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Scheduling Storms and Streams in the Cloud
2015
Performance Evaluation Review
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Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. ...
When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. ...
s S4 [25] , Twitter's Storm [29] , IBM's InfoSphere Stream [15] , TimeStream [22] , D-Stream [32] , and online MapReduce [6] . ...
doi:10.1145/2796314.2745882
fatcat:cmkjnpcmf5cuzkxnsm3nvnupyy
Scheduling Storms and Streams in the Cloud
2016
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
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Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. ...
When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. ...
s S4 [25] , Twitter's Storm [29] , IBM's InfoSphere Stream [15] , TimeStream [22] , D-Stream [32] , and online MapReduce [6] . ...
doi:10.1145/2904080
fatcat:fzmtjczh5fhclc7cj3yax2mxiu
Scheduling Storms and Streams in the Cloud
2015
Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems - SIGMETRICS '15
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Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. ...
When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. ...
s S4 [25] , Twitter's Storm [29] , IBM's InfoSphere Stream [15] , TimeStream [22] , D-Stream [32] , and online MapReduce [6] . ...
doi:10.1145/2745844.2745882
dblp:conf/sigmetrics/GhaderiSS15
fatcat:kn5rxxdit5dhnok2wxjcgbovoa
Scheduling Storms and Streams in the Cloud
[article]
2015
arXiv
pre-print
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Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. ...
When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. ...
s S4 [25] , Twitter's Storm [29] , IBM's InfoSphere Stream [15] , TimeStream [22] , D-Stream [32] , and online MapReduce [6] . ...
arXiv:1502.05968v1
fatcat:uvooer6gzbg3vksahp4qnar6iq
Stream Processing in Community Network Clouds
2015
2015 3rd International Conference on Future Internet of Things and Cloud
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ACKNOWLEDGMENT This research is supported by the CLOMMUNITY project funded by the European Commission under FP7 Grant Agreement 317879; the End-to-End Clouds project funded by the Swedish Foundation for ...
Strategic Research under the contract RIT10-0043; the ICT-TNG SRA initiative at KTH. ...
It is an open problem to make the Storm scheduler and stream groupings aware of the network topology, in order to provide optimal placement of spouts and bolts in the community network so that the traffic ...
doi:10.1109/ficloud.2015.95
dblp:conf/ficloud/DanniswaraSAV15
fatcat:k6nxpogdnrfjtelyo24mokwdli
A Framework for Data Stream Applications in a Distributed Cloud
2016
Central-European Workshop on Services and their Composition
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The ever increasing diffusion of sensing and computing devices enables a new generation of data stream processing (DSP) applications that operate in a distributed Cloud environment. ...
In this paper we present our extension of Storm, which provides distributed monitoring, scheduling and management capabilities. ...
Thanks to the anonymous reviewers for the valuable comments, to Valeria Cardellini, and to Gabriele Scolastri for the implementation of the Rizou's algorithm. ...
dblp:conf/zeus/Nardelli16
fatcat:gxurej2ayfgwdglfk5pxt6ogny
EdgeWise: A Better Stream Processing Engine for the Edge
2019
USENIX Annual Technical Conference
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In our single-node and distributed experiments we compare EDGEWISE to the state-of-the-art Storm system. We report up to a 3x improvement in throughput while keeping latency low. ...
However, existing Stream Processing Engines (SPEs) are unsuited for the Edge because their designs assume Cloud-class resources and relatively generous throughput and latency constraints. ...
This work was supported in part by the National Science Foundation, under grant CNS-1814430. ...
dblp:conf/usenix/FuGDL19
fatcat:nylltvy4jvb2hp7s4al2mbncw4
When FPGA-Accelerator Meets Stream Data Processing in the Edge
2019
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
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Previous efforts have focused on introducing light-weight distributed stream processing (DSP) systems and dividing the computation between Edge servers and the clouds. ...
Our experiments show that compared to Storm, F-Storm reduces the latency by 36% and 75% for matrix multiplication and grep application. ...
This led to the proliferation of Distributed Stream Processing (DSP) systems such as Storm [2] , Flink [3] , and Spark Streaming [4] in data centers and clouds to perform online processing of these ...
doi:10.1109/icdcs.2019.00180
dblp:conf/icdcs/0001HI0XCL19
fatcat:nzgqzjm3drdvhk6sydr26v5zbm
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
[article]
2015
arXiv
pre-print
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Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. ...
In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. ...
This is a term used by Storm, and it has the same meaning as re-scheduling. ...
arXiv:1501.03610v3
fatcat:wilbz5pc3jdftihobw2b4j4nia
Experimental Study on the Performance and Resource Utilization of Data Streaming Frameworks
2018
2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
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The goal of the work is to enlighten the cloud-clients and the cloud-providers with the knowledge of the choice of the resource-efficient and requirement-adaptive streaming platform for a given application ...
Based on the potential to process both streams and batches in real-time and the popularity of usage, in this work, we have chosen Apache Flink [5] , Apache Storm [6] , and Twitter Heron [7] as the ...
doi:10.1109/ccgrid.2018.00029
dblp:conf/ccgrid/ChatterjeeM18
fatcat:3uvm4l2jhjg3hjna2t4ygm7vc4
Cross-Layer Scheduling in Cloud Systems
2015
2015 IEEE International Conference on Cloud Engineering
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In such cloud stacks, the scheduler of the application engine (which allocates tasks to servers) remains decoupled from the SDN scheduler (which allocates network routes). ...
This coordinated scheduling orchestrates the placement of application tasks (e.g., Hadoop maps and reduces, or Storm bolts) in tandem with the selection of network routes that arise from these tasks. ...
In this paper, we explore a cross-layer approach to scheduling in such cloud stacks. ...
doi:10.1109/ic2e.2015.36
dblp:conf/ic2e/AlkaffGL15
fatcat:xs3cuftt4vdbfer56fbpsoxi5e
Distributed Data Stream Processing and Edge Computing: A Survey on Resource Elasticity and Future Directions
[article]
2017
arXiv
pre-print
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This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. ...
This work examines some of these challenges and discusses solutions proposed in the literature to address them. ...
This work has been carried out in the scope of a joint project between the French National Center for Scientific Research (CNRS) and the University of Melbourne. ...
arXiv:1709.01363v2
fatcat:ajven75pjrgqhkpmi2d3pxs5pu
Distributed data stream processing and edge computing: A survey on resource elasticity and future directions
2018
Journal of Network and Computer Applications
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This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. ...
This work examines some of these challenges and discusses solutions proposed in the literature to address them. ...
This work has been carried out in the scope of a joint project between the French National Center for Scientific Research (CNRS) and the University of Melbourne. ...
doi:10.1016/j.jnca.2017.12.001
fatcat:twmpqzkb3nco3a7nwyhloe5qvu
Live Big Data Analytics Resource Management Techniques in Fog Computing for TeleHealth Applications
2021
Jordanian Journal of Computers and Information Technology
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Conducting these tele-health applications over the traditional cloud violates the deadline constrains of the stream analytics applications, which results not only in performance degradation, but also in ...
In addition, Differentiated S-FARM scheduler is proposed to support per-user control to the analytic results' accuracy and speed. ...
to represent the FARM platform based on YARN for compatible stream/batch analytics in the fog/cloud system. ...
doi:10.5455/jjcit.71-1605864596
fatcat:hnrgfkjttraefkkmyfkk2x567q
Application of Workflow Technology for Big Data Analysis Service
2018
Applied Sciences
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This study elucidates the architecture and application modeling, customization, dynamic construction, and scheduling of a cloud workflow system. ...
Users can rend cloud capabilities and customize a set of big data analysis applications in the form of workflow processes. ...
For example, Yahoo introduced the S4 distributed stream computing system, and Twitter developed the Storm streaming computing system. ...
doi:10.3390/app8040591
fatcat:hlf7ekmcgjahll6ewrt3t5ivy4
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