Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Reevaluating Data Stall Time with the Consideration of Data Access Concurrency

Data access delay has become the prominent performance bottleneck of high-end computing systems. The key to reducing data access delay in system design is to diminish data stall time. Memory locality and concurrency are the two essential factors influencing the performance of modern memory systems. However, existing studies in reducing data stall time rarely focus on utilizing data access concurrency because the impact of memory concurrency on overall memory system performance is not well understood. In this study, a pair of novel data stall time models, the L-C model for the combined effort of locality and concurrency and the P-M model for the effect of pure miss on data stall time, are presented. The models provide a new understanding of data access delay and provide new directions for performance optimization. Based on these new models, a summary table of advanced cache optimizations is presented. It has 38 entries contributed by data concurrency while only has 21 entries contributed by data locality, which shows the value of data concurrency. The L-C and P-M models and their associated results and opportunities introduced in this study are important and necessary for future data-centric architecture and algorithm design of modern computing systems.

A Study on Modeling and Optimization of Memory Systems

Accesses Per Cycle(APC),Concurrent Average Memory Access Time(C-AMAT),and Layered Performance Matching(LPM)are three memory performance models that consider both data locality and memory assess concurrency.The APC model measures the throughput of a memory architecture and therefore reflects the quality of service(QoS)of a memory system.The C-AMAT model provides a recursive expression for the memory access delay and therefore can be used for identifying the potential bottlenecks in a memory hierarchy.The LPM method transforms a global memory system optimization into localized optimizations at each memory layer by matching the data access demands of the applications with the underlying memory system design.These three models have been proposed separately through prior efforts.This paper reexamines the three models under one coherent mathematical framework.More specifically,we present a new memorycentric view of data accesses.We divide the memory cycles at each memory layer into four distinct categories and use them to recursively define the memory access latency and concurrency along the memory hierarchy.This new perspective offers new insights with a clear formulation of the memory performance considering both locality and concurrency.Consequently,the performance model can be easily understood and applied in engineering practices.As such,the memory-centric approach helps establish a unified mathematical foundation for model-driven performance analysis and optimization of contemporary and future memory systems.