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

BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1

Benchmarking Distributed Stream Data Processing Systems

The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems' performance characteristics. In this paper, we propose a framework for benchmarking distributed stream processing engines. We use our suite to evaluate the performance of three widely used SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations, which are the basic type of operations in stream analytics. For this benchmark, we design workloads based on real-life, industrial use-cases inspired by the online gaming industry. The contribution of our work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we carefully separate the system under test and driver, in order to correctly represent the open world model of typical stream processing deployments and can, therefore, measure system performance under realistic conditions. Third, we build the first benchmarking framework to define and test the sustainable performance of streaming systems. Our detailed evaluation highlights the individual characteristics and use-cases of each system.Comment: Published at ICDE 201

Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm\u27s default allocation

New techniques to lower the tail latency in stream processing systems

Over the past decade, the demand for real time processing of huge amount of streaming data has emerged and grown rapidly. Apache Storm, Apache Flink, Samza and many other stream processing frameworks have been proposed and implemented to meet this need. Although lots of effort has been made to reduce the average latency of stream processing systems, how to shorten their tail latency has received little attention. This thesis presents a series of novel techniques for reducing the tail latency in stream processing systems like Apache Storm. Concretely, we present three mechanisms: (1) adaptive timeout coupled with selective replay to catch straggler tuples; (2) shared queues among different tasks of the same operator to reduce overall queueing delay; (3) latency feedback-based load balancing, intended to mitigate heterogenous scenarios. We have implemented these techniques in Apache Storm, and present experimental results using sets of micro-benchmarks as well as two topologies from Yahoo! Inc. Our results show improvement in tail latency in the range of 2%-72.9%

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