Reservoir: Named Data for Pervasive Computation Reuse at the Network Edge
Reservoir: Named Data for Pervasive Computation Reuse at the Network Edge
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DOI：
10.1109/percom53586.2022.9762397
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发表时间：
2021-12
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影响因子：
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通讯作者：
Md Washik Al Azad;Spyridon Mastorakis
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文献类型：
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作者：
Md Washik Al Azad;Spyridon Mastorakis
在边缘计算用例（例如，智能城市）中，多个用户和设备可能彼此非常接近，对于相同服务（例如，图像或视频注释）具有相似输入数据的计算任务可能会卸载到边缘。这些任务的执行通常会产生相同的结果（输出），因此会产生重复（冗余）计算。基于这种观察，先前的工作提倡“计算重用”，这是一种范式，其中先前执行的任务的结果存储在边缘，并被重用以满足具有类似输入数据的传入任务，而不是从头开始执行这些传入任务。然而，在实际的边缘计算部署中实现计算重用，其中服务可能由多个（分布式）边缘节点（服务器）提供，以实现可伸缩性和容错，这在很大程度上仍未得到探索。为了应对这一挑战，在本文中，我们提出了Reservoir，这是一个框架，可以在边缘实现普普性计算重用，同时对用户设备和边缘网络基础设施的操作施加边际开销。Reservoir利用LSH （Locality Sensitive hash）技术，运行在命名数据网络（NDN）之上，对NDN架构进行了扩展，实现了网络中计算重用的语义。我们的评估表明，与没有计算重用的情况相比，Reservoir可以以近乎完美的精度重用计算，实现4.25 - 21.34倍的任务完成时间。
In edge computing use cases (e.g., smart cities), where several users and devices may be in close proximity to each other, computational tasks with similar input data for the same services (e.g., image or video annotation) may be offloaded to the edge. The execution of such tasks often yields the same results (output) and thus duplicate (redundant) computation. Based on this observation, prior work has advocated for “computation reuse”, a paradigm where the results of previously executed tasks are stored at the edge and are reused to satisfy incoming tasks with similar input data, instead of executing these incoming tasks from scratch. However, realizing computation reuse in practical edge computing deployments, where services may be offered by multiple (distributed) edge nodes (servers) for scalability and fault tolerance, is still largely unexplored. To tackle this challenge, in this paper, we present Reservoir, a framework to enable pervasive computation reuse at the edge, while imposing marginal overheads on user devices and the operation of the edge network infrastructure. Reservoir takes advantage of Locality Sensitive Hashing (LSH) and runs on top of Named-Data Networking (NDN), extending the NDN architecture for the realization of the computation reuse semantics in the network. Our evaluation demonstrated that Reservoir can reuse computation with up to an almost perfect accuracy, achieving 4.25–21.34× lower task completion times compared to cases without computation reuse.