Model-driven Scheduling for Distributed Stream Processing Systems

Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application. In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior. Specifically, we use application performance models to offer reliable estimates of the resource allocation required. Further, this intuition also drives resource mapping, and helps narrow the estimated and actual dataflow performance and resource utilization. Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.Comment: 54 page

Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure

The need for low latency analysis over high-velocity data streams motivates the need for distributed continuous dataflow systems. Contemporary stream processing systems use simple techniques to scale on elastic cloud resources to handle variable data rates. However, application QoS is also impacted by variability in resource performance exhibited by clouds and hence necessitates autonomic methods of provisioning elastic resources to support such applications on cloud infrastructure. We develop the concept of “dynamic dataflows” which utilize alternate tasks as additional control over the dataflow's cost and QoS. Further, we formalize an optimization problem to represent deployment and runtime resource provisioning that allows us to balance the application's QoS, value, and the resource cost. We propose two greedy heuristics, centralized and sharded, based on the variable-sized bin packing algorithm and compare against a Genetic Algorithm (GA) based heuristic that gives a near-optimal solution. A large-scale simulation study, using the linear road benchmark and VM performance traces from the AWS public cloud, shows that while GA-based heuristic provides a better quality schedule, the greedy heuristics are more practical, and can intelligently utilize cloud elasticity to mitigate the effect of variability, both in input data rates and cloud resource performance, to meet the QoS of fast data applications

Towards a flexible data stream analytics platform based on the GCM autonomous software component technology

International audienceBig data stream analytics platforms not only need to support performance-dictated elasticity benefiting for instance from Cloud environments. They should also support analytics that can evolve dynamically from the application viewpoint, given data nature can change so the necessary treatments on them. The benefit is that this can avoid to undeploy the current analytics, modify it off-line, redeploy the new version, and resume the analysis, missing data that arrived in the meantime. We also believe that such evolution should better be driven by autonomic behaviors whenever possible. We argue that a software component based technology, as the one we have developed so far, GCM/ProActive, can be a good fit to these needs. Using it, we present our solution, still under development, named GCM-streaming, which to our knowledge seems to be quite original

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Model-driven development of data intensive applications over cloud resources

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array

Automatic Generation of Distributed Runtime Infrastructure for Internet of Things

Ph. D. ThesisThe Internet of Things (IoT) represents a network of connected devices that are able to cooperate and interact with each other in order to reach a particular goal. To attain this, the devices are equipped with identifying, sensing, networking and processing capabilities. Cloud computing, on the other hand, is the delivering of on-demand computing services – from applications, to storage, to processing power – typically over the internet. Clouds bring a number of advantages to distributed computing because of highly available pool of virtualized computing resource. Due to the large number of connected devices, real-world IoT use cases may generate overwhelmingly large amounts of data. This prompts the use of cloud resources for processing, storage and analysis of the data. Therefore, a typical IoT system comprises of a front-end (devices that collect and transmit data), and back-end – typically distributed Data Stream Management Systems (DSMSs) deployed on the cloud infrastructure, for data processing and analysis. Increasingly, new IoT devices are being manufactured to provide limited execution environment on top of their data sensing and transmitting capabilities. This consequently demands a change in the way data is being processed in a typical IoT-cloud setup. The traditional, centralised cloud-based data processing model – where IoT devices are used only for data collection – does not provide an efficient utilisation of all available resources. In addition, the fundamental requirements of real-time data processing such as short response time may not always be met. This prompts a new processing model which is based on decentralising the data processing tasks. The new decentralised architectural pattern allows some parts of data streaming computation to be executed directly on edge devices – closer to where the data is collected. Extending the processing capabilities to the IoT devices increases the robustness of applications as well as reduces the communication overhead between different components of an IoT system. However, this new pattern poses new challenges in the development, deployment and management of IoT applications. Firstly, there exists a large resource gap between the two parts of a typical IoT system (i.e. clouds and IoT devices); hence, prompting a new approach for IoT applications deployment and management. Secondly, the new decentralised approach necessitates the deployment of DSMS on distributed clusters of heterogeneous nodes resulting in unpredictable runtime performance and complex fault characteristics. Lastly, the environment where DSMSs are deployed is very dynamic due to user or device mobility, workload variation, and resource availability. In this thesis we present solutions to address the aforementioned challenges. We investigate how a high-level description of a data streaming computation can be used to automatically generate a distributed runtime infrastructure for Internet of Things. Subsequently, we develop a deployment and management system capable of distributing different operators of a data streaming computation onto different IoT gateway devices and cloud infrastructure. To address the other challenges, we propose a non-intrusive approach for performance evaluation of DSMSs and present a protocol and a set of algorithms for dynamic migration of stateful data stream operators. To improve our migration approach, we provide an optimisation technique which provides minimal application downtime and improves the accuracy of a data stream computation

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

Scalable and responsive real time event processing using cloud computing

PhD ThesisCloud computing provides the potential for scalability and adaptability in a cost e ective manner. However, when it comes to achieving scalability for real time applications response time cannot be high. Many applications require good performance and low response time, which need to be matched with the dynamic resource allocation. The real time processing requirements can also be characterized by unpredictable rates of incoming data streams and dynamic outbursts of data. This raises the issue of processing the data streams across multiple cloud computing nodes. This research analyzes possible methodologies to process the real time data in which applications can be structured as multiple event processing networks and be partitioned over the set of available cloud nodes. The approach is based on queuing theory principles to encompass the cloud computing. The transformation of the raw data into useful outputs occurs in various stages of processing networks which are distributed across the multiple computing nodes in a cloud. A set of valid options is created to understand the response time requirements for each application. Under a given valid set of conditions to meet the response time criteria, multiple instances of event processing networks are distributed in the cloud nodes. A generic methodology to scale-up and scale-down the event processing networks in accordance to the response time criteria is de ned. The real time applications that support sophisticated decision support mechanisms need to comply with response time criteria consisting of interdependent data ow paradigms making it harder to improve the performance. Consideration is given for ways to reduce the latency,improve response time and throughput of the real time applications by distributing the event processing networks in multiple computing nodes