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When Two Choices Are not Enough: Balancing at Scale in Distributed Stream Processing
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-5872-7809
2016 (English)In: 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2016, p. 589-600Conference paper, Published paper (Refereed)
Abstract [en]

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: having 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 heavy hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d >= 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 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.

Place, publisher, year, edition, pages
2016. p. 589-600
Series
IEEE International Conference on Data Engineering, ISSN 1084-4627
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-193258ISI: 000382554200050Scopus ID: 2-s2.0-84980322422ISBN: 978-1-5090-2020-1 (print)OAI: oai:DiVA.org:kth-193258DiVA, id: diva2:1033630
Conference
32nd IEEE International Conference on Data Engineering (ICDE), MAY 16-20, 2016, Helsinki, FINLAND
Note

QC 20161007

Available from: 2016-10-07 Created: 2016-09-30 Last updated: 2018-04-05Bibliographically approved
In thesis
1. Mining Big and Fast Data: Algorithms and Optimizations for Real-Time Data Processing
Open this publication in new window or tab >>Mining Big and Fast Data: Algorithms and Optimizations for Real-Time Data Processing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In the last decade, real-time data processing has attracted much attention from both academic community and industry, as the meaning of big data has evolved to incorporate as well the speed of data. The massive and rapid production of data comes via numerous services, i.e., Web, social networks, Internet of Things (IoT) and mobile devices. For instance, global positioning systems are producing continuous data points using various location-based services. IoT devices are continuously monitoring variety of parameters, like temperature, heart beats, and others, and sending the data over the network. Moreover, part of the data produced by these real-time services is linked-data that requires tools for streaming graph analytics. Real-time graphs are ubiquitous in many fields, from the web advertising to bio-analytics. Developing analytical tools to process this amount of information at a real-time is challenging, yet extremely essential, for developing new services in areas such as web analytics, e-health and marketing.

Distributed stream processing engines (dspes) are often employed for real-time data processing, as they distribute work to many machines to achieve the required performance guarantees, i.e., low latency and high throughput. However, the scalability of dspes is often questioned when the input streams are skewed or the underlying resources are heterogeneous. In this thesis, we perform a scalability study for dspes. In particular, we study the load- balancing problem for dspes, which is caused by the skewness in the workload and heterogeneity in the cluster. In doing so, we develop several efficient and accurate algorithms to reduce the load imbalance in a distributed system. Moreover, our algorithms are integrated into Apache Storm, which is an open source stream processing framework.

Another dimension of real-time data processing involves developing novel algorithms for graph-related problems. The later part of the thesis presents several algorithms for evolving graphs. One of the most interesting features of real-world networks is the presence of community structure, which divides a network into groups of nodes with dense connections internally and sparse connections between groups. We study the community detection problem in the fully dynamic settings by formulating it as a top-k densest subgraph problem. In doing so, we achieve an extremely efficient approximation algorithm that scales to graphs with billions of edges. Further, we study the top-k graph pattern-mining problem in fully dynamic settings and develop a probabilistic algorithm using reservoir sampling. We provide the theoretical analysis for the proposed algorithms and show via empirical evaluation that our algorithms achieve up to several orders of magnitude improvement compared to the state-of-the-art algorithm.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 42
Series
TRITA-EECS-AVL ; 2018:27
Keywords
Stream Processing, Load Balancing, Fully Dynamic Graphs, Real-Time Data Processing, Top-k Densest Subgraph, Frequent Subgraph Mining
National Category
Computer Systems
Research subject
Information and Communication Technology; Computer Science
Identifiers
urn:nbn:se:kth:diva-225487 (URN)978-91-7729-729-1 (ISBN)
Public defence
2018-05-07, Sal B, Electrum building, Kistagången 16, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-05 Last updated: 2018-04-10Bibliographically approved

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