Elastic Scaling for Distributed Latency-Sensitive Data Stream Operators

Matteis, Tiziano De; Mencagli, Gabriele

doi:10.1109/pdp.2017.31

Cited by 27 publications

(19 citation statements)

References 11 publications

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“…e other instances continue processing. Later, De Ma eis and Mencagli extend their approach to horizontal scaling across several machines [35], building on the same predictive control model.…”

Section: Centralized Elasticitymentioning

confidence: 99%

A Comprehensive Survey on Parallelization and Elasticity in Stream Processing

Röger

Mayer

2019

ACM Comput. Surv.

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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 ings. 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 enables SP systems to process these streams with continuously high quality of service. e current research landscape provides a broad spectrum of methods for parallelization and elasticity in SP. Each method makes speci c assumptions and focuses on particular aspects of the problem. 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. erefore, in this survey, we study the literature and develop a classi cation of current methods for both parallelization and elasticity in SP systems. or even di erent processing nodes in a shared-nothing cloud-based infrastructure. Frequent state synchronization must not hamper parallel processing, while the processing results have to remain consistent. Research proposes di erent approaches for parallel, stateless and stateful SP. ey di er in assumptions about the operator functions and state externalization mechanisms an SP system supports.is led to the development of a broad range of parallelization approaches tackling di erent problem cases.Second, how to continuously adapt the level of parallelization when the conditions of the SP operators, e.g. the workload or resources available, change at runtime. On the one hand, an SP system always needs enough resources to process the input data streams with a satisfying quality of service (QoS), e.g. latency or throughput. On the other hand, continuous provisioning of computing resources for peak workloads wastes resources at o -peak hours. us, an elastic SP system scales its resources according to the current need. Cloud computing provides on-demand resources to realize such elasticity [9]. e pay-as-you-go business model of cloud computing allows to cut costs by dynamically adapting the resource reservations to the needs of the SP system. It is challenging to strive the right balance between resource over-provisioning-which is costly, but is robust to workload uctuations-and on-demand scaling-which is cheap, but is vulnerable to sudden workload peaks. To this end, academia and industry developed elasticity methods. Again, they di er in their optimization objectives and assumptions about the operator parallelization model employed, the target system architecture, state management as well as timing and methodology.While there are many works that propose methods and solutions for speci c parallelization and elasticity problems in SP systems, there is a severe lack of overview, comparison, and classi cation of these methods. When we investigate...

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Section: Centralized Elasticitymentioning

confidence: 99%

A Comprehensive Survey on Parallelization and Elasticity in Stream Processing

Röger

Mayer

2019

ACM Comput. Surv.

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“…Differently from the above cited works that present reactive scaling strategies, De Matteis and Mencagli propose a proactive strategy for a DSP distributed environment that takes into account a limited future time horizon to choose the reconfigurations. Differently from our solution, their approach is not integrated in an existing DSP framework.…”

Section: Related Workmentioning

confidence: 99%

Optimal operator deployment and replication for elastic distributed data stream processing

Cardellini

Presti

Nardelli

et al. 2017

Concurrency and Computation

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Summary Processing data in a timely manner, data stream processing (DSP) applications are receiving an increasing interest for building new pervasive services. Due to the unpredictability of data sources, these applications often operate in dynamic environments; therefore, they require the ability to elastically scale in response to workload variations. In this paper, we deal with a key problem for the effective runtime management of a DSP application in geo‐distributed environments: We investigate the placement and replication decisions while considering the application and resource heterogeneity and the migration overhead, so to select the optimal adaptation strategy that can minimize migration costs while satisfying the application quality of service (QoS) requirements. We present elastic DSP replication and placement (EDRP), a unified framework for the QoS‐aware initial deployment and runtime elasticity management of DSP applications. In EDRP, the deployment and runtime decisions are driven by the solution of a suitable integer linear programming problem, whose objective function captures the relative importance between QoS goals and reconfiguration costs. We also present the implementation of EDRP and the related mechanisms on Apache Storm. We conduct a thorough experimental evaluation, both numerical and prototype‐based, that shows the benefits achieved by EDRP on the application performance.

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“…Other works (e.g., [7,[35][36][37][38]) use more complex centralized policies to determine the scaling decisions, exploiting optimization methods that rely on the knowledge of a global model, such as integer linear programming [7], control theory [35], queueing theory [36], and fuzzy logic [37]. In [7], we presented an integer linear programming problem for the run-time elasticity management of DSP applications that takes into account the application reconfiguration costs after scaling operations and aims to minimize them while satisfying the application performance requirements.…”

Section: Elasticity Policiesmentioning

confidence: 99%

“…However, their solution is specifically designed for sliding-window preference queries executed on multi-core architectures. While the previously mentioned policies are reactive and cannot thus provision replicas in advance, De Matteis and Mencagli [35] proposed a proactive control-based strategy that takes into account a limited future time horizon to choose the reconfigurations. Centralized heuristic policies have been also proposed in [6,[38][39][40].…”

Section: Elasticity Policiesmentioning

confidence: 99%

“…A DSP application can be regarded as a directed acyclic graph (DAG), where data sources, operators, and consumers are connected by streams (see Figure 1a) An operator is a self-contained processing element that carries out a specific operation (e.g., filtering and POS-tagging), whereas a stream is an unbounded sequence of data (e.g., tuples). DSP applications are usually employed in latency-sensitive domains [2,35,36], where reduced response time is required. Although multiple definitions of response time exist in the context of DSP, the widely used one defines it as the overall processing and transmission latency from a data source to a final consumer on the application DAG.…”

Section: Dsp Application Modelmentioning

confidence: 99%

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Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach

et al. 2018

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The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically scale their execution on multiple computing resources to process the incoming data flow in parallel. Being that data sources and consumers are usually located at the network edges, nowadays the presence of geo-distributed computing resources represents an attractive environment for DSP. However, controlling the applications and the processing infrastructure in such wide-area environments represents a significant challenge. In this paper, we present a hierarchical solution for the autonomous control of elastic DSP applications and infrastructures. It consists of a two-layered hierarchical solution, where centralized components coordinate subordinated distributed managers, which, in turn, locally control the elastic adaptation of the application components and deployment regions. Exploiting this framework, we design several self-adaptation policies, including reinforcement learning based solutions. We show the benefits of the presented self-adaptation policies with respect to static provisioning solutions, and discuss the strengths of reinforcement learning based approaches, which learn from experience how to optimize the application performance and resource allocation.

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Elastic Scaling for Distributed Latency-Sensitive Data Stream Operators

Cited by 27 publications

References 11 publications

A Comprehensive Survey on Parallelization and Elasticity in Stream Processing

A Comprehensive Survey on Parallelization and Elasticity in Stream Processing

Optimal operator deployment and replication for elastic distributed data stream processing

Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach

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