# A Survey on Network Optimization Techniques for Blockchain Systems
## 6 Gap Analysis  
Despite all the efforts that have been put into making blockchain scalable, there is still room for further research. This section discusses some identified gaps and directions for possible future work. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **PC** & **PR** & **BI** & **CL** & **DL** & **LY** & **TT** & **Main Contributions** & **Limitations** \\ \hline \multirow{3}{*}{[100]} & \multirow{3}{*}{✓} & & & & & & & & Methodology to construct optimum network based on empirical data & May not scale well with peers \\ \cline{1-1} \cline{5-10}  & & & & & & & & & A DRL algorithm to select optimum values for tunable parameters of the blockchain & Single agent may not scale well with an increase in peers \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Multi-Layer Techniques. PC = Public Blockchain, PR = Private Blockchain, BI = Blockchain-IoT, CL = Consensus Layer, DL = Data Layer, LY = Latency, TT = Transactional Throughput. Some blockchain frameworks allow network administrators to configure block size, interval and other parameters manually. Therefore, to construct an optimum blockchain network with improved transactional throughput, some researchers conducted extensive measurements to determine the impact of parameters on the blockchain's ability to process transactions. After analyzing the experimental results, guidelines on selecting the best value for each parameter were presented. However, since these guidelines were extracted from a setup consisting of a few nodes, the approach may not suit the blockchain when it scales up with the number of peers. Therefore, researchers could investigate optimization algorithms that will take the network's size and other relevant parameters as input and automatically determine and apply the optimum value for all the configurable blockchain parameters. 2. Deep Reinforcement Learning has been applied to automatically select tunable blockchain parameters, such as consensus algorithm and block size. In principle, a DRL agent persistently interacts with its environment and takes actions that will converge to an optimal state. However, persistently interacting with the blockchain network will impede its normal operation and subsequently bottleneck its transactional throughput. Therefore, future work could research how to achieve a trade-off between maximized performance and minimized interaction. 3. Solutions proposed as a better alternative to the random neighbor selection used in current blockchain implementations suggests selecting neighbors based on proximity or latency of communication. Hence, each peer selects neighbors offering the least communication latency during the neighbor selection stage. Furthermore, if many peers consider a specific peer as having low-latency communication, they will all select it. Consequently, it will lead to that peer having too many neighbors and, consequently, overloading it. Therefore, further research could examine neighbor selection strategies and limit the number of neighbors based on network size, a peer's computing and network resources. 4. Blockchain is a distributed technology, hence its P2P network management is also distributed, thus, all peers are responsible for setting up and managing the P2P network. However, this approach has significant network management overhead and does not easily lend itself to a dynamic reconfiguration. Therefore, to make P2P management more flexible and minimize its management overhead, researchers could consider developing intelligent semi-distributed techniques [84] to manage the P2P of blockchain networks. Thus, some peers in the blockchain are assigned special responsibilities. For example, they are tasked with calculating the optimum P2P topology and selecting suitable neighbors for every peer in the network. 5. Using semi-distributed strategies for P2P network management will be computationally intensive when the blockchain peers increases.