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Optimal Operator Replication and Placement for Distributed Stream Processing Systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/bigdata.2015.7363773openalex-snapshot
A predictive scheduling framework for fast and distributed stream data processing
inferredcites →https://doi.org/10.1109/hpcsim.2016.7568388openalex-snapshot
Elastic stateful stream processing in storm
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T-Storm: Traffic-Aware Online Scheduling in Storm
inferredcites →https://doi.org/10.1109/icdcs.2015.48openalex-snapshot
Elastic Stream Processing with Latency Guarantees
inferredcites →https://doi.org/10.1109/icdcs.2015.49openalex-snapshot
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
inferredcites →https://doi.org/10.1109/icde.2006.105openalex-snapshot
Network-Aware Operator Placement for Stream-Processing Systems
inferredcites →https://doi.org/10.1109/icdew.2014.6818346openalex-snapshot
Automated operator placement in distributed Data Stream Management Systems subject to user constraints
inferredcites →https://doi.org/10.1109/infocom.2016.7524433openalex-snapshot
Task allocation for distributed stream processing
inferredcites →https://doi.org/10.1109/jiot.2016.2579198openalex-snapshot
Edge Computing: Vision and Challenges
inferredcites →https://doi.org/10.1109/tpds.2013.295openalex-snapshot
Elastic Scaling for Data Stream Processing
inferredcites →https://doi.org/10.1145/2488222.2488267openalex-snapshot
Adaptive online scheduling in storm
inferredcites →https://doi.org/10.1145/2611286.2611294openalex-snapshot
Latency-aware elastic scaling for distributed data stream processing systems
inferredcites →https://doi.org/10.1145/2611286.2611309openalex-snapshot
Cloud-based data stream processing
inferredcites →https://doi.org/10.1145/2611286.2611314openalex-snapshot
Auto-scaling techniques for elastic data stream processing
inferredcites →https://doi.org/10.1145/2661829.2661882openalex-snapshot
Fast Heuristics for Near-Optimal Task Allocation in Data Stream Processing over Clusters
inferredcites →https://doi.org/10.1145/2675743.2772598openalex-snapshot
The DEBS 2015 grand challenge
inferredcites →https://doi.org/10.1145/2675743.2776766openalex-snapshot
Distributed QoS-aware scheduling in storm
inferredcites →https://doi.org/10.1145/2806777.2806847openalex-snapshot
Online parameter optimization for elastic data stream processing
inferredcites →https://doi.org/10.1145/2903146openalex-snapshot
A Game-Theoretic Approach for Elastic Distributed Data Stream Processing
inferredcites →https://doi.org/10.1145/2933267.2933312openalex-snapshot
Optimal operator placement for distributed stream processing applications
inferredcites →https://doi.org/10.4108/eai.25-10-2016.2266628openalex-snapshot
Joint Operator Replication and Placement Optimization for Distributed Streaming Applications
inferredcites →https://doi.org/10.4135/9781412985161openalex-snapshot
Multiple Attribute Decision Making
inferredcites →https://doi.org/10.5167/uzh-82049openalex-snapshot
Scalable linked data stream processing via network-aware workload scheduling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Gt-Scheduler: A Hybrid Graph-Partitioning and Tabu-Search Based Scheduling of Distributed Data Stream Processing Operators
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Optimal operator deployment and replication for elastic distributed data stream processing
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AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms
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Operator Rebinding for Stream Processing on NUMA Machines
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Joint Operator Scaling and Placement for Distributed Stream Processing Applications in Edge Computing
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Towards a Security-Aware Deployment of Data Streaming Applications in Fog Computing
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Enabling Pinning Strategies for Stream Processing Applications on Multicores
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Multi-application operator placement in cloud–edge environments: a Deep Reinforcement Learning approach for big Data Stream Processing
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NebulaStream: Data Management for the Internet of Things
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SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters
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T3-Scheduler: A topology and Traffic aware two-level Scheduler for stream processing systems in a heterogeneous cluster
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Cost effective stream workflow scheduling to handle application structural changes
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I-Scheduler: Iterative scheduling for distributed stream processing systems
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A multi-domain cooperative scheduling framework for distributed stream computing systems
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TCEP: Transitions in operator placement to adapt to dynamic network environments
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Harnessing sliding-window execution semantics for parallel stream processing
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Joker: Elastic stream processing with organic adaptation
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Amnis: Optimized stream processing for edge computing
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L-Heron: An open-source load-aware online scheduler for Apache Heron
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Declarative continuous reasoning in the cloud-IoT continuum
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On the Cost of Acking in Data Stream Processing Systems
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Merge, Split, and Cluster: Dynamic Deployment of Stream Processing Applications
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Optimal Placement of Stream Processing Operators in the Fog
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Storm-RTS: Stream Processing with Stable Performance for Multi-Cloud and Cloud-edge
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To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing
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Performance-Aware Refactoring of Cloud-Based Big Data Applications
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Online Analytics with Local Operator Rebinding for Simulation Data Stream Processing
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Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures
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