{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:37:22Z","timestamp":1755693442127,"version":"3.41.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Ministry for Education and Research as BIFOLD - Berlin Institute for the Foundations of Learning and Data","award":["01IS18025A and 01IS18037A"],"award-info":[{"award-number":["01IS18025A and 01IS18037A"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            Geographically distributed (geo-distributed) datacenters for stream data processing typically comprise multiple edges and core datacenters connected through\n            <jats:bold>Wide-Area Network (WAN)<\/jats:bold>\n            with a master node responsible for allocating tasks to worker nodes. Since WAN links significantly impact the performance of distributed task execution, the existing task assignment approach is unsuitable for distributed stream data processing with low latency and high throughput demand. In this paper, we propose SAFA, a resource provisioning framework using the\n            <jats:bold>Software-Defined Networking (SDN)<\/jats:bold>\n            concept with an SDN controller responsible for monitoring the WAN, selecting an appropriate subset of worker nodes, and assigning tasks to the designated worker nodes. We implemented the data plane of the framework in P4 and the control plane components in Python. We tested the performance of the proposed system on Apache Spark, Apache Storm, and Apache Flink using the Yahoo! streaming benchmark on a set of custom topologies. The results of the experiments validate that the proposed approach is viable for distributed stream processing and confirm that it can improve at least 1.64\u00d7 the processing time of incoming events of the current stream processing systems.\n          <\/jats:p>","DOI":"10.1145\/3571158","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T09:57:07Z","timestamp":1668074227000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SDN-enabled Resource Provisioning Framework for Geo-Distributed Streaming Analytics"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8282-1571","authenticated-orcid":false,"given":"Habib","family":"Mostafaei","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6935-530X","authenticated-orcid":false,"given":"Shafi","family":"Afridi","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Berlin, Berlin, Germany"}]}],"member":"320","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Akamai\u2019s [state of the internet]: Q1 2017 report. 2017. https:\/\/www.bit.ly\/3jPSKEP."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2927062"},{"key":"e_1_3_2_4_2","unstructured":"Amazon EC2 On-Demand Pricing. 2020. https:\/\/aws.amazon.com\/ec2\/pricing\/on-demand\/."},{"key":"e_1_3_2_5_2","unstructured":"Apache Flink. 2021. https:\/\/flink.apache.org\/."},{"key":"e_1_3_2_6_2","unstructured":"Apache Hadoop YARN. 2021. https:\/\/hadoop.apache.org\/docs\/stable\/hadoop-yarn\/hadoop-yarn-site\/YARN.html."},{"key":"e_1_3_2_7_2","unstructured":"Apache Kafka. 2021. https:\/\/kafka.apache.org\/."},{"key":"e_1_3_2_8_2","unstructured":"Apache Mesos. 2021. https:\/\/mesos.apache.org\/."},{"key":"e_1_3_2_9_2","unstructured":"Apache Spark. 2021. https:\/\/spark.apache.org\/."},{"key":"e_1_3_2_10_2","unstructured":"Apache Storm. 2021. https:\/\/storm.apache.org\/."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2656877.2656890"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3092819.3092823"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2012.40"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.138"},{"key":"e_1_3_2_15_2","unstructured":"Extending the Yahoo Streaming Benchmarks. 2020. https:\/\/github.com\/dataArtisans\/yahoo-streaming-benchmark."},{"key":"e_1_3_2_16_2","unstructured":"Google cloud: pricing. 2020. https:\/\/cloud.google.com\/pubsub\/pricing."},{"key":"e_1_3_2_17_2","unstructured":"Google Datacenters. 2021. https:\/\/about.google\/locations\/."},{"key":"e_1_3_2_18_2","first-page":"1","article-title":"Optimizing timeliness and cost in geo-distributed streaming analytics","author":"Heintz B.","year":"2017","unstructured":"B. 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