ComboFunc:联合资源组合与容器放置,实现无服务器功能与异构容器的扩展

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-09-03 DOI:10.1109/TPDS.2024.3454071
Zhaojie Wen;Qiong Chen;Quanfeng Deng;Yipei Niu;Zhen Song;Fangming Liu
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引用次数: 0

摘要

无服务器计算为开发人员提供了一种免维护的资源使用方法,但同时也将资源管理责任转移给了云平台。然而,当创建许多功能容器时,无服务器功能资源的细粒度可能会导致节点上出现性能瓶颈和资源碎片。这给有效扩展功能资源和优化节点资源分配带来了挑战,阻碍了整体敏捷性。为了应对这些挑战,我们为无服务器平台推出了创新的资源扩展系统 ComboFunc。ComboFunc 将函数与不同规格的异构容器关联起来,并优化它们的资源组合和布局。这种方法不仅能为容器创建选择合适的节点,还能利用 Kubernetes 就地 Pod 垂直扩展的新功能来提高资源扩展的灵活性和效率。ComboFunc 允许一个函数对应具有不同资源规格的异构容器,并提供就地修改现有容器资源规格的功能,从而有效利用了节点上的零散资源。这反过来又提高了整个集群的整体资源利用率,提高了扩展灵活性。我们还将异构容器的组合和放置问题建模为一个 NP 难问题,并设计了一个基于贪婪算法的启发式解决方案,该方案可在多项式时间内解决该问题。我们在 Kubernetes 平台上实现了 ComboFunc 的原型,并使用本地集群上的真实痕迹进行了实验。结果表明,与现有策略相比,ComboFunc 的函数资源扩展速度提高了 3.01 倍,资源成本降低了 42.6%。
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ComboFunc: Joint Resource Combination and Container Placement for Serverless Function Scaling With Heterogeneous Container
Serverless computing provides developers with a maintenance-free approach to resource usage, but it also transfers resource management responsibility to the cloud platform. However, the fine granularity of serverless function resources can lead to performance bottlenecks and resource fragmentation on nodes when creating many function containers. This poses challenges in effectively scaling function resources and optimizing node resource allocation, hindering overall agility. To address these challenges, we have introduced ComboFunc, an innovative resource scaling system for serverless platforms. ComboFunc associates function with heterogeneous containers of varying specifications and optimizes their resource combination and placement. This approach not only selects appropriate nodes for container creation, but also leverages the new feature of Kubernetes In-place Pod Vertical Scaling to enhance resource scaling agility and efficiency. By allowing a single function to correspond to heterogeneous containers with varying resource specifications and providing the ability to modify the resource specifications of existing containers in place, ComboFunc effectively utilizes fragmented resources on nodes. This, in turn, enhances the overall resource utilization of the entire cluster and improves scaling agility. We also model the problem of combining and placing heterogeneous containers as an NP-hard problem and design a heuristic solution based on a greedy algorithm that solves it in polynomial time. We implemented a prototype of ComboFunc on the Kubernetes platform and conducted experiments using real traces on a local cluster. The results demonstrate that, compared to existing strategies, ComboFunc achieves up to 3.01 × faster function resource scaling and reduces resource costs by up to 42.6%.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
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.
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