Merge pull request #54373 from MicrosoftDocs/main

Auto Publish – main to live - 2026-04-22 23:00 UTC

Author: learn-build-service-prod[bot]

.openpublishing.redirection.json

@@ -33,12 +33,12 @@ The combiner function is best illustrated through an example. Assume that a map
 2. _M. Hammoud and M. F. Sakr (2011). [Locality-Aware Reduce Task Scheduling for MapReduce](https://web2.qatar.cmu.edu/~mhhammou/mhh_msakr_LARTS_CloudCom2011_paper.pdf) CloudCom_
 3. _[HDFS Architecture Guide](http://hadoop.apache.org/docs/r1.0.4/hdfs_design.html) Hadoop_
 4. _M. Hammoud, M. S. Rehman, and M. F. Sakr (2012). [Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic](https://dl.acm.org/doi/10.1109/CLOUD.2012.92) CLOUD_
-5. _J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, and H. Pirahesh (1997). [Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tab, and Sub-totals](https://arxiv.org/ftp/cs/papers/0701/0701155.pdf) Data Mining and Knowledge Discovery_
+5. _J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, and H. Pirahesh (1997). [Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tab, and Sub-totals](https://arxiv.org/pdf/cs/0701155.pdf) Data Mining and Knowledge Discovery_
 
 ***
 
 [^1]: <https://www.researchgate.net/publication/221276678_LEEN_LocalityFairness-Aware_Key_Partitioning_for_MapReduce_in_the_Cloud> "S. Ibrahim, H. Jin, L. Lu, S. Wu, B. He, and L. Qi (Dec. 2010). *LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud* CloudComm"
 [^2]: <https://web2.qatar.cmu.edu/~mhhammou/mhh_msakr_LARTS_CloudCom2011_paper.pdf> "M. Hammoud and M. F. Sakr (2011). *Locality-Aware Reduce Task Scheduling for MapReduce* CloudCom"
 [^3]: <http://hadoop.apache.org/docs/r1.0.4/hdfs_design.html> "*HDFS Architecture Guide* Hadoop"
 [^4]: <https://dl.acm.org/doi/10.1109/CLOUD.2012.92> "M. Hammoud, M. S. Rehman, and M. F. Sakr (2012). *Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic* CLOUD"
-[^5]: <https://arxiv.org/ftp/cs/papers/0701/0701155.pdf> "J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, and H. Pirahesh (1997). *Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tab, and Sub-totals* Data Mining and Knowledge Discovery"
+[^5]: <https://arxiv.org/pdf/cs/0701155.pdf> "J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, and H. Pirahesh (1997). *Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tab, and Sub-totals* Data Mining and Knowledge Discovery"

learn-pr/achievements/surface-portal-service-support-badge-social.png

@@ -55,9 +55,9 @@ _Figure 7: Options for scheduling a reduce task, $R$, with feeding nodes TT1 and
 1. _S. Ibrahim, H. Jin, L. Lu, S. Wu, B. He, and L. Qi (Dec. 2010). [LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud](https://www.researchgate.net/publication/221276678_LEEN_LocalityFairness-Aware_Key_Partitioning_for_MapReduce_in_the_Cloud) CloudCom_
 2. _M. Hammoud and M. F. Sakr (2011). [Locality-Aware Reduce Task Scheduling for MapReduce](https://web2.qatar.cmu.edu/~mhhammou/mhh_msakr_LARTS_CloudCom2011_paper.pdf) CloudCom_
 3. _M. Hammoud, M. S. Rehman, and M. F. Sakr (2012). [Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic](https://dl.acm.org/doi/10.1109/CLOUD.2012.92) CLOUD_
-4. _[Hadoop scheduling](https://developer.ibm.com/articles/os-hadoop-scheduling/) IBM_
+4. _[Hadoop scheduling](https://www.ibm.com/docs/en/spectrum-symphony/7.3.2?topic=distribution-resource-scheduling-plans)     IBM_
 5. _[Hadoop fair scheduler](https://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html) Hadoop_
-6. _B. Thirumala Rao and L. S. S. Reddy (November 2011). [Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments](https://arxiv.org/ftp/arxiv/papers/1207/1207.0780.pdf) International Journal of Computer Applications_
+6. _B. Thirumala Rao and L. S. S. Reddy (November 2011). [Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments](https://arxiv.org/abs/1207.0780) International Journal of Computer Applications_
 7. _M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica (April 2010). [Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling](https://dl.acm.org/doi/10.1145/1755913.1755940) EuroSys, pp. 265-278_
 8. _[Mahout Homepage](https://mahout.apache.org/) Apache Mahout_
 9. _S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang (2010). [The HiBench Benchmark Suite: Characterization of the MapReduce-Based Data Analysis](https://ieeexplore.ieee.org/document/5452747) ICDEW_
@@ -71,9 +71,9 @@ _Figure 7: Options for scheduling a reduce task, $R$, with feeding nodes TT1 and
 [^1]: <https://www.researchgate.net/publication/221276678_LEEN_LocalityFairness-Aware_Key_Partitioning_for_MapReduce_in_the_Cloud> "S. Ibrahim, H. Jin, L. Lu, S. Wu, B. He, and L. Qi (Dec. 2010). *LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud* CloudCom"
 [^2]: <https://web2.qatar.cmu.edu/~mhhammou/mhh_msakr_LARTS_CloudCom2011_paper.pdf> "M. Hammoud and M. F. Sakr (2011). *Locality-Aware Reduce Task Scheduling for MapReduce* CloudCom"
 [^3]: <https://dl.acm.org/doi/10.1109/CLOUD.2012.92> "M. Hammoud, M. S. Rehman, and M. F. Sakr (2012). *Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic* CLOUD"
-[^4]: <https://developer.ibm.com/articles/os-hadoop-scheduling/> "*Hadoop scheduling* IBM"
+[^4]: <https://www.ibm.com/docs/en/spectrum-symphony/7.3.2?topic=distribution-resource-scheduling-plans> "*Hadoop scheduling* IBM"
 [^5]: <https://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html> "*Hadoop fair scheduler* Apache Hadoop"
-[^6]: <https://arxiv.org/ftp/arxiv/papers/1207/1207.0780.pdf> "B. Thirumala Rao and L. S. S. Reddy (November 2011). *Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments* International Journal of Computer Applications"
+[^6]: <https://arxiv.org/abs/1207.0780> "B. Thirumala Rao and L. S. S. Reddy (November 2011). *Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments* International Journal of Computer Applications"
 [^7]: <https://dl.acm.org/doi/10.1145/1755913.1755940> "M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica (April 2010). *Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling* EuroSys, pp. 265-278"
 [^8]: <https://mahout.apache.org/> "*Mahout Homepage* Apache Mahout"
 [^9]: <https://ieeexplore.ieee.org/document/5452747> "S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang (2010). *The HiBench Benchmark Suite: Characterization of the MapReduce-Based Data Analysis* ICDEW"

learn-pr/achievements/surface-portal-service-support-badge.svg

@@ -35,10 +35,10 @@ Clearly, Hadoop MapReduce's standard speculative execution approach suffers from
 
 1. _M. Zaharia, A. Konwinski, A. Joseph, R. Katz, and I. Stoica (2008). [Improving MapReduce Performance in Heterogeneous Environments](https://dl.acm.org/doi/10.5555/1855741.1855744) OSDI_
 2. _T. White (2011). [Hadoop: The Definitive Guide](http://shop.oreilly.com/product/0636920010388.do) 2nd Edition O'Reilly_
-3. _Z. Guo and G. Fox (2012). [Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization](https://citeseerx.ist.psu.edu/doc_view/pid/e8416af240aca62a4a7b9ffa3589a4e93576981f) In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing_
+3. _Z. Guo and G. Fox (2012). [Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization](https://ranger.uta.edu/~jrao/papers/Middleware14.pdf) In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing_
 
 ***
 
 [^1]: <https://dl.acm.org/doi/10.5555/1855741.1855744> "M. Zaharia, A. Konwinski, A. Joseph, R. Katz, and I. Stoica (2008). *Improving MapReduce Performance in Heterogeneous Environments* OSDI"
 [^2]: <http://shop.oreilly.com/product/0636920010388.do> "T. White (2011). *Hadoop: The Definitive Guide* 2nd Edition O'Reilly"
-[^3]: <https://citeseerx.ist.psu.edu/doc_view/pid/e8416af240aca62a4a7b9ffa3589a4e93576981f> "Z. Guo and G. Fox (2012). *Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization* In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing"
+[^3]: <https://ranger.uta.edu/~jrao/papers/Middleware14.pdf> "Z. Guo and G. Fox (2012). *Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization* In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing"

learn-pr/cmu-cloud-developer/cmu-analytics-engines-mapreduce/includes/2-data-structure-flow.md

@@ -129,11 +129,11 @@ Another feature that is useful in Cassandra is that it has out-of-the-box suppor
 ### References
 
 1. _Merkle, R. (1988). [A digital signature based on a conventional encryption function](https://people.eecs.berkeley.edu/~raluca/cs261-f15/readings/merkle.pdf) Proceedings of CRYPTO (pp. 369–378). Springer-Verlag_
-2. _Hayashibara, Naohiro, et al. (2004). [The φ accrual failure detector](https://citeseerx.ist.psu.edu/pdf/11ae4c0c0d0c36dc177c1fff5eb84fa49aa3e1a8) Reliable Distributed Systems, Proceedings of the 23rd IEEE International Symposium on Reliable Distributed Systems_
+2. _Hayashibara, Naohiro, et al. (2004). [The φ accrual failure detector](https://www.researchgate.net/publication/29682135_The_ph_accrual_failure_detector) Reliable Distributed Systems, Proceedings of the 23rd IEEE International Symposium on Reliable Distributed Systems_
 3. _DeCandia, Giuseppe, et al. (2007). [Dynamo: Amazon's highly available key-value store](https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf) ACM SIGOPS Operating Systems Review. Vol. 41. No. 6 ACM_
 
 ***
 
 [^1]: <https://people.eecs.berkeley.edu/~raluca/cs261-f15/readings/merkle.pdf> "Merkle, R. (1988). *A digital signature based on a conventional encryption function* Proceedings of CRYPTO (pp. 369–378). Springer-Verlag"
-[^2]: <https://citeseerx.ist.psu.edu/pdf/11ae4c0c0d0c36dc177c1fff5eb84fa49aa3e1a8> "Hayashibara, Naohiro, et al. (2004). *The φ accrual failure detector* Reliable Distributed Systems, 2004. Proceedings of the 23rd IEEE International Symposium on Reliable Distributed Systems"
+[^2]: <https://www.researchgate.net/publication/29682135_The_ph_accrual_failure_detector> "Hayashibara, Naohiro, et al. (2004). *The φ accrual failure detector* Reliable Distributed Systems, 2004. Proceedings of the 23rd IEEE International Symposium on Reliable Distributed Systems"
 [^3]: <https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf> "DeCandia, Giuseppe, et al. (2007). *Dynamo: Amazon's highly available key-value store* ACM SIGOPS Operating Systems Review. Vol. 41. No. 6 ACM"

learn-pr/cmu-cloud-developer/cmu-analytics-engines-mapreduce/includes/5-job-task-schedule.md

@@ -86,7 +86,7 @@ Later in this module, we cover how the cloud evolved to make computing a public
 ### References
 
 1. _Real Time Statistics Project (2015). [Internet Live Stats](https://www.internetlivestats.com/)_
-2. _IBM (2017). [What is Big Data?](https://developer.ibm.com/dwblog/2017/what-is-big-data-insight/)_
+2. _IBM (2017). [What is Big Data?](https://www.ibm.com/think/topics/big-data)_
 4. _Google Inc. (2015). [How Search Works](https://www.google.com/insidesearch/howsearchworks/thestory/)_
 5. _Hilbert, Martin and Lopez, Priscila (2011). [The world's technological capacity to store, communicate, and compute information](https://science.sciencemag.org/content/332/6025/60)_
 6. _Hamilton, James R and others (2007). [On Designing and Deploying Internet-Scale Services](https://highscalability.com/paper-on-designing-and-deploying-internet-scale-services/)_
@@ -95,7 +95,7 @@ Later in this module, we cover how the cloud evolved to make computing a public
 ***
 
 [^1]: <https://www.internetlivestats.com/> "*Real Time Statistics Project* (2015)"
-[^2]: <https://developer.ibm.com/dwblog/2017/what-is-big-data-insight/> "IBM (2017). *What is Big Data?*"
+[^2]: <https://www.ibm.com/think/topics/big-data> "IBM (2017). *What is Big Data?*"
 [^4]: <https://www.google.com/insidesearch/howsearchworks/thestory/> "Google Inc. (2015). *How Search Works*"
 [^5]: <https://science.sciencemag.org/content/332/6025/60> "Hilbert, Martin and Lopez, Priscila (2011). *The world's technological capacity to store, communicate, and compute information*" 
 [^6]: <https://highscalability.com/paper-on-designing-and-deploying-internet-scale-services/> "Hamilton, James R and others (2007). *On Designing and Deploying Internet-Scale Services*" 

learn-pr/cmu-cloud-developer/cmu-analytics-engines-mapreduce/includes/6-fault-tolerance.md

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.dpu.surface-portal-service-support.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: This content is part of the "Creating service orders and support requests in Surface portals" module.
+  ms.date: 04/22/2026
+  author: ozahran7
+  ms.author: v-zahranomar
+  ms.topic: unit
+durationInMinutes: 1
+content: |
+  [!include[](includes/1-introduction.md)]

learn-pr/cmu-cloud-developer/cmu-case-study-nosql-databases/includes/3-apache-cassandra.md

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.dpu.surface-portal-service-support.create-service-order
+title: Create a Service Order
+metadata:
+  title: Create a Service Order
+  description: This content is part of the "Creating service orders and support requests in Surface portals" module.
+  ms.date: 04/22/2026
+  author: ozahran7
+  ms.author: v-zahranomar
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/2-create-service-order.md)]