Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 762-764· 0 citations· 10 references
Abstract
Serverless computing, particularly Function-as-aService (FaaS), has emerged as a dominant cloud computing paradigm offering automatic scaling, pay-per-use pricing, and simplified deployment. However, the stateless and ephemeral nature of serverless functions introduces significant performance challenges, most notably cold-start latency and costly remote data access. Caching has been widely recognized as a key technique to mitigate these issues, yet the unique constraints of serverless environments-such as ephemeral containers, limited local state, and multi-tenant resource sharing-demand fundamentally new caching designs. In this paper, we present a concise survey of five representative caching systems for serverless platforms published between 2021 and 2025. We categorize them into three classes: container-level caching for cold-start mitigation, datalevel caching for reducing storage access latency, and cache coherence mechanisms for distributed consistency. We compare these systems along key dimensions including caching granularity, eviction strategy, transparency, and performance gains. Finally, we identify open challenges and promising directions for future research.
To mitigate the cold-start overhead of serverless functions, two orthogonal approaches have been studied: in-memory caching and snapshot-based in-storage caching. In this work, we propose FaaSlim, a snapshot-based serverless computing system that integrates both approaches to reduce cold start latency. FaaSlim classifies VM pages into three categories, read, write, and permission update, based on page fault types, enabling partial caching of only a selected subset of pages rather than the entire VM while reclaiming the rest. When the VM is reused, only reclaimed pages are fetched from disk, reducing page fault overhead and function latency, while requiring less memory during idle periods. We also devise a partial caching-aware eviction policy, GDSF-CE, which selects VMs and page subsets to evict based on cache efficiency, a metric that quantifies the benefit of partial caching by relating latency reduction to memory consumption. Our evaluation using real-world traces shows that FaaSlim reduces the total overhead of snapshot-based cold starts by 23.0–27.8% compared to the state-of-the-art combination of in-storage and in-memory caching, FaaSnap with CIDRE’s GDSF-C, across diverse serverless workloads.
Sanghyeon Eom, Chanyoung Park, Gihong Lee et al.· International Conference on...· 0 citations
CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.
Zejian Wang, Nan Lin, Zinuo Cai et al.· ACM Transactions on Architec...· 0 citations
When affinity recovers too little KV work, its residual load skew reduces or erases the improvement, so gating any deployment with a shadow replay rather than enabling affinity from workload statistics alone is recommended.
Serverless computing provides on-demand elasticity, pay-per-use, and simplified deployment. However, serverless functions are typically stateless and depend on external storage services such as object stores or databases to exchange data or support stateful functions. Provisioning and configuring these storage systems still requires manual setup or declarative scripts, introducing complexity, slowing development, and increasing the risk of configuration errors. To address these challenges, in this paper, we introduce STORE, a self-provisioning storage architecture for serverless functions. STORE automatically selects the optimal storage backend and eliminates developer effort through zero-touch and zero-configuration provisioning, achieved by moving the self-provisioning logic to the platform level. Our evaluation results show that STORE reduces implementation effort by up to 84% compared to well-established Infrastructure-as-Code frameworks such as Terraform and Pulumi while maintaining low latency and linear scalability under realistic workloads, without introducing performance overhead.
Florian Trimmel, Cynthia Marcelino, T. Pusztai et al.· International Conference on...· 0 citations
Cloud computing has become the new foundation of the current digital network, which provides the possibility to store data with scaling and on-demand access to huge amounts of data. Nevertheless, the fast development of the cloud-based applications has posed the significant threats in terms of latency, bandwidth usage as well as storage efficiency. Conventional cloud storage platforms are very much dependent on the concept of traditional or heuristic based caching whereby caching is not always adequate to adjust to the dynamism and heterogeneity of workloads. The given paper is a complete research on the optimization of cloud storage systems by using intelligent caching algorithms. Through machine learning, predictive analytics, and adaptive replacement technologies, intelligent caching will be used to enhance the speed and efficiency of data access, minimizing network congestions, and increasing the efficiency of a system as a whole. The suggested algorithm is a combination of workload-sensitive placement of caches, predicting access pattern, and managing the cache in real time to dynamically optimize storage throughput. The high performance of the cloud of isolated simulated cloud workloads has been extensively tested and results in high improvements in cache hit ratio, response time and bandwidth use as compared to traditional caching methods. The findings support the fact that smart caching schemes offer a solid and scalable remedy to optimization of next-generation cloud storage.
Carlos Mendes· International Journal of App...· 0 citations