Blockchain technology is a unique distributed ledger technology that has been widely used in various areas. With the increase in data on the blockchain and the append-only nature of the blockchain, the traditional blockchain’s full replica storage technique leads to blockchain storage scalability problem. Existing methods prioritize minimizing the storage strain on blockchain nodes while ignoring the availability of data, resulting in a lengthy average response time for users to access the blockchain. To address the shortcomings, this paper proposes an Information-Centric Networking-based blockchain storage architecture. The architecture uses the enhanced resolution system for community division to build blockchain node partitions and store blockchain ledgers in the underlying network. It introduces virtual chain for rapid blockchain indexing and adopts a collaborative block replica deletion algorithm across neighboring partitions, including replica number decision based on blockchain access decay characteristics and replica deletion based on resource relationship. Finally, we compare and analyze the proposed blockchain storage architecture with BC-store and KASARASA, and the results demonstrate that this architecture has significantly lower average access time than others. The replica data volume of this method is reduced by 57.2% compared to the full replica policy, but the access time is only 5.2% slower when compared to the full replica policy, which substantially increases the replica storage utilization.

In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.

Many bandwidth-intensive applications (such as online live, online games, etc.) are more suitable for using multicast to transmit information. Due to the advantages in scalability, Shared Tree (ST) is more suitable for large-scale deployment than Source-Based Tree (SBT). However, in ST-based multicast, all multicast sources need to send multicast data to a center node called a core, which will lead to core overload and traffic concentration. Besides, most existing multicast protocols use the shortest path between the source or the core and each receiver to construct the multicast tree, which will result in traffic overload on some links. In this paper, we propose an Identifier and Locator Decoupled Multicast approach (ILDM) based on Information-Centric Networking (ICN). ILDM uses globally unique names to identify multicast services. For each multicast service, the mapping between the multicast service name and the addresses of multicast tree nodes is stored in the Name Resolution System (NRS). To avoid core overload and traffic aggregation, we presented a dynamic core management and selection mechanism, which can dynamically select a low-load core for each multicast service. Furthermore, we designed a path state-aware multicast tree node selection mechanism to achieve traffic load balancing by using low-load links more effectively. Experimental results showed that our proposed multicast approach outperformed some other multicast methods in terms of core load, number of join requests, link load, traffic concentration, and routing state.

Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.

Memory management has become an important part in developing real-time and embedded applications. The conventional dynamic storage allocation (DSA) algorithms have been considered inappropriate for real-time applications due to the unbounded response time and fragmentation problem. There are some new allocators designed to meet the requirements nowadays. Two-Level Segregated Fit memory allocator (TLSF) was proposed and widely used in the Real-Time Operating Systems (RTOS) like RTLinux. It has a bounded response time and quite good performance. However, there are still some problems with this algorithm and it could be improved. Here we discuss some improvements of the TLSF algorithm. Experiments show it has low fragmentation and faster response time.

Software-defined networking (SDN) separates the control plane and the data plane, which provides network applications with global network topology and the flexibility to customize packet forwarding rules. SDN has a wide range of innovative applications in 5G, Internet of Things, and information center networks. However, the match-action programming model represented by OpenFlow/Protocol Oblivious Forwarding (POF) in SDN can only process limited types of data such as packets and metadata, making it hard to fulfill future network applications. In this paper, data type and data location are added in the matching fields and actions to make the match-action table (MAT) compatible with multiple types of data, hence improving the data plane’s programmability. Data type helps the MAT to perceive multiple types of data, allowing them to be processed by a single MAT. Data location allows MAT to be decoupled from data meaning, quickly locating specific data in the switch. Based on Intel’s Data Plane Development Kit (DPDK), we design and implement a pipeline that is compatible with multiple types of data processing. Protocol and data type oblivious match-action tables and atomic instructions are included in the pipeline. Experiments show that representing data with data type and data location makes the pipeline compatible with multiple types of data without sacrificing forwarding performance, fulfilling the needs of network applications to handle a variety of types of data while avoiding repeating hardware design.