Kai Zhang;Jiahui Hong;Zhengying He;Yinan Jing;X. Sean Wang
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AdaptChain: Adaptive Data Sharing and Synchronization for NFV Systems on Heterogeneous Architectures
In a Network Function Virtualization (NFV) system, network functions (NFs) are implemented on general-purpose hardware, including CPU, GPU, and FPGA. Studies have shown that there is no one-size-fits-all processor, as each processor demonstrates performance advantages to implement certain types of NFs. With more general-purpose processors such as GPUs being deployed in data center servers, the best practice to build a high-performance NFV service chain should employ available heterogeneous processors. However, current NFV systems fail to utilize these processors for acceleration. This is because, due to separate memory spaces, data synchronization is demanded to guarantee correctness, which can incur non-trivial overhead and result in low performance. This paper proposes AdaptChain, a data management facility that enables adaptive data sharing and synchronization for hybrid NFV systems on heterogeneous architectures. AdaptChain shares the host and device memory among NFs in a service chain. With adaptive synchronization plan generation and NF code adaptation, AdaptChain exploits three classes of opportunities to reduce the amount of synchronized data while guaranteeing correctness. Experimental results show that AdaptChain improves the overall throughput by up to 3.2× and reduces the latency by up to 52%.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.