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

Integrative Dynamic Reconfiguration in a Parallel Stream Processing Engine

Load balancing, operator instance collocations and horizontal scaling are critical issues in Parallel Stream Processing Engines to achieve low data processing latency, optimized cluster utilization and minimized communication cost respectively. In previous work, these issues are typically tackled separately and independently. We argue that these problems are tightly coupled in the sense that they all need to determine the allocations of workloads and migrate computational states at runtime. Optimizing them independently would result in suboptimal solutions. Therefore, in this paper, we investigate how these three issues can be modeled as one integrated optimization problem. In particular, we first consider jobs where workload allocations have little effect on the communication cost, and model the problem of load balance as a Mixed-Integer Linear Program. Afterwards, we present an extended solution called ALBIC, which support general jobs. We implement the proposed techniques on top of Apache Storm, an open-source Parallel Stream Processing Engine. The extensive experimental results over both synthetic and real datasets show that our techniques clearly outperform existing approaches

When Two Choices Are not Enough: Balancing at Scale in Distributed Stream Processing

Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: more workers implies fewer keys per worker, so it becomes harder to "average out" the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heaving hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to $d \geq 2$ choices to ensure a balanced load, where $d$ is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by $\mathbf{150\%}$ and $\mathbf{60\%}$ respectively over the previous state-of-the-art when deployed on Apache Storm.Comment: 12 pages, 14 Figures, this paper is accepted and will be published at ICDE 201

Prediction based scaling in a distributed stream processing cluster

2020 Spring.Includes bibliographical references.Proliferation of IoT sensors and applications have enabled us to monitor and analyze scientific and social phenomena with continuously arriving voluminous data. To provide real-time processing capabilities over streaming data, distributed stream processing engines (DSPEs) such as Apache STORM and Apache FLINK have been widely deployed. These frameworks support computations over large-scale, high frequency streaming data. However, current on-demand auto-scaling features in these systems may result in an inefficient resource utilization which is closely related to cost effectiveness in popular cloud-based computing environments. We propose ARSTREAM, an auto-scaling computing environment that manages fluctuating throughputs for data from sensor networks, while ensuring efficient resource utilization. We have built an Artificial Neural Network model for predicting data processing queues and this model captures non-linear relationships between data arrival rates, resource utilization, and the size of data processing queue. If a bottleneck is predicted, ARSTREAM scales-out the current cluster automatically for current jobs without halting them at the user level. In addition, ARSTREAM incorporates threshold-based re-balancing to minimize data loss during extreme peak traffic that could not be predicted by our model. Our empirical benchmarks show that ARSTREAM forecasts data processing queue sizes with RMSE of 0.0429 when tested on real-time data

Collaborative Reuse of Streaming Dataflows in IoT Applications

Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams. They are used by Internet of Things (IoT) applications to analyze sensor data from Smart City cyber-infrastructure, and make active utility management decisions. As the ecosystem of such IoT applications that leverage shared urban sensor streams continue to grow, applications will perform duplicate pre-processing and analytics tasks. This offers the opportunity to collaboratively reuse the outputs of overlapping dataflows, thereby improving the resource efficiency. In this paper, we propose \emph{dataflow reuse algorithms} that given a submitted dataflow, identifies the intersection of reusable tasks and streams from a collection of running dataflows to form a \emph{merged dataflow}. Similar algorithms to unmerge dataflows when they are removed are also proposed. We implement these algorithms for the popular Apache Storm DSPS, and validate their performance and resource savings for 35 synthetic dataflows based on public OPMW workflows with diverse arrival and departure distributions, and on 21 real IoT dataflows from RIoTBench.Comment: To appear in IEEE eScience Conference 201

DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data

Title from PDF of title page, viewed April 19, 2017Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 57-58)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016Real-time event modeling and recognition is one of the major research areas that is yet to reach its fullest potential. In the exploration of a system to fit in the tremendous challenges posed by data growth, several big data ecosystems have evolved. Big Data Ecosystems are currently dealing with various architectural models, each one aimed to solve a real-time problem with ease. There is an increasing demand for building a dynamic architecture using the powers of real-time and computational intelligence under a single workflow to effectively handle fast-changing business environments. To the best of our knowledge, there is no attempt at supporting a distributed machine-learning paradigm by separating learning and recognition tasks using Big Data Ecosystems. The focus of our study is to design a distributed machine learning model by evaluating the various machine-learning algorithms for event detection learning and predictive analysis with different features in audio domains. We propose an integrated architectural model, called DMLA, to handle real-time problems that can enhance the richness in the information level and at the same time reduce the overhead of dealing with diverse architectural constraints. The DMLA architecture is the variant of a Lambda Architecture that combines the power of Apache Spark, Apache Storm (Heron), and Apache Kafka to handle massive amounts of data using both streaming and batch processing techniques. The primary dimension of this study is to demonstrate how DMLA recognizes real-time, real-world events (e.g., fire alarm alerts, babies needing immediate attention, etc.) that would require a quick response by the users. Detection of contextual information and utilizing the appropriate model dynamically has been distributed among the components of the DMLA architecture. In the DMLA framework, a dynamic predictive model, learned from the training data in Spark, is loaded from the context information into a Storm topology to recognize/predict the possible events. The event-based context aware solution was designed for real-time, real-world events. The Spark based learning had the highest accuracy of over 80% among several machine-learning models and the Storm topology model achieved a recognition rate of 75% in the best performance. We verify the effectiveness of the proposed architecture is effective in real-time event-based recognition in audio domains.Introduction -- Background and related work -- Proposed framework -- Results and evaluation -- Conclusion and future wor

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe