Supercomputing futures : the next sharing paradigm for HPC resources : economic model, market analysis and consequences for the Grid

À la croisée des chemins du génie informatique, de la finance et de l'économétrie, cette thèse se veut fondamentalement un exercice en ingénierie économique dont l' objectif est de contribuer un système novateur, durable et adaptatif pour le partage de resources de calcul haute-performance. Empruntant à la finance fondamentale et à l'analyse technique, le modèle proposé construit des ratios et des indices de marché à partir de statistiques transactionnelles. Cette approche, encourageant les comportements stratégiques, pave la voie à une métaphore de partage plus efficace pour la Grid, où l'échange de ressources se voit maintenant pondéré. Le concept de monnaie de Grid, un instrument beaucoup plus liquide et utilisable que le troc de resources comme telles est proposé: les Grid Credits. Bien que les indices proposés ne doivent pas être considérés comme des indicateurs absolus et contraignants, ils permettent néanmoins aux négociants de se faire une idée de la valeur au marché des différentes resources avant de se positionner. Semblable sur de multiples facettes aux bourses de commodités, le Grid Exchange, tel que présenté, permet l'échange de resources via un mécanisme de double-encan. Néanmoins, comme les resources de super-calculateurs n'ont rien de standardisé, la plate-forme permet l'échange d'ensemble de commodités, appelés requirement sets, pour les clients, et component sets, pour les fournisseurs. Formellement, ce modèle économique n'est qu'une autre instance de la théorie des jeux non-coopératifs, qui atteint éventuellement ses points d'équilibre. Suivant les règles du "libre-marché", les utilisateurs sont encouragés à spéculer, achetant, ou vendant, à leur bon vouloir, l'utilisation des différentes composantes de superordinateurs. En fin de compte, ce nouveau paradigme de partage de resources pour la Grid dresse la table à une nouvelle économie et une foule de possibilités. Investissement et positionnement stratégique, courtiers, spéculateurs et même la couverture de risque technologique sont autant d'avenues qui s'ouvrent à l'horizon de la recherche dans le domaine

Market driven elastic secure infrastructure

In today’s Data Centers, a combination of factors leads to the static allocation of physical servers and switches into dedicated clusters such that it is difficult to add or remove hardware from these clusters for short periods of time. This silofication of the hardware leads to inefficient use of clusters. This dissertation proposes a novel architecture for improving the efficiency of clusters by enabling them to add or remove bare-metal servers for short periods of time. We demonstrate by implementing a working prototype of the architecture that such silos can be broken and it is possible to share servers between clusters that are managed by different tools, have different security requirements, and are operated by tenants of the Data Center, which may not trust each other. Physical servers and switches in a Data Center are grouped for a combination of reasons. They are used for different purposes (staging, production, research, etc); host applications required for servicing specific workloads (HPC, Cloud, Big Data, etc); and/or configured to meet stringent security and compliance requirements. Additionally, different provisioning systems and tools such as Openstack-Ironic, MaaS, Foreman, etc that are used to manage these clusters take control of the servers making it difficult to add or remove the hardware from their control. Moreover, these clusters are typically stood up with sufficient capacity to meet anticipated peak workload. This leads to inefficient usage of the clusters. They are under-utilized during off-peak hours and in the cases where the demand exceeds capacity the clusters suffer from degraded quality of service (QoS) or may violate service level objectives (SLOs). Although today’s clouds offer huge benefits in terms of on-demand elasticity, economies of scale, and a pay-as-you-go model yet many organizations are reluctant to move their workloads to the cloud. Organizations that (i) needs total control of their hardware (ii) has custom deployment practices (iii) needs to match stringent security and compliance requirements or (iv) do not want to pay high costs incurred from running workloads in the cloud prefers to own its hardware and host it in a data center. This includes a large section of the economy including financial companies, medical institutions, and government agencies that continue to host their own clusters outside of the public cloud. Considering that all the clusters may not undergo peak demand at the same time provides an opportunity to improve the efficiency of clusters by sharing resources between them. The dissertation describes the design and implementation of the Market Driven Elastic Secure Infrastructure (MESI) as an alternative to the public cloud and as an architecture for the lowest layer of the public cloud to improve its efficiency. It allows mutually non-trusting physically deployed services to share the physical servers of a data center efficiently. The approach proposed here is to build a system composed of a set of services each fulfilling a specific functionality. A tenant of the MESI has to trust only a minimal functionality of the tenant that offers the hardware resources. The rest of the services can be deployed by each tenant themselves MESI is based on the idea of enabling tenants to share hardware they own with tenants they may not trust and between clusters with different security requirements. The architecture provides control and freedom of choice to the tenants whether they wish to deploy and manage these services themselves or use them from a trusted third party. MESI services fit into three layers that build on each other to provide: 1) Elastic Infrastructure, 2) Elastic Secure Infrastructure, and 3) Market-driven Elastic Secure Infrastructure. 1) Hardware Isolation Layer (HIL) – the bottommost layer of MESI is designed for moving nodes between multiple tools and schedulers used for managing the clusters. It defines HIL to control the layer 2 switches and bare-metal servers such that tenants can elastically adjust the size of the clusters in response to the changing demand of the workload. It enables the movement of nodes between clusters with minimal to no modifications required to the tools and workflow used for managing these clusters. (2) Elastic Secure Infrastructure (ESI) builds on HIL to enable sharing of servers between clusters with different security requirements and mutually non-trusting tenants of the Data Center. ESI enables the borrowing tenant to minimize its trust in the node provider and take control of trade-offs between cost, performance, and security. This enables sharing of nodes between tenants that are not only part of the same organization by can be organization tenants in a co-located Data Center. (3) The Bare-metal Marketplace is an incentive-based system that uses economic principles of the marketplace to encourage the tenants to share their servers with others not just when they do not need them but also when others need them more. It provides tenants the ability to define their own cluster objectives and sharing constraints and the freedom to decide the number of nodes they wish to share with others. MESI is evaluated using prototype implementations at each layer of the architecture. (i) The HIL prototype implemented with only 3000 Lines of Code (LOC) is able to support many provisioning tools and schedulers with little to no modification; adds no overhead to the performance of the clusters and is in active production use at MOC managing over 150 servers and 11 switches. (ii) The ESI prototype builds on the HIL prototype and adds to it an attestation service, a provisioning service, and a deterministically built open-source firmware. Results demonstrate that it is possible to build a cluster that is secure, elastic, and fairly quick to set up. The tenant requires only minimum trust in the provider for the availability of the node. (iii) The MESI prototype demonstrates the feasibility of having a one-of-kind multi-provider marketplace for trading bare-metal servers where providers also use the nodes. The evaluation of the MESI prototype shows that all the clusters benefit from participating in the marketplace. It uses agents to trade bare-metal servers in a marketplace to meet the requirements of their clusters. Results show that compared to operating as silos individual clusters see a 50% improvement in the total work done; up to 75% improvement (reduction) in waiting for queues and up to 60% improvement in the aggregate utilization of the test bed. This dissertation makes the following contributions: (i) It defines the architecture of MESI allows mutually non-trusting tenants of the data center to share resources between clusters with different security requirements. (ii) Demonstrates that it is possible to design a service that breaks the silos of static allocation of clusters yet has a small Trusted Computing Base (TCB) and no overhead to the performance of the clusters. (iii) Provides a unique architecture that puts the tenant in control of its own security and minimizes the trust needed in the provider for sharing nodes. (iv) A working prototype of a multi-provider marketplace for bare-metal servers which is a first proof-of-concept that demonstrates that it is possible to trade real bare-metal nodes at practical time scales such that moving nodes between clusters is sufficiently fast to be able to get some useful work done. (v) Finally results show that it is possible to encourage even mutually non-trusting tenants to share their nodes with each other without any central authority making allocation decisions. Many smart, dedicated engineers and researchers have contributed to this work over the years. I have jointly led the efforts to design the HIL and the ESI layer; led the design and implementation of the bare-metal marketplace and the overall MESI architecture

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

Energy Demand Response for High-Performance Computing Systems

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system\u27s demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems\u27 energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system\u27s emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system\u27s energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response

Putting the User at the Centre of the Grid: Simplifying Usability and Resource Selection for High Performance Computing

Computer simulation is finding a role in an increasing number of scientific disciplines, concomitant with the rise in available computing power. Realizing this inevitably re- quires access to computational power beyond the desktop, making use of clusters, supercomputers, data repositories, networks and distributed aggregations of these re- sources. Accessing one such resource entails a number of usability and security prob- lems; when multiple geographically distributed resources are involved, the difficulty is compounded. However, usability is an all too often neglected aspect of computing on e-infrastructures, although it is one of the principal factors militating against the widespread uptake of distributed computing. The usability problems are twofold: the user needs to know how to execute the applications they need to use on a particular resource, and also to gain access to suit- able resources to run their workloads as they need them. In this thesis we present our solutions to these two problems. Firstly we propose a new model of e-infrastructure resource interaction, which we call the user–application interaction model, designed to simplify executing application on high performance computing resources. We describe the implementation of this model in the Application Hosting Environment, which pro- vides a Software as a Service layer on top of distributed e-infrastructure resources. We compare the usability of our system with commonly deployed middleware tools using five usability metrics. Our middleware and security solutions are judged to be more usable than other commonly deployed middleware tools. We go on to describe the requirements for a resource trading platform that allows users to purchase access to resources within a distributed e-infrastructure. We present the implementation of this Resource Allocation Market Place as a distributed multi- agent system, and show how it provides a highly flexible, efficient tool to schedule workflows across high performance computing resources

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications