We propose a runtime-assisted approach to enforce convergence in distributed executions of replicated data types. The key distinguishing aspect of our approach is that it guarantees convergence unconditionally – without requiring data type operations to satisfy algebraic laws such as commutativity and idempotence. Consequently, programmers are no longer obligated to prove convergence on a per-type basis. Moreover, our approach lets sequential data types be reused in a distributed setting by extending their implementations rather than refactoring them. The novel component of our approach is a distributed runtime that orchestrates well-formed executions that are guaranteed to converge. Despite the utilization of a runtime, our approach comes at no additional cost of latency and availability. Instead, we introduce a novel tradeoff against a metric called staleness, which roughly corresponds to the time taken for replicas to converge. We implement our approach in a system called Quark and conduct a thorough evaluation of its tradeoffs.

The sixth generation of DOCSIS standard is currently under development for the provisioning of multi-Gbps services over cable networks. Building upon DOCSIS 3.1 (D3.1), DOCSIS 4.0 (D4) introduces several features including full-duplex transmission and extended-spectrum, which benefit from subcarrier-level OFDM modulation configurations to adapt to varying channel conditions. To exploit the full potential of D4, we propose a softwarized adaptive subcarrier modulation management framework. The optimization system consists of (i) a clustering mechanism that classifies CMs (Cable Modems) with a similar channel condition into the same group using a sparsified K-means algorithm and (ii) an efficient profile generation mechanism to balance achieved channel throughput and packet error rate within the same group. Then, we implement key elements of the softwarized system using a virtualized network function in our DOCSIS experimental testbed that enables the programmatic control of OFDM channels using D4 performance parameters. Our experimental results show that the proposed optimization function offers significant improvements in OFDM channel throughput over current industry management practices. Furthermore, we confirm via simulations that using a novel clustering algorithm for the classification of CM populations and a new bit-loading method measurably enhances channel performance in large-scale distributed deployment scenarios.