Enabling Fairness in Cloud Computing Infrastructures

Cloud computing has emerged as a key technology in many ways over the past few years, evidenced by the fact that 93% of the organizations is either running applications or experimenting with Infrastructure-as-a-Service (IaaS) cloud. Hence, to meet the demands of a large set of target audience, IaaS cloud service providers consolidate applications belonging to multiple tenants. However, consolidation of applications leads to performance interference with each other as these applications end up competing for the shared resources violating QoS of the executing tenants. This dissertation investigates the implications of interference in consolidated cloud computing environments to enable fairness in the execution of applications across tenants. In this context, this dissertation identifies three key issues in cloud computing infrastructures. We observe that tenants using IaaS public clouds share multi-core datacenter servers. In such a situation, we identify that the applications belonging to tenants might compete for shared architectural resources like Last Level Cache (LLC) and bandwidth to memory, slowing down the execution time of applications. This necessitates a need for a technique that can accurately estimate the slowdown in execution time caused due to multi-tenant execution. Such slowdown estimates can be used to bill tenants appropriately enabling fairness among tenants. For private datacenters, where performance degradation cannot be tolerated, it becomes critical to detect interference and investigate its root cause. Under such circumstances, there is a need for a real-time, lightweight and scalable mechanism that can detect performance degradation and identify the root cause resource which applications are contending for (I/O, network, CPU, Shared Cache). Finally, the advent of microservice computing environments, calls for a need to rethink resource management strategies in multi-tenant execution scenarios. Specifically, we observe that the visibility enabled by microservices execution framework can be exploited to achieve high throughput and resource utilization while still meeting Service Level Agreements (SLAs) in multi-tenant execution scenarios. To enable this, we propose techniques that can dynamically batch and reorder requests propagating through individual microservice stages within an application.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149844/1/ramsri_1.pd

Colocation-aware Resource Management for Distributed Parallel Applications in Consolidated Clusters

Department of Computer Science and EngineeringConsolidated clusters, which run various distributed parallel applications such as big data frameworks, machine learning applications, and scienti???c applications to solve complex problems in wide range of fields, are already used commonly. Resource providers allow various applications with different characteristics to execute together to efficiently utilize their resources. There are some important issues about scheduling applications to resources. When applications share the same resources, interference between them affects their performance. The performance of applications can be improved or degraded depending on which resources are used to execute them based on various characteristics of applications and resources. Characteristics and resource requirements of applications can constrain their placement, and these constraints can be extended to constraints between applications. These issues should be considered to manage resource e???ciently and improve the performance of applications. In this thesis, we study how to manage resources e???ciently while scheduling distributed parallel applications in consolidated clusters. First, we present a holistic VM placement technique for distributed parallel applications in heterogeneous virtual cluster, aiming to maximize the e???ciency of the cluster and consequently reduce cost for service providers and users. We analyze the e???ect of heterogeneity of resource, di???erent VM con???gurations, and interference between VMs on the performance of distributed parallel applications and propose a placement technique that uses a machine learning algorithm to estimate the runtime of a distributed parallel application. Second, we present a two-level scheduling algorithms, which distribute applications to platforms then map tasks to each node. we analyze the platform and co-runner a???nities of looselycoupled applications and use them for scheduling decision. Third, we study constraint-aware VM placement in heterogeneous clusters. We present a modelofVMplacementconstraintsandconstraint-awareVMplacementalgorithms. Weanalyze the e???ect of VM placement constraint, and evaluate the performance of algorithms over various settings with simulation and experiments in a small cluster. Finally, we propose interference-awareresource management system for CNN models in GPU cluster. We analyze the e???ect of interference between CNN models. We then propose techniques to mitigate slowdown from interference for target model, and to predict performance of CNN models when they are co-located. We propose heuristic algorithm to schedule CNN models, and evaluate the techniques and algorithm from experiments in GPU cluster.clos

The Fifth Workshop on HPC Best Practices: File Systems and Archives

The workshop on High Performance Computing (HPC) Best Practices on File Systems and Archives was the fifth in a series sponsored jointly by the Department Of Energy (DOE) Office of Science and DOE National Nuclear Security Administration. The workshop gathered technical and management experts for operations of HPC file systems and archives from around the world. Attendees identified and discussed best practices in use at their facilities, and documented findings for the DOE and HPC community in this report

A differentiated proposal of three dimension i/o performance characterization model focusing on storage environments

The I/O bottleneck remains a central issue in high-performance environments. Cloud computing, high-performance computing (HPC) and big data environments share many underneath difficulties to deliver data at a desirable time rate requested by high-performance applications. This increases the possibility of creating bottlenecks throughout the application feeding process by bottom hardware devices located in the storage system layer. In the last years, many researchers have been proposed solutions to improve the I/O architecture considering different approaches. Some of them take advantage of hardware devices while others focus on a sophisticated software approach. However, due to the complexity of dealing with high-performance environments, creating solutions to improve I/O performance in both software and hardware is challenging and gives researchers many opportunities. Classifying these improvements in different dimensions allows researchers to understand how these improvements have been built over the years and how it progresses. In addition, it also allows future efforts to be directed to research topics that have developed at a lower rate, balancing the general development process. This research present a three-dimension characterization model for classifying research works on I/O performance improvements for large scale storage computing facilities. This classification model can also be used as a guideline framework to summarize researches providing an overview of the actual scenario. We also used the proposed model to perform a systematic literature mapping that covered ten years of research on I/O performance improvements in storage environments. This study classified hundreds of distinct researches identifying which were the hardware, software, and storage systems that received more attention over the years, which were the most researches proposals elements and where these elements were evaluated. In order to justify the importance of this model and the development of solutions that targets I/O performance improvements, we evaluated a subset of these improvements using a a real and complete experimentation environment, the Grid5000. Analysis over different scenarios using a synthetic I/O benchmark demonstrates how the throughput and latency parameters behaves when performing different I/O operations using distinct storage technologies and approaches.O gargalo de E/S continua sendo um problema central em ambientes de alto desempenho. Os ambientes de computação em nuvem, computação de alto desempenho (HPC) e big data compartilham muitas dificuldades para fornecer dados em uma taxa de tempo desejável solicitada por aplicações de alto desempenho. Isso aumenta a possibilidade de criar gargalos em todo o processo de alimentação de aplicativos pelos dispositivos de hardware inferiores localizados na camada do sistema de armazenamento. Nos últimos anos, muitos pesquisadores propuseram soluções para melhorar a arquitetura de E/S considerando diferentes abordagens. Alguns deles aproveitam os dispositivos de hardware, enquanto outros se concentram em uma abordagem sofisticada de software. No entanto, devido à complexidade de lidar com ambientes de alto desempenho, criar soluções para melhorar o desempenho de E/S em software e hardware é um desafio e oferece aos pesquisadores muitas oportunidades. A classificação dessas melhorias em diferentes dimensões permite que os pesquisadores entendam como essas melhorias foram construídas ao longo dos anos e como elas progridem. Além disso, também permite que futuros esforços sejam direcionados para tópicos de pesquisa que se desenvolveram em menor proporção, equilibrando o processo geral de desenvolvimento. Esta pesquisa apresenta um modelo de caracterização tridimensional para classificar trabalhos de pesquisa sobre melhorias de desempenho de E/S para instalações de computação de armazenamento em larga escala. Esse modelo de classificação também pode ser usado como uma estrutura de diretrizes para resumir as pesquisas, fornecendo uma visão geral do cenário real. Também usamos o modelo proposto para realizar um mapeamento sistemático da literatura que abrangeu dez anos de pesquisa sobre melhorias no desempenho de E/S em ambientes de armazenamento. Este estudo classificou centenas de pesquisas distintas, identificando quais eram os dispositivos de hardware, software e sistemas de armazenamento que receberam mais atenção ao longo dos anos, quais foram os elementos de proposta mais pesquisados e onde esses elementos foram avaliados. Para justificar a importância desse modelo e o desenvolvimento de soluções que visam melhorias no desempenho de E/S, avaliamos um subconjunto dessas melhorias usando um ambiente de experimentação real e completo, o Grid5000. Análises em cenários diferentes usando um benchmark de E/S sintética demonstra como os parâmetros de vazão e latência se comportam ao executar diferentes operações de E/S usando tecnologias e abordagens distintas de armazenamento

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

Methods and design issues for next generation network-aware applications

Networks are becoming an essential component of modern cyberinfrastructure and this work describes methods of designing distributed applications for high-speed networks to improve application scalability, performance and capabilities. As the amount of data generated by scientific applications continues to grow, to be able to handle and process it, applications should be designed to use parallel, distributed resources and high-speed networks. For scalable application design developers should move away from the current component-based approach and implement instead an integrated, non-layered architecture where applications can use specialized low-level interfaces. The main focus of this research is on interactive, collaborative visualization of large datasets. This work describes how a visualization application can be improved through using distributed resources and high-speed network links to interactively visualize tens of gigabytes of data and handle terabyte datasets while maintaining high quality. The application supports interactive frame rates, high resolution, collaborative visualization and sustains remote I/O bandwidths of several Gbps (up to 30 times faster than local I/O). Motivated by the distributed visualization application, this work also researches remote data access systems. Because wide-area networks may have a high latency, the remote I/O system uses an architecture that effectively hides latency. Five remote data access architectures are analyzed and the results show that an architecture that combines bulk and pipeline processing is the best solution for high-throughput remote data access. The resulting system, also supporting high-speed transport protocols and configurable remote operations, is up to 400 times faster than a comparable existing remote data access system. Transport protocols are compared to understand which protocol can best utilize high-speed network connections, concluding that a rate-based protocol is the best solution, being 8 times faster than standard TCP. An HD-based remote teaching application experiment is conducted, illustrating the potential of network-aware applications in a production environment. Future research areas are presented, with emphasis on network-aware optimization, execution and deployment scenarios

Accelerating Network Communication and I/O in Scientific High Performance Computing Environments

High performance computing has become one of the major drivers behind technology inventions and science discoveries. Originally driven through the increase of operating frequencies and technology scaling, a recent slowdown in this evolution has led to the development of multi-core architectures, which are supported by accelerator devices such as graphics processing units (GPUs). With the upcoming exascale era, the overall power consumption and the gap between compute capabilities and I/O bandwidth have become major challenges. Nowadays, the system performance is dominated by the time spent in communication and I/O, which highly depends on the capabilities of the network interface. In order to cope with the extreme concurrency and heterogeneity of future systems, the software ecosystem of the interconnect needs to be carefully tuned to excel in reliability, programmability, and usability. This work identifies and addresses three major gaps in today's interconnect software systems. The I/O gap describes the disparity in operating speeds between the computing capabilities and second storage tiers. The communication gap is introduced through the communication overhead needed to synchronize distributed large-scale applications and the mixed workload. The last gap is the so called concurrency gap, which is introduced through the extreme concurrency and the inflicted learning curve posed to scientific application developers to exploit the hardware capabilities. The first contribution is the introduction of the network-attached accelerator approach, which moves accelerators into a "stand-alone" cluster connected through the Extoll interconnect. The novel communication architecture enables the direct accelerators communication without any host interactions and an optimal application-to-compute-resources mapping. The effectiveness of this approach is evaluated for two classes of accelerators: Intel Xeon Phi coprocessors and NVIDIA GPUs. The next contribution comprises the design, implementation, and evaluation of the support of legacy codes and protocols over the Extoll interconnect technology. By providing TCP/IP protocol support over Extoll, it is shown that the performance benefits of the interconnect can be fully leveraged by a broader range of applications, including the seamless support of legacy codes. The third contribution is twofold. First, a comprehensive analysis of the Lustre networking protocol semantics and interfaces is presented. Afterwards, these insights are utilized to map the LNET protocol semantics onto the Extoll networking technology. The result is a fully functional Lustre network driver for Extoll. An initial performance evaluation demonstrates promising bandwidth and message rate results. The last contribution comprises the design, implementation, and evaluation of two easy-to-use load balancing frameworks, which transparently distribute the I/O workload across all available storage system components. The solutions maximize the parallelization and throughput of file I/O. The frameworks are evaluated on the Titan supercomputing systems for three I/O interfaces. For example for large-scale application runs, POSIX I/O and MPI-IO can be improved by up to 50% on a per job basis, while HDF5 shows performance improvements of up to 32%

Using TCP/IP traffic shaping to achieve iSCSI service predictability

This thesis reproduces the properties of load interference common in many storage devices using resource sharing for flexibility and maximum hardware utilization. The nature of resource sharing and load is studied and compared to assumptions and models used in previous work. The results are used to design a method for throttling iSCSI initiators, attached to an iSCSI target server, using a packet delay module in Linux Traffic Control. The packet delay throttle enables close-to-linear rate reduction for both read and write operations. Iptables and Ipset are used to add dynamic packet matching needed for rapidly changing throttling values. All throttling is achieved without triggering TCP retransmit timeout and subsequent slow start caused by packet loss. A control mechanism for dynamically adapting throttling values to rapidly changing workloads is implemented using a modified proportional integral derivative (PID) controller. Using experiments, control engineering filtering techniques and results from previous research, a suitable per resource saturation indicator was found. The indicator is an exponential moving average of the wait time of active resource consumers. It is used as input value to the PID controller managing the packet rates of resource consumers, creating a closed control loop managed by the PID controller. Finally a prototype of an autonomic resource prioritization framework is designed. The framework identifies and maintains information about resources, their consumers, their average wait time for active consumers and their set of throttleable consumers. The information is kept in shared memory and a PID controller is spawned for each resource, thus safeguarding read response times by throttling writers on a per-resource basis. The framework is exposed to extreme workload changes and demonstrates high ability to keep read response time below a predefined threshold. Using moderate tuning efforts the framework exhibits low overhead and resource consumption, promising suitability for large scale operation in production environments

Proceedings of the First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014): Porto, Portugal

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014