Composable architecture for rack scale big data computing

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture

Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics

The diversity of workload requirements and increasing hardware heterogeneity in emerging high performance computing (HPC) systems motivate resource disaggregation. Resource disaggregation allows compute and memory resources to be allocated individually as required to each workload. However, it is unclear how to efficiently realize this capability and cost-effectively meet the stringent bandwidth and latency requirements of HPC applications. To that end, we describe how modern photonics can be co-designed with modern HPC racks to implement flexible intra-rack resource disaggregation and fully meet the bit error rate (BER) and high escape bandwidth of all chip types in modern HPC racks. Our photonic-based disaggregated rack provides an average application speedup of 11% (46% maximum) for 25 CPU and 61% for 24 GPU benchmarks compared to a similar system that instead uses modern electronic switches for disaggregation. Using observed resource usage from a production system, we estimate that an iso-performance intra-rack disaggregated HPC system using photonics would require 4x fewer memory modules and 2x fewer NICs than a non-disaggregated baseline.Comment: 15 pages, 12 figures, 4 tables. Published in IEEE Cluster 202

Development of a secure monitoring framework for optical disaggregated data centres

Data center (DC) infrastructures are a key piece of nowadays telecom and cloud services delivery, enabling the access and storage of enormous quantities of information as well as the execution of complex applications and services. Such aspect is being accentuated with the advent of 5G and beyond architectures, since a significant portion of the network and service functions are being deployed as specialized virtual elements inside dedicated DC infrastructures. As such, the development of new architectures to better exploit the resources of DC becomes of paramount importanceThe mismatch between the variability of resources required by running applications and the fixed amount of resources in server units severely limits resource utilization in today's Data Centers (DCs). The Disaggregated DC (DDC) paradigm was recently introduced to address these limitations. The main idea behind DDCs is to divide the various computational resources into independent hardware modules/blades, which are mounted in racks, bringing greater modularity and allowing operators to optimize their deployments for improved efficiency and performance, thus, offering high resource allocation flexibility. Moreover, to efficiently exploit the hardware blades and establish the connections across them according to upper layer requirements, a flexible control and management framework is required. In this regard, following current industrial trends, the Software Defined Networking (SDN) paradigm is one of the leading technologies for the control of DC infrastructures, allowing for the establishment of high-speed, low-latency optical connections between hardware components in DDCs in response to the demands of higher-level services and applications. With these concepts in mind, the primary objective of this thesis is to design and carry out the implementation of the control of a DDC infrastructure layer that is founded on the SDN principles and makes use of optical technologies for the intra-DC network fabric, highlighting the importance of quality control and monitoring. Thanks to several SDN agents, it becomes possible to gather statistics and metrics from the multiple infrastructure elements (computational blades and network equipment), allowing DC operators to monitor and make informed decisions on how to utilize the infrastructure resources to the greatest extent feasible. Indeed, quality assurance operations are of capital importance in modern DC infrastructures, thus, it becomes essential to guarantee a secure communication channel for gathering infrastructure metrics/statistics and enforcing (re-)configurations, closing the full loop, then addressing the security layer to secure the communication channel by encryption and providing authentication for the server and the client

Free space intra-datacenter interconnects based on 2D optical beam steering enabled by photonic integrated circuits

Data centers are continuously growing in scale and can contain more than one million servers spreading across thousands of racks; requiring a large-scale switching network to provide broadband and reconfigurable interconnections of low latency. Traditional data center network architectures, through the use of electrical packet switches in a multi-tier topology, has fundamental weaknesses such as oversubscription and cabling complexity. Wireless intra-data center interconnection solutions have been proposed to deal with the cabling problem and can simultaneously address the over-provisioning problem by offering efficient topology re-configurability. In this work we introduce a novel free space optical interconnect solution for intra-data center networks that utilizes 2D optical beam steering for the transmitter, and high bandwidth wide-area photodiode arrays for the receiver. This new breed of free space optical interconnects can be developed on a photonic integrated circuit; offering ns switching at sub-µW consumption. The proposed interconnects together with a networking architecture that is suitable for utilizing those devices could support next generation intra-data center networks, fulfilling the requirements of seamless operation, high connectivity, and agility in terms of the reconfiguration time.Peer ReviewedPostprint (published version

Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity

Energy-Efficient Workload Placement with Bounded Slowdown in Disaggregated Datacenters

Disaggregated Data Center (DDC) is a modern datacenter architecture that decouples hardware resources from monolithic servers into pools of resources that can be dynamically composed to match diverse workload requirements. While disaggregation improves resource utilization, it could negatively impact workload slowdown due to the latency of accessing disaggregated resources over the datacenter network. To this end, we consider CPU and memory disaggregation and conduct measurements to experimentally profile several popular datacenter workloads in order to characterize the impact of disaggregation on workload execution slowdown. We then develop a workload placement algorithm, called Iterative Rounding-based Placement ( IRoP), that given a set of workloads, determines where to place each workload (i.e., on which CPU) and how much local and remote memory is allocated to it. The key insight in designing IRoP is that the impact of remote memory latency on slowdown can be substantially masked by assigning workloads to higher-performing CPUs, albeit at the cost of higher power consumption. As such, IRoP aims to find a workload placement that minimizes the DDC power consumption while respecting a bounded slowdown for each workload. We provide extensive simulation results to demonstrate the flexibility of IRoP in providing a wide range of trade-offs between power consumption and workload slowdown. We also compare IRoP with several existing baselines. Our results indicate that IRoP can reduce power consumption and slowdown in the considered scenarios by up to 8% and 12%, respectively

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen