A Hybrid Approach to High Availability in Stream Processing Systems

The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail, which potentially causes hard system burden. This paper proposes s2p (Stream Process Provenance), which mainly consists of online provenance and offline provenance, to provide fine- and coarse-grained provenance in different precision. We base our design of s2p on the fact that, for a mature online DSP system, the abnormal results are rare, and the results that require a detailed analysis are even rarer. We also consider state transition in our provenance explanation. We implement s2p on Apache Flink named as s2p-flink and conduct three experiments to evaluate its scalability, efficiency, and overhead from end-to-end cost, throughput, and space overhead. Our evaluation shows that s2p-flink incurs a 13% to 32% cost overhead, 11% to 24% decline in throughput, and few additional space costs in the online provenance phase. Experiments also demonstrates the s2p-flink can scale well. A case study is presented to demonstrate the feasibility of the whole s2p solution.

Scalability is one of the common goals addressed by distributed stream processing systems. Distributed stream processing systems execute streaming applications that are segmented and distributed among several nodes across clusters, in order to cater heavy and growing stream processing use cases. Due to the tremendous benefits provided by cloud infrastructures, distributed stream processing systems are often adapted to be deployed on cloud-native environments. Container orchestrators tend to provide modularity and ease of management for containerised applications. Dynamic scalability and dynamic scheduling of streaming applications become the key points, in addressing the efficiency of cloud-native distributed stream processing systems. In this paper, the author attempts to summarise the researches that have been conducted within the domain of dynamic scaling and dynamic scheduling of stream processing systems, related to cloud-nativeness. KeywordsDistributed stream processing systemsContainer orchestratorsSchedulersStreaming application

As more aspects of our daily lives are being computerized, ever larger amounts of data are being produced at ever greater speeds. In this data lies great value, and we need technologies that enable us to extract this value. This thesis is concerned with one type of technology that allows us to do this: Distributed Stream Processing Systems (DSPS) are systems consisting of many computers that jointly process, and hence extract value from, large amounts of data at high speeds. This dissertation consists of three research projects that investigate two aspects of DSPS: In two projects, different approaches to increase the efficiency of DSPS were studied and in one project, the value of increased efficiency in stream processing was evaluated. All of these projects have been conducted on real computer systems and they are all of quantitative nature. In the first study, a graph partitioning algorithm was leveraged to schedule the workload within a DSPS. This reduced the communication load between hosts, while maintaining or increasing the throughput of the system. The second study was concerned with the auto-configuration of DSPS. We used a probabilistic black-box optimization strategy called Bayesian Optimization to increase throughput performance of DSPSs through configuration. In the third study, we investigated the value of increased efficiency of a DSPS. This was done by building a DSPS based entity ranking system and by evaluating the effect of timely data processing on the quality of the generated rankings.

With the advent of data-intensive applications that generate large volumes of real-time data, distributed stream processing systems (DSPS) become increasingly important in domains such as social networking and web analytics. In practice, DSPSs must handle highly variable workloads caused by unpredictable changes in stream rates. Cloud computing offers an elastic infrastructure that DSPSs can use to obtain resources on-demand, but an open problem is to decide on the correct resource allocation when deploying DSPSs in the cloud. This paper proposes an adaptive approach for provisioning virtual machines (VMs) for the use of a DSPS in the cloud. We initially perform a set of benchmarks across performance metrics such as network latency and jitter to explore the feasibility of cloud-based DSPS deployments. Based on these results, we propose an algorithm for VM provisioning for DSPSs that reacts to changes in the stream workload. Through a prototype implementation on Amazon EC2, we show that our approach can achieve low-latency stream processing when VMs are not overloaded, while adjusting resources dynamically with workload changes.

Modern distributed stream processing systems can potentially be applied to real time network flow processing. However, differences in performance make some systems more suitable than others for being applied to this domain. We propose a novel performance benchmark, which is based on common security analysis algorithms of NetFlow data to determine the suitability of distributed stream processing systems. Three of the most used distributed stream processing systems are bench-marked and the results are compared with NetFlow data processing challenges and requirements. The benchmark results show that each system reached a sufficient data processing speed using a basic deployment scenario with little to no configuration tuning. Our benchmark, unlike any other, enables the performance of small structured messages to be processed on any stream processing system.

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

Cloud computing offers a cost-efficient data analytics platform. This is enabled by constant innovations in tools and technologies for analyzing large volumes of data through distributed batch processing systems and real-time data through distributed stream processing systems. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. To address this stalemate, both software-based and hardware-based solutions have been proposed yet all have substantial limitations in terms of efficiency, expressiveness, and security. In this thesis, we present solutions that enable practical and expressive confidentiality- preserving batch and stream-based analytics. We achieve this by performing computations over encrypted data using Partially Homomorphic Encryption (PHE) and Property-Preserving Encryption (PPE) in novel ways, and by utilizing remote or Trusted Execution Environment (TEE) based trusted services where needed.We introduce a set of extensions and optimizations to PHE and PPE schemes and propose the novel abstraction of Secure Data Types (SDTs) which enables the application of PHE and PPE schemes in ways that improve performance and security. These abstractions are leveraged to enable a set of compilation techniques making data analytics over encrypted data more practical. When PHE alone is not expressive enough to perform analytics over encrypted data, we use a novel planner engine to decide the most efficient way of utilizing client-side completion, remote re-encryption, or trusted hardware re-encryption based on Intel Software Guard eXtensions (SGX) to overcome the limitations of PHE. We also introduce two novel symmetric PHE schemes that allow arithmetic operations over encrypted data. Being symmetric, our schemes are more efficient than the state-of-the-art asymmetric PHE schemes without compromising the level of security or the range of homomorphic operations they support. We apply the aforementioned techniques in the context of batch data analytics and demonstrate the improvements over previous systems. Finally, we present techniques designed to enable the use of PHE and PPE in resource-constrained Internet of Things (IoT) devices and demonstrate the practicality of stream processing over encrypted data.

Shuffle grouping is a technique used by stream processing frameworks to share input load among parallel instances of stateless operators. With shuffle grouping each tuple of a stream can be assigned to any available operator instance, independently from any previous assignment. A common approach to implement shuffle grouping is to adopt a Round-Robin policy, a simple solution that fares well as long as the tuple execution time is almost the same for all the tuples. However, such an assumption rarely holds in real cases where execution time strongly depends on tuple content. As a consequence, parallel stateless operators within stream processing applications may experience unpredictable unbalance that, in the end, causes undesirable increase in tuple completion times. In this paper we propose Online Shuffle Grouping (OSG), a novel approach to shuffle grouping aimed at reducing the overall tuple completion time. OSG estimates the execution time of each tuple, enabling a proactive and online scheduling of input load to the target operator instances. Sketches are used to efficiently store the otherwise large amount of information required to schedule incoming load. We provide a probabilistic analysis and illustrate, through both simulations and a running prototype, its impact on stream processing applications.

Stream Processing (SP) has evolved as the leading paradigm to process and gain value from the high volume of streaming data produced, e.g., in the domain of the Internet of Things. An SP system is a middleware that deploys a network of operators between data sources, such as sensors, and the consuming applications. SP systems typically face intense and highly dynamic data streams. Parallelization and elasticity enable SP systems to process these streams with continuous high quality of service. The current research landscape provides a broad spectrum of methods for parallelization and elasticity in SP. Each method makes specific assumptions and focuses on particular aspects. However, the literature lacks a comprehensive overview and categorization of the state of the art in SP parallelization and elasticity, which is necessary to consolidate the state of the research and to plan future research directions on this basis. Therefore, in this survey, we study the literature and develop a classification of current methods for both parallelization and elasticity in SP systems.

Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.

A growing number of applications require continuous processing of high-throughput data streams, e.g., financial analysis, network traffic monitoring, or big data analytics. Performing these analyses by using Distributed Stream Processing Systems (DSPSs) in large clusters is emerging as a promising solution to address the scalability challenges posed by these kind of scenarios. Yet, the high time-variability of stream characteristics makes it very inefficient to statically allocate the data-center resources needed to guarantee application Service Level Agreements (SLAs) and calls for original, dynamic, and adaptive resource allocation strategies. In this paper we analyze the problem of planning adaptive replication strategies for DSPS applications under the challenging assumption of minimal statistical knowledge of input characteristics. We investigate and evaluate how different CP techniques can be employed, and quantitatively show how different alternatives offer different trade-offs between problem solution time and stream processing runtime cost through experimental results over realistic testbeds.KeywordsStream ProcessingLarge Neighborhood SearchVLDB EndowmentReplica ActivationDistribute Stream ProcessingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Many emerging on-line data analysis applications require applying continuous query operations such as correlation, aggregation, and filtering to data streams in real-time. Distributed stream processing systems allow in-network stream processing to achieve better scalability and quality-of-service (QoS) provision. In this paper we present Synergy, a distributed stream processing middleware that provides sharing-aware component composition. Synergy enables efficient reuse of both data streams and processing components, while composing distributed stream processing applications with QoS demands. Synergy provides a set of fully distributed algorithms to discover and evaluate the reusability of available data streams and processing components when instantiating new stream applications. For QoS provision, Synergy performs QoS impact projection to examine whether the shared processing can cause QoS violations on currently running applications. We have implemented a prototype of the Synergy middleware and evaluated its performance on both PlanetLab and simulation testbeds. The experimental results show that Synergy can achieve much better resource utilization and QoS provision than previously proposed schemes, by judiciously sharing streams and processing components during application composition.

In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination trees. In this paper, we focus on the problem of constructing dissemination trees to minimize the average loss of fidelity of the system. We observe that existing heuristic-based approaches can only explore a limited solution space and hence may lead to sub-optimal solutions. On the contrary, we propose an adaptive and cost-based approach. Our cost model takes into account both the processing cost and the communication cost. Furthermore, as a distributed stream processing system is vulnerable to inaccurate statistics, runtime fluctuations of data characteristics, server workloads, and network conditions, we have designed our scheme to be adaptive to these situations: an operational dissemination tree may be incrementally transformed to a more cost-effective one. Our adaptive strategy employs distributed decisions made by the distributed servers independently based on localized statistics collected by each server at runtime. For a relatively static environment, we also propose two static tree construction algorithms relying on apriori system statistics. These static trees can also be used as initial trees in a dynamic environment. We apply our schemes to both single- and multi-object dissemination. Our extensive performance study shows that the adaptive mechanisms are effective in a dynamic context and the proposed static tree construction algorithms perform close to optimal in a static environment.

Communication-efficient processing of multiple continuous aggregate queries

SummaryLoad balancing among the processing elements (PEs) of distributed stream processing system (DSPS) is a key issue in the presence of data skewness. Existing data partitioning schemes for DSPS suffer from the scalability problem and system in‐efficiency. Non‐key based partitioning strategies raise prohibitively high memory overhead for the stateful operations with a large number of keys and high data parallelism, while the key‐based schemes introduce load imbalance for highly skewed data. Predicting the nature of stream data in advance can help to reduce the load imbalance among the PEs of DSPS. For this purpose, the heavy hitter algorithms approximate the hot items of streaming data. However, existing designs suffer from unsatisfied prediction accuracy. In this work, we propose an efficient algorithm to filter hot items in a stream of incoming data. The proposed scheme dynamically monitors the items of a stream and greatly improves the accuracy of estimation by keeping the actual key‐value pair for the frequent items. On one hand, to ensure better load balancing for the skewed data streams, the detected hot keys are directed to more than two PEs randomly from the limited workers. On the other hand, for less frequent keys, the proposed scheme explores the principle of the power of two choices to distribute load. We conduct extensive experiments on both real‐world and synthetic data sets. The results show that the proposed pre‐filtering approach significantly outperforms existing designs in terms of prediction accuracy. The results also show that our design achieves a more balanced load as compared to the existing designs.