This paper presents a novel hybrid segmented policy that reduces capacity requirements while keeping processing costs low and shows that dynamically adjusting the segment ratio in segmented policies based on historical workload patterns enhances efficiency.
Abstract
While traditional cache policy evaluations fix capacity - often at 0.1% of the dataset - and measure the resulting hit rate, practical edge-cloud deployments require balancing both storage and computational overhead as billed resources. Consequently, system operators frequently focus on a different objective: determining the minimum cache size needed to satisfy a specific Service-Level Objective (SLO) hit-rate. This paper explores this SLO-centric paradigm by analyzing the minimum capacity and execution time each policy requires to hit a defined target. Additionally, we show that dynamically adjusting the segment ratio in segmented policies based on historical workload patterns enhances efficiency. Through evaluations across real-world and synthetic traces, we present a novel hybrid segmented policy that reduces capacity requirements while keeping processing costs low.
The results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees, and resource-aware scheduling decreases node-level congestion and further mitigates SLO violations, demonstrating the effectiveness of incorporating application-level flexibility and hardware-level insights into scheduling and rescheduling decisions.
Oliver Larsson, Thijs Metsch, Cristian Klein et al.· 0 citations
Enterprise applications increasingly depend on distributed caching to sustain sub-millisecond response times at scale. Amazon ElastiCache Redis, operating in cluster mode, provides horizontal partitioning across configurable shard topologies, enabling throughput and memory capacity to grow in proportion to demand. While many organizations have adopted cluster configurations, empirical guidance on topology selection, key distribution optimization, and the measurable performance impact of individual tuning techniques remains sparse. This paper addresses that gap through systematic benchmarking across multiple cluster topologies (3 to 90 shards), three Graviton-based instance families (m6g, r6g, r7g), three workload profiles, and five optimization techniques, augmented by client library analysis, memory optimization guidance, and production cost validation. Production case studies from financial services, e-commerce, and real-time analytics platforms validate laboratory findings. Results offer empirical guidance for cloud architects designing caching architectures that balance latency requirements, horizontal scalability objectives, and infrastructure cost efficiency.
Kandasamy Selvaraj· International journal of com...· 0 citations
This paper conducts a comprehensive evaluation of general-purpose compute instances offered by leading cloud service providers, focusing on the interplay between processor architecture, cost, and performance metrics. Utilizing standardized configurations and benchmarking methodologies across Intel, AMD, and ARM architectures, this research delineates the tradeoffs inherent in cloud infrastructure selection. Results demonstrate ARM-based instances provide superior cost-efficiency for scale-out and cloud-native workloads, while Intel architectures maintain dominance in legacy-sensitive, performance-critical environments. Insights derived aim to empower computing professionals in optimizing cloud resource allocation, maximizing computational throughput per expense, and guiding strategic deployments within evolving distributed systems paradigms.
Rahul Sadhwani· International Conference on...· 0 citations
TADOF is a dynamic optimization framework that jointly performs energy-aware elastic scaling and task migration under drift-aware periodic modeling and burst detection and observed about 18–34% lower energy and 15–28% lower migration cost than threshold and A2C/TD3 baselines while preserving QoS.
Juan Guo, Yanqun Zuo, Zhixian Chang· Cluster Computing· 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