Consensus, widely regarded as the most fundamental primitive in distributed systems, lies at the core of countless services that require coordination among remote processes. Datacenter services typically achieve consensus through long-established, quorum-based algorithms such as Paxos and Raft, including recent re-adaptations for kernel bypass datapaths (e.g. smartNIC/RDMA-based consensus). While these optimizations can reduce latency to the μs-scale, they remain constrained by inherent message complexity, namely the need for acknowledgments from majority quorums to tolerate faults and arbitrary message delays. Our approach takes a step further from bare acceleration of classical primitives, focusing instead on leveraging FPGA-smartNIC and priority-queue reservation to achieve synchronous remote interactions in practice. We use synchrony to devise a novel, efficient quorum-less consensus protocol which we use to build Nano-consensus: a novel hardware consensus engine. Nano-consensus operates at network line rate and can reach consensus in 1.03μs for single-packet instances, delivering 3.82× latency and 4.8× improvements over the state of the art. We demonstrate how Nano-consensus can be integrated into distributed applications to boost both performance and consistency.

Modern data centers face growing workloads, putting accrued pressure on network monitoring solutions necessary for ensuring correct and efficient operation. Advances in network programmability have meanwhile led to yet more monitoring data being straightforwardly collected from switches, exacerbating bottlenecks in corresponding collection-centric approaches. This limits scalability and responsiveness, especially when several monitoring tasks are deployed side-by-side, as is common for network management. We present a novel and comprehensive selection-centric solution for network monitoring and management (M&M) called FARM that significantly simplifies the development and deployment of network M&M tasks while being effective and scalable. FARM's main novelty lies in its comprehensive design. Instead of focusing solely on individual parts of network monitoring, FARM takes a global perspective on the problem and aligns all of its components correspondingly: a strongly decentralized software architecture, a specifically designed programming model, and an integrated performance optimization framework. In short, FARM performs monitoring (re)actions locally on switches to the extent possible, using centralized components only if and when needed, and globally optimizes placement, considering placement constraints intrinsically expressed through its programming model as well as commonalities among tasks. Deployed in a production data center, FARM shows significant gains in responsiveness (up to 3427× faster over recent generic approaches and 4 × faster over highly specialized solutions), and savings in network band-width (10000 ×) and computational effort. Placement optimization shows excellent scalability up to 10200 seeds across 1040 switches.

Abstract This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

Abstract This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

Detecting recurrent patterns in time series data is an important capability. The reason is that repeating patterns on the one hand indicate well defined processes that can be further analyzed once detected and on the other hand are a reliable feature to predict future occurrences and adapt accordingly. The challenge in real data to define a period is that a time series is usually also influenced by non-periodic dynamics and noise. In this work, a mathematical framework is proved to define regular patterns. Their properties are used within a suggested algorithm based on the concept of autocorrelation and function approximation to fit a model capturing the periodic part of the time series. Based on that model and a corresponding autocorrelation, a new score is defined to evaluate how well a hypothesized period fits to the time series. This score is particularly useful in a big data scenario where decisions for periodicity are needed to be taken automatically, which is one of the main achievement of the presented work. The period analysis algorithm is applied to data from two different use cases. The first one is a data center scenario where the information of the periodic pattern is used to create a feature that improves a machine learning framework predicting future resource demands. The feature represents the phase of the repeating pattern. In a second scenario, expression data from mice liver cells are investigated concerning periodic rhythms. A Python implementation of the presented algorithm is provided via a github repository under https://github.com/LauritzR/period-detection .