As applications demand increasing memory capacity in clouds, memory pooling provides a cost-effective way to improve utilization and expand capacity. Compute Express Link (CXL), which enables high-performance direct access to remote memory, makes this approach increasingly practical. However, existing studies of memory offloading fall short when multiple applications share a memory pool, as they overlook application heterogeneity and cross-application interference. We present Aethon, a memory offloading system for public clouds that maximizes offloaded data while ensuring each application satisfies the service-level agreement (SLA). Aethon integrates an application-transparent predictor to estimate offloading-induced performance degradation, and adaptively determines cold- and hot-data offloading volumes for each application. Compared with representative work, Aethon offloads 14.2% more data on average (up to 27.4%), while satisfying SLA requirements.
Guangqiang Luan, Pu Pang, Quan Chen et al.· Proceedings of the Internati...· 0 citations
Serverless edge computing, despite its flexibility and efficiency, is hindered by high startup latency during peak load. Remote fork, employing either Checkpoint/Restore (C/R) or Remote Direct Memory Access (RDMA), offers a potential solution for function scaling acceleration. Although RDMA fork is faster, the opportunities are limited, whereas C/R fork is more common but slower. Moreover, the regeneration capability that a forked function can further fork new instances complicates the remote fork decisions for fast scaling. Therefore, in this paper, we are motivated to address the problem on how to holistically exploit C/R fork and RDMA fork with the consideration of the underlying infrastructure features (e.g., topology, resources, etc.) to realize fast function scaling. We first formulate it to an Integer Linear Programming (ILP) problem. We further introduce a Heat metric to assess the potential of an edge server as a fork destination according to the topology and resource availability, and propose a Heat-based fork strategy (HEAT) for both the fork destination site and the corresponding fork mode decisions. Experiment results demonstrate that HEAT improves the function scaling speed by 46% and RDMA resource utilization by 51%, compared to state-of-the-art scaling solutions.
Zhe-Xiong Li, Deze Zeng, Lin Gu et al.· IEEE Transactions on Mobile...· 0 citations
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