications using the HTML5 canvas, snapshot-based offloading does not function correctly, because the snapshot cannot capture the state of the canvas. In this paper, we propose a code generation technique to save the canvas state as part of a snapshot, so that the snapshot-based offloading can be applied to web applications using the canvas.
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