Optimal VDC service provisioning in optically interconnected disaggregated data centers

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Virtual data center (VDC) is a key service in modern data center (DC) infrastructures. However, the rigid architecture of traditional servers inside DCs may lead to blocking situations when deploying VDC instances. To overcome this problem, the disaggregated DC paradigm is introduced. In this letter, we present an integer linear programming (ILP) formulation to optimally allocate VDC requests on top of an optically interconnected disaggregated DC infrastructure, aiming to quantify the benefits that such an architecture can bring when compared with traditional server-centric DCs. Moreover, a lightweight simulated annealing-based heuristic is provided for the scenarios where the ILP scalability is challenged. The obtained numerical results reveal the substantial benefits yielded by the resource disaggregation paradigm.Peer ReviewedPostprint (author's final draft

On the benefits of resource disaggregation for virtual data centre provisioning in optical data centres

Virtual Data Centre (VDC) allocation requires the provisioning of both computing and network resources. Their joint provisioning allows for an optimal utilization of the physical Data Centre (DC) infrastructure resources. However, traditional DCs can suffer from computing resource underutilization due to the rigid capacity configurations of the server units, resulting in high computing resource fragmentation across the DC servers. To overcome these limitations, the disaggregated DC paradigm has been recently introduced. Thanks to resource disaggregation, it is possible to allocate the exact amount of resources needed to provision a VDC instance. In this paper, we focus on the static planning of a shared optically interconnected disaggregated DC infrastructure to support a known set of VDC instances to be deployed on top. To this end, we provide optimal and sub-optimal techniques to determine the necessary capacity (both in terms of computing and network resources) required to support the expected set of VDC demands. Next, we quantitatively evaluate the benefits yielded by the disaggregated DC paradigm in front of traditional DC architectures, considering various VDC profiles and Data Centre Network (DCN) topologies.Peer ReviewedPostprint (author's final draft

An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks

In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak efficiency because of the optical network topology. Logical separation of conventional DC servers across an optical network architecture is also investigated in this article. When compared to physical decentralisation at the rack size, logical decomposition of data centers inside each rack offers a small decrease in the overall DC energy usage thanks to better resource needs allocation. This allows for a flexible, composable architecture that can accommodate performance based in-memory applications. Moreover, we look at the state of fundamentalmodel and its use in both static and dynamic data centres. According to our findings, typical DCs become more energy efficient when workload modularity increases, although excessive resource use still exists. By enabling optimal resource use and energy savings, disaggregation and micro-services were able to reduce the typical DC's up to 30%. Furthermore, we offer a heuristic to duplicate the Mixed integer model's output trends for energy-efficient allocation of caseloads in modularized DCs

Future Energy Efficient Data Centers With Disaggregated Servers

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This paper examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this paper, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers. First, we present our new design for the photonic DS-based data center architecture, supplemented with a complete description of the architectural components. Second, we develop a mixed integer linear programming (MILP) model to optimize VM allocation for the DS-based data center, including the data center communication fabric power consumption. Our results indicate that, in DS data centers, the optimum allocation of pooled resources and their communication power yields up to 42% average savings in total power consumption when compared with the CS approach. Due to the MILP high computational complexity, we developed an energy efficient resource provisioning heuristic for DS with communication fabric (EERP-DSCF), based on the MILP model insights, with comparable power efficiency to the MILP model. With EERP-DSCF, we can extend the number of served VMs, where the MILP model scalability for a large number of VMs is challenging. Furthermore, we assess the energy efficiency of the DS design under stringent conditions by increasing the CPU to memory traffic and by including high noncommunication power consumption to determine the conditions at which the DS and CS designs become comparable in power consumption. Finally, we present a complete analysis of the communication patterns in our new DS design and some recommendations for design and implementation challenges

Design and implementation of a graphical interface for the Orchestrator in SDN-enabled Data centres

The increased use of Internet in recent years due to cloud services, such as social networks, applications, web pages... has forced the necessity of data centres be able to support this large amount of daily traffic. A current data centre, in order to store this amount of information, can be made up of hundreds of thousands of servers and links that allow users to enjoy their content anytime, almost anywhere and with a good quality of service. Due to this mass of data centres, the scientific community has to face hard challenges of how to manage and control the large number of elements that form them. Therefore, new techniques are being developed both to control and manage with the purpose of reducing maintenance costs, facilitating the deployment of new applications, and definitively, making the most of the data centres. For this reason, it has been developed a useful graphical interface which is able to facilitate the use of management and control layers of a data centre. To sum up, this project designed and developed in Java, allows the user to observe how the data centre's resources are being used in a visually and friendly way

On the complexity of configuration and orchestration for enabling disaggregated server provisioning in optical composable data centers

Due to the limitations of traditional data center (DC) architectures, the concept of infrastructure disaggregation has been proposed. DC resources are separated into multiple blades to be exploited independently. As a result, composable DC (CDC) infrastructures are achieved, enhancing the modularity of resource provisioning. However, disaggregation introduces additional challenges that need to be carefully analyzed. One relates to the potential complexity increase on the orchestration and infrastructure configuration that need to be performed when provisioning resources to support services. This aspect is highly influenced by the distribution of resources at the physical infrastructure. As such, when analyzing the performance of a CDC, it becomes essential to also study the related operational complexity of the resource orchestration and configuration phases. Furthermore, the requirements of several tenant services may impose heterogeneous deployments over the shared physical infrastructure in the form of either disaggregated single-server or multi-server distributions. The associated orchestration/configuration cost is again highly influenced by the data plane architecture of the CDC. With these aspects in mind, in this paper, we provide a methodology for analysis of the complexity of resource orchestration for a service deployment and the associated configuration cost in optical CDCs, considering various service deployment setups. A selected set of CDC architectures found in the literature is employed to quantitatively illustrate how the data plane design and service deployment strategies affect the complexity of infrastructure configuration and resource orchestration.This work has been supported by the Spanish Government through project TRAINER-B (PID2020-118011GB-C22) with FEDER contribution.Peer ReviewedPostprint (author's final draft

Disaggregated Servers for Future Energy Efficient Data Centres

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today’s data centres can no longer support. This thesis examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm known as disaggregated server (DS). The DS design arranges data centres resources in physical pools such as processing, memory and IO module pools; rather than packing each subset in a single server. In this work, we study energy efficient resource provisioning and virtual machine (VM) allocation in the DS based data centres compared to CS based data centres. First, we developed a mixed integer linear programming (MILP) model to optimise VM allocation for DS based data centre. Our results indicate that considering pooled resources yields up to 62% total saving in power consumption compared to the CS approach. Due to the MILP high computational complexity, we developed an energy efficient, fast and scalable resource provisioning heuristic (EERP-DS), based on the MILP insights, with comparable power efficiency to the MILP. Second, we extended the resources provisioning and VM allocation MILP to include the data centre communication fabric power consumption. The results show that the inclusion of the communication fabric still yields considerable power savings compared to the CS approach, up to 48% power saving. Third, we developed an energy efficient resource provisioning for DS with communication fabric heuristic (EERP-DSCF). EERP-DSCF achieved comparable results to the second MILP and with it we can extend the number of served VMs where the MILP scalability for big number of VMs is challenging. Finally, we present our new design for the photonic DS based data centre architecture supplemented with a complete description of the architecture components, communication patterns and some recommendations for the design implementation challenges

Resource allocation in disaggregated optical networks

The recently introduced disaggregation model is gaining interest due to its benefits when compared with traditional models.In essence, it consists on the separation of traditional hardware appliances (e.g. servers, network nodes) into commodity components, which then are mounted independently for their exploitation into customized physical infrastructures. Such an approach allows telecommunication operators and service providers to appropriately size their infrastructure and grow as needed. One of the main key benefits of the disaggregation model is the break of the vendor lock-in, pushing towards interoperability between equipment from different vendor with minimum standardization of software and hardware specifications, allowing operators to build the best solutions for their needs. Moreover, efficient scaling is also an important benefit introduced by the disaggregation approach. Due to these benefits, among others, the disaggregation model is gaining momentum and is being adopted into multiple fields and domains of nowadays telecom infrastructures. In this regard, the scenario under study of this master thesis focuses on disaggregated optical transport networks. Disaggregation allows for more open and customized optical networks, reducing both capital and operational expenditures for infrastructure owners.However, despite of these positive aspects, disaggregated optical networks face several challenges, beingthe degradation of the network performance when compared to traditional integrated solutions the most important one. In this regard, this thesis investigates the impact of disaggregation in optical networks and investigates regeneration as a potential solution to compensate the performances’ degradation. Under this premise, optimal solutions for regenerator placement, exploiting the inherent grooming capabilities of regenerators, are proposed and evaluatedIncomin

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

Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches $\mathbf{O}(10^6)$ servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in $\approx 500 ps$ and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where $3\times$ less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in $\mathbf{O}(10^{-3}) s$. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved $>20\%$ with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work