A Scheduling Algorithm to Maximize Storm Throughput in Heterogeneous Cluster

In the most popular distributed stream processing frameworks (DSPFs), programs are modeled as a directed acyclic graph. This model allows a DSPF to benefit from the parallelism power of distributed clusters. However, choosing the proper number of vertices for each operator and finding an appropriate mapping between these vertices and processing resources have a determinative effect on overall throughput and resource utilization; while the simplicity of current DSPFs' schedulers leads these frameworks to perform poorly on large-scale clusters. In this paper, we present the design and implementation of a heterogeneity-aware scheduling algorithm that finds the proper number of the vertices of an application graph and maps them to the most suitable cluster node. We start to scale up the application graph over a given cluster gradually, by increasing the topology input rate and taking new instances from bottlenecked vertices. Our experimental results on Storm Micro-Benchmark show that 1) the prediction model estimate CPU utilization with 92% accuracy. 2) Compared to default scheduler of Storm, our scheduler provides 7% to 44% throughput enhancement. 3) The proposed method can find the solution within 4% (worst case) of the optimal scheduler which obtains the best scheduling scenario using an exhaustive search on problem design space

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Programming model abstractions for optimizing I/O intensive applications

This thesis contributes from the perspective of task-based programming models to the efforts of optimizing I/O intensive applications. Throughout this thesis, we propose programming model abstractions and mechanisms that target a twofold objective: from the one hand, improve the I/O and total performance of applications on nowadays complex storage infrastructures. From the other hand, achieve such performance improvement without increasing the complexity of applications programming. The following paragraphs briefly summarize each of our contributions. First, towards exploiting compute-I/O patterns of I/O intensive applications and transparently improving I/O and total performance, we propose a number of abstractions that we refer to as I/O Awareness abstractions. An I/O aware task-based programming model is able to separate the handling of I/O and computations by supporting I/O Tasks. The execution of such tasks can overlap with compute tasks execution. Moreover, we provide programming model support to improve I/O performance by addressing the issue of I/O congestion. This is achieved by using Storage Bandwidth Constraints to control the level of task parallelism. We support two types of such constraints: (i) Static storage bandwidth constraints that are manually set by application developers. (ii) Auto-tunable constraints that are automatically set and tuned throughout the execution of application. Second, in order to exploit the heterogeneity of modern storage systems to improve performance in a transparent manner, we propose a set of capabilities that we refer to as Storage heterogeneity Awareness. A storage-heterogeneity aware task-based programming model builds on the concepts and abstractions that are introduced in the first contribution to improve the I/O performance of applications on heterogeneous storage systems. More specifically, such programming models support the following features: (i) abstracting the heterogeneity of the storage devices and exposing them as one hierarchical storage resource. (ii) supporting dedicated I/O scheduling. (iii) Finally, we introduce a mechanism that automatically and periodically flushes obsolete data from higher storage layers to lower storage layers. Third, targeting increasing parallelism levels of applications, we propose a Hybrid Programming Model that combines task-based programming models and MPI. In this programming model, tasks are used to achieve coarse-grained parallelism on large-scale distributed infrastructures, whereas MPI is used to gain fine-grained parallelism by parallelizing tasks execution. Such a hybrid programming model offers the possibility to enable parallel I/O and high-level I/O libraries in tasks. We enable such a hybrid programming model by supporting Native MPI Tasks. These tasks are native to the programming model for two reasons: they execute task code as opposed to calling external MPI binaries or scripts. Also, the data transfers and input/output handling is done in a completely transparent manner to application developers. Therefore, increasing parallelism levels while easing the design and programming of applications. Finally, to exploit the inherent parallelism opportunities in applications and overlap computation with I/O, we propose an Eager mechanism for releasing data dependencies. Unlike the traditional approach for releasing dependencies, eagerly releasing data dependencies allows successor tasks to be released for execution as soon as their data dependencies are ready, without having to wait for predecessor task(s) to completely finish execution. In order to support the eager-release of data dependencies, we describe the following core modifications to the design of task-based programming models: (i) defining and managing data dependency relationships as parameter-aware dependencies (ii) a mechanism for notifying the programming model that an output data has been generated before the execution of the producer task ends.Aquesta tesi contribueix des de la perspectiva dels models de programació basats en tasques als esforços d’optimitzar les aplicacions intensives de I/O. Al llarg d'aquesta tesi, proposem abstraccions i mecanismes del model de programació que persegueixen un doble objectiu: per una banda, millorar la I/O i el rendiment total de les aplicacions a les complexes infraestructures d'emmagatzematge de l'actualitat. D'altra banda, aconsegueixi aquesta millora del rendiment sense augmentar la complexitat de la programació d'aplicacions. Els paràgrafs següents resumeixen cadascuna de les nostres contribucions. En primer lloc, proposem una sèrie d'abstraccions a què ens referim com a abstraccions de consciència de I/O. Un model de programació basat en tasques amb reconeixement d'I/O pot separar el maneig d'I/O i els càlculs en admetre Tasques d'I/O. L'execució d'aquestes tasques es pot superposar amb l'execució de tasques de càlcul. A més, proporcionem suport de model de programació per millorar el rendiment d'I/O en abordar el problema de la congestió d'I/O. Això s'aconsegueix mitjançant l'ús de restriccions d'amplada de banda d'emmagatzematge per controlar el nivell de paral·lelisme de tasques. Admetem dos tipus d'aquestes restriccions: estàtic i autoajustable. En segon lloc, proposem un conjunt de capacitats a què ens referim com a Consciència d'heterogeneïtat d'emmagatzematge. Un model de programació basat en tasques conscient de l'heterogeneïtat de l'emmagatzematge es basa en els conceptes i les abstraccions que s'introdueixen en la primera contribució per millorar el rendiment d'I/O de les aplicacions en sistemes d'emmagatzematge heterogenis. Més específicament, aquests models de programació admeten les característiques següents: (i) abstreure l'heterogeneïtat dels dispositius d'emmagatzematge i exposar-los com a recurs d'emmagatzematge jeràrquic. (ii) admetre la programació d'I/O dedicada. (iii) Finalment, presentem un mecanisme que descarrega automàticament i periòdicament les dades obsoletes de les capes d'emmagatzematge superiors a les capes d'emmagatzematge inferiors. En tercer lloc, proposem un model de programació híbrid que combina models de programació basats en tasques i MPI. En aquest model de programació, les tasques s'utilitzen per aconseguir un paral·lelisme de gra gruixut en infraestructures distribuïdes a gran escala, mentre que MPI es fa servir per obtenir un paral·lelisme de gra fi en paral·lelitzar l'execució de tasques. Un model d'aquest tipus de programació híbrid ofereix la possibilitat d'habilitar I/O paral·leles i biblioteques d'I/O d'alt nivell en tasques. Habilitem un model de programació híbrid d'aquest tipus en admetre tasques MPI natives que executen codi de tasca en lloc de trucar a binaris o scripts MPI externs. A més, la transferència de dades i el maneig d’entrada / sortida es realitza d’una manera completament transparent per als desenvolupadors d’aplicacions. Per tant, augmenta els nivells de paral·lelisme alhora que se'n facilita el disseny i la programació d'aplicacions. Finalment proposem un mecanisme Eager per alliberar dependències de dades. A diferència de l'enfocament tradicional per alliberar dependències, alliberar amb entusiasme les dependències de dades permet que les tasques successores s'alliberin per a la seva execució tan aviat com les dependències de dades estiguin llestes, sense haver d'esperar que les tasques predecessores acabin completament l'execució. Per tal de donar suport a l'alliberament ansiós de les dependències de dades, descrivim les següents modificacions centrals al disseny de models de programació basats en tasques: (i) definir i administrar les relacions de dependència de dades com a dependències conscients de paràmetres (ii ) un mecanisme per notificar la model de programació que s'ha generat una dada de sortida abans que finalitzi l'execució de la tasca de productor.Postprint (published version

System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014