Dazhao Cheng;Kai Yan;Xinquan Cai;Yili Gong;Chuang Hu
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引用次数: 0
Abstract
Function-as-a-Service (FaaS) is a promising cloud computing model known for its scalability and elasticity. In various application domains, FaaS workflows have been widely adopted to manage user requests and complete computational tasks efficiently. Motivated by the fact that function containers collaboratively use the image layer's memory, co-placing functions would leverage memory sharing to reduce cluster memory footprint, this article studies layer-wise memory sharing for serverless functions. We find that overwhelming memory sharing by placing containers in the same cluster machine may lead to performance deterioration and Service Level Objective (SLO) violations due to the increased CPU pressure. We investigate how to maximally reduce cluster memory footprint via layer-wise memory sharing for serverless workflows while guaranteeing their SLO. First, we study the container memory sharing problem under serverless workflows with a static Directed Acyclic Graph (DAG) structure. We prove it is NP-Hard and propose a 2-approximation algorithm, namely MDP. Then we consider workflows with dynamic DAG structure scenarios, where the memory sharing problem is also NP-Hard. We design a Greedy-based algorithm called GSP to address this issue. We implement a carefully designed prototype on the OpenWhisk platform, and our evaluation results demonstrate that both MDP and GSP achieve a balanced and satisfying state, effectively reducing up to 63
$\%$
of cache memory usage while guaranteeing serverless workflow SLO.
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.