Abstract:
In this paper, we propose a new scheme OMCEM that optimizes the memory consumption
of the Ethereum mempool. Our work will be as follows: at first, we Transform Ethereum
mempool to a bloom filter based scheme with some benefits such as it reduces the
mempool to a fraction of the current size, and it offers resilience against DDoS attacks,
and It also facilitates setting up Ethereum node on low resource devices such as embedded
systems and system with low computation resources. Second, we will also perform a
simulation on the Ethereum transaction log of over one month and offer a detailed
analysis of its performance. The methodology for this paper we are using is the concept
of bloom filter as Bloom filters are used to help identify transactions that are unknown
to a node or in a block. Our proposed system will check if a specific transaction is
already in the Ethereum transaction mempool? If yes, then the transaction is dropped
and not forwarded to peers. If it is the first time that the node is seeing the transaction,
it is added to the Ethereum bloom filter mempool and forwarded to peers. Similarly,
transactions can be removed from the Ethereum mempool by removing the transaction
from the Ethereum bloom filter mempool. Since, the bloom filter and its variants are
probabilistic structures, telling us whether TX is in the mempool or definitely not
in the mempool. We also evaluate the impact of design parameters such as bloom filter and counting bloom filter dimensions, on performance metrics (space-time trade off and accuracy). We implement our model in C++ and perform a simulation on a
month of Ethereum logs which comprises about a million transactions and analyze the
performance of OMCEM.
