Network Synthesis of a Topology Reconfigurable Disaggregated Rack Scale Datacentre for Multi-Tenancy

A performance analysis of a hybrid reeonfigurable disaggregated dataeentre is presented. It offers substantial benefits in terms of network blocking, power consumption and cost when compared to pure circuit switched and statistical hybrid architectures

A Network Topology for Composable Infrastructures

This paper proposes a passive optical backplane as a new network topology for composable computing infrastructures. The topology provides a high capacity, low-latency and flexible fabric that interconnects disaggregated resource components. The network topology is dedicated to inter-resource communication between composed logical hosts to ensure effective performance. We formulated a mixed integer linear programming (MILP) model that dynamically creates logical networks to support intra logical host communication over the physical network topology. The MILP performs energy efficient logical network instantiation given each application's resource demand. The topology can achieve 1 Tbps capacity per resource node given appropriate wavelength transmission data rate and the right number of wavelengths per node

Optically Disaggregated Data Centers With Minimal Remote Memory Latency: Technologies, Architectures, and Resource Allocation

Disaggregated rack-scale data centers have been proposed as the only promising avenue to break the barrier of the fixed CPU-to-memory proportionality caused by main-tray direct-attached conventional/traditional server-centric systems. However, memory disaggregation has stringent network requirements in terms of latency, energy efficiency, bandwidth, and bandwidth density. This paper identifies all the requirements and key performance indicators of a network to disaggregate IT resources while summarizing the progress and importance of optical interconnects. Crucially, it proposes a rack-and-cluster scale architecture, which supports the disaggregation of CPU, memory, storage, and/or accelerator blocks. Optical circuit switching forms the core of this architecture, whereas the end-points (IT resources) are equipped with on-chip programmable hybrid electrical packet/circuit switches. This architecture offers dynamically reconfigurable physical topology to form virtual ones, each embedded with a set of functions. It analyzes the latency overhead of disaggregated DDR4 (parallel) and the proposed hybrid memory cube (serial) memory elements on the conventional and the proposed architecture. A set of resource allocation algorithms are introduced to (1) optimally select disaggregated IT resources with the lowest possible latency, (2) pool them together by means of a virtual network interconnect, and (3) compose virtual disaggregated servers. Simulation findings show up to a 34% resource utilization increase over traditional data centers while highlighting the importance of the placement and locality among compute, memory, and storage resources. In particular, the network-aware locality-based resource allocation algorithm achieves as low as 15 ns, 95 ns, and 315 ns memory transaction round-trip latency on 63%, 22%, and 15% of the allocated virtual machines (VMs) accordingly while utilizing 100% of the CPU resources. Furthermore, a formulation to parameterize and evaluate the additional financial costs endured by disaggregation is reported. It is shown that the more diverse the VM requests are, the higher the net financial gain is. Finally, an experiment was carried out using silicon photonic midboard optics and an optical circuit switch, which demonstrates forward error correction free 10−1210−12 bit error rate performance on up to five-tier scale-out networks

In compute/memory dynamic packet/circuit switch placement for optically disaggregated data centers

Network function services on conventional hybrid data center (DC) architectures such as HELIOS are hard-wired and dedicated to specific network resources. This limits flexibility and performance to handle diverse traffic patterns. Furthermore, disaggregation of server resources has shown promising potential to improve resource utilization, which has been a limitation of conventional server-centric DCs. This paper presents a reconfigurable hybrid disaggregated DC (dRedBox) architecture that combines the concept of server resource disaggregation with cutting-edge software and electronic and optical technologies. The dRedBox architecture provides a remarkable amount of flexibility and connectivity through hardware-based multilayer network function service programmability. This allows for multilayer network services to be dynamically deployed at runtime to network resources and, in turn, handle diverse traffic patterns. Furthermore, this study proposes algorithms and strategies for selecting and deploying electronic packet switching and optical circuit switching function services to implement virtual machine network requests across dRedBox and conventional hybrid disaggregated architectures under different traffic patterns. Finally, the performance of the various strategies on the dRedBox and conventional hybrid disaggregated DC architectures is evaluated in terms of blocking probability, energy efficiency, network utilization, and cost. Extensive results show that, at 10% blocking probability, dRedBox architecture achieves 100% gain on VM placement and 92% energy savings compared with conventional hybrid disaggregated architectures

Energy Efficient Placement of Workloads in Composable Data Center Networks

This paper studies the energy efficiency of composable datacentre (DC) infrastructures over network topologies. Using a mixed integer linear programming (MILP) model, we compare the performance of disaggregation at rack-scale and pod-scale over selected electrical, optical and hybrid network topologies relative to a traditional DC. Relative to a pod-scale DC, the results show that physical disaggregation at rack-scale is sufficient for optimal efficiency when the optical network topology is adopted, and resource components are allocated in a suitable manner. The optical network topology also enables optimal energy efficiency in composable DCs. The paper also studies logical disaggregation of traditional DC servers over an optical network topology. Relative to physical disaggregation at rack-scale, logical disaggregation of server resources within each rack enables marginal fall in the total DC power consumption (TDPC) due to improved resource demands placement. Hence, an adaptable composable infrastructure that can support both in memory (access) latency sensitive and insensitive workloads is enabled. We also conduct a study of the adoption of micro-service architecture in both traditional and composable DCs. Our results show that increasing the modularity of workloads improves the energy efficiency in traditional DCs, but disproportionate utilization of DC resources persists. A combination of disaggregation and micro-services achieved up to 23% reduction in the TDPC of the traditional DC by enabling optimal resources utilization and energy efficiencies. Finally, we propose a heuristic for energy efficient placement of workloads in composable DCs which replicates the trends produced by the MILP model formulated in this paper

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms