Dynamic Action Scheduling in a Parallel Database System

This paper describes a scheduling technique for parallel database systems to obtain high performance, both in terms of response time and throughput. The technique enables both intra- and inter-transaction parallelism while controlling concurrency between transactions correctly. Scheduling is performed dynamically at transaction execution time, taking into account dynamic aspects of the execution and allowing parallelism between the scheduling and transaction execution processes. The technique has a solid conceptual background, based on a simple graph-based approach. The usability and effectiveness of the technique are demonstrated by implementation in and measurements on the parallel PRISMA database system

An Efficient Framework for Execution of Smart Contracts in Hyperledger Sawtooth

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are self-executing codes of agreement between interested parties commonly implemented using blockchains. A block contains a set of transactions representing changes to the system and a hash of the previous block. The SCTs are executed multiple times during the block production and validation phases across the network. The execution is sequential in most blockchain technologies. In this work, we incorporate a direct acyclic graph (DAG) based parallel scheduler framework for concurrent execution of SCTs. The dependencies among a block's transactions are represented through a concurrent DAG data structure that assists in throughput optimization. We have created a DAG scheduler module that can be incorporated into blockchain platforms for concurrent execution with ease. We have also formally established the safety and liveness properties of the DAG scheduler. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2.6. The performance across multiple smart contract applications is measured for various scheduler types. Experimental analysis shows that the proposed framework achieves notable performance improvements over the parallel SCT execution frameworks