DAG-based Task Orchestration for Edge Computing

As we increase the number of personal computing devices that we carry (mobile devices, tablets, e-readers, and laptops) and these come equipped with increasing resources, there is a vast potential computation power that can be utilized from those devices. Edge computing promises to exploit these underlying computation resources closer to users to help run latency-sensitive applications such as augmented reality and video analytics. However, one key missing piece has been how to incorporate personally owned unmanaged devices into a usable edge computing system. The primary challenges arise due to the heterogeneity, lack of interference management, and unpredictable availability of such devices. In this paper we propose an orchestration framework IBDASH, which orchestrates application tasks on an edge system that comprises a mix of commercial and personal edge devices. IBDASH targets reducing both end-to-end latency of execution and probability of failure for applications that have dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH takes memory constraints of each edge device and network bandwidth into consideration. To assess the effectiveness of IBDASH, we run real application tasks on real edge devices with widely varying capabilities.We feed these measurements into a simulator that runs IBDASH at scale. Compared to three state-of-the-art edge orchestration schemes, LAVEA, Petrel, and LaTS, and two intuitive baselines, IBDASH reduces the end-to-end latency and probability of failure, by 14% and 41% on average respectively. The main takeaway from our work is that it is feasible to combine personal and commercial devices into a usable edge computing platform, one that delivers low latency and predictable and high availability

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

The fast multipole method at exascale

This thesis presents a top to bottom analysis on designing and implementing fast algorithms for current and future systems. We present new analysis, algorithmic techniques, and implementations of the Fast Multipole Method (FMM) for solving N- body problems. We target the FMM because it is broadly applicable to a variety of scientific particle simulations used to study electromagnetic, fluid, and gravitational phenomena, among others. Importantly, the FMM has asymptotically optimal time complexity with guaranteed approximation accuracy. As such, it is among the most attractive solutions for scalable particle simulation on future extreme scale systems. We specifically address two key challenges. The first challenge is how to engineer fast code for today’s platforms. We present the first in-depth study of multicore op- timizations and tuning for FMM, along with a systematic approach for transforming a conventionally-parallelized FMM into a highly-tuned one. We introduce novel opti- mizations that significantly improve the within-node scalability of the FMM, thereby enabling high-performance in the face of multicore and manycore systems. The second challenge is how to understand scalability on future systems. We present a new algorithmic complexity analysis of the FMM that considers both intra- and inter- node communication costs. Using these models, we present results for choosing the optimal algorithmic tuning parameter. This analysis also yields the surprising prediction that although the FMM is largely compute-bound today, and therefore highly scalable on current systems, the trajectory of processor architecture designs, if there are no significant changes could cause it to become communication-bound as early as the year 2015. This prediction suggests the utility of our analysis approach, which directly relates algorithmic and architectural characteristics, for enabling a new kind of highlevel algorithm-architecture co-design. To demonstrate the scientific significance of FMM, we present two applications namely, direct simulation of blood which is a multi-scale multi-physics problem and large-scale biomolecular electrostatics. MoBo (Moving Boundaries) is the infrastruc- ture for the direct numerical simulation of blood. It comprises of two key algorithmic components of which FMM is one. We were able to simulate blood flow using Stoke- sian dynamics on 200,000 cores of Jaguar, a peta-flop system and achieve a sustained performance of 0.7 Petaflop/s. The second application we propose as future work in this thesis is biomolecular electrostatics where we solve for the electrical potential using the boundary-integral formulation discretized with boundary element methods (BEM). The computational kernel in solving the large linear system is dense matrix vector multiply which we propose can be calculated using our scalable FMM. We propose to begin with the two dielectric problem where the electrostatic field is cal- culated using two continuum dielectric medium, the solvent and the molecule. This is only a first step to solving biologically challenging problems which have more than two dielectric medium, ion-exclusion layers, and solvent filled cavities. Finally, given the difficulty in producing high-performance scalable code, productivity is a key concern. Recently, numerical algorithms are being redesigned to take advantage of the architectural features of emerging multicore processors. These new classes of algorithms express fine-grained asynchronous parallelism and hence reduce the cost of synchronization. We performed the first extensive performance study of a recently proposed parallel programming model, called Concurrent Collections (CnC). In CnC, the programmer expresses her computation in terms of application-specific operations, partially-ordered by semantic scheduling constraints. The CnC model is well-suited to expressing asynchronous-parallel algorithms, so we evaluate CnC using two dense linear algebra algorithms in this style for execution on state-of-the-art mul- ticore systems. Our implementations in CnC was able to match and in some cases even exceed competing vendor-tuned and domain specific library codes. We combine these two distinct research efforts by expressing FMM in CnC, our approach tries to marry performance with productivity that will be critical on future systems. Looking forward, we would like to extend this to distributed memory machines, specifically implement FMM in the new distributed CnC, distCnC to express fine-grained paral- lelism which would require significant effort in alternative models.Ph.D

Performance Modeling and Resource Management for Mapreduce Applications

Big Data analytics is increasingly performed using the MapReduce paradigm and its open-source implementation Hadoop as a platform choice. Many applications associated with live business intelligence are written as complex data analysis programs defined by directed acyclic graphs of MapReduce jobs. An increasing number of these applications have additional requirements for completion time guarantees. The advent of cloud computing brings a competitive alternative solution for data analytic problems while it also introduces new challenges in provisioning clusters that provide best cost-performance trade-offs. In this dissertation, we aim to develop a performance evaluation framework that enables automatic resource management for MapReduce applications in achieving different optimization goals. It consists of the following components: (1) a performance modeling framework that estimates the completion time of a given MapReduce application when executed on a Hadoop cluster according to its input data sets, the job settings and the amount of allocated resources for processing it; (2) a resource allocation strategy for deadline-driven MapReduce applications that automatically tailors and controls the resource allocation on a shared Hadoop cluster to different applications to achieve their (soft) deadlines; (3) a simulator-based solution to the resource provision problem in public cloud environment that guides the users to determine the types and amount of resources that should lease from the service provider for achieving different goals; (4) an optimization strategy to automatically determine the optimal job settings within a MapReduce application for efficient execution and resource usage. We validate the accuracy, efficiency, and performance benefits of the proposed framework using a set of realistic MapReduce applications on both private cluster and public cloud environment

D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method

Architecting Time-Critical Big-Data Systems

Current infrastructures for developing big-data applications are able to process –via big-data analytics- huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches