#IstioCon Performance tuning and best practices in a Knative based, large-scale serverless platform with Istio 张龚, Gong Zhang, IBM China Development Lab 庄宇, Yu Zhuang, IBM China Development Lab #IstioCon Cloud. Working on IBM Cloud Code Engine (Serverless platform), focusing on Knative, Istio, and Tekton, community, leading team to develop and offer serverless capabilities in IBM Cloud, which based on (Serving and Eventing) that introduce event-driven and serverless capabilities for Kubernetes clusters for deploying, running, and managing serverless, cloud- native applications. It provides benefits:

service credentials and config files (usually application_default_credentials.json in AppData) Google Compute Engine Using default credentials is essentially the simplest way to integrate with various SSO providers The integration in DBeaver supports distinctive BigQuery functionalities, accommodating its serverless infrastructure, robust scalability, and compatibility with multi-cloud data analytics. Before listener port 443 (default) Database* Database name SNOWFLAKE_SAMPLE_DATA Warehouse* Cluster of compute resources in Snowflake. Warehouses are required for queries, as well as all DML operations, including

service credentials and config files (usually application_default_credentials.json in AppData) Google Compute Engine Using default credentials is essentially the simplest way to integrate with various SSO providers The integration in DBeaver supports distinctive BigQuery functionalities, accommodating its serverless infrastructure, robust scalability, and compatibility with multi-cloud data analytics. Before listener port 443 (default) Database* Database name SNOWFLAKE_SAMPLE_DATA Warehouse* Cluster of compute resources in Snowflake. Warehouses are required for queries, as well as all DML operations, including

The integration in DBeaver supports distinctive BigQuery functionalities, accommodating its serverless infrastructure, robust scalability, and compatibility with multi-cloud data analytics. Before listener port 443 (default) Database* Database name SNOWFLAKE_SAMPLE_DATA Warehouse* Cluster of compute resources in Snowflake. Warehouses are required for queries, as well as all DML operations, including

increment_type(weights); // 3 population(incre_type)++; if (decre_type != -1) population(decre_type)--; } 1. Compute the weights matrix. 2. Determine the arrival time by sampling an exponential distribution. 3. Normalize get the probabilities for possible events. Sample a random variable to decide which event happens.Compute the weights matrix 11 void TumorGenerator::evolve_step() { Eigen::ArrayXXd weights = transition_rates Policy call .get() to obtain population arrays.Parallel STL algorithm (average population) 15 // compute average population Eigen::ArrayXXd initial_array = Eigen::ArrayXXd::Zero(population_result[0].rows()

modern rollouts for VM services ● Security ○ Enforce the same policies in the same way, across compute environments ● Observability ○ See VM metrics alongside containers ● Extensibility #IstioCon

● Rename Handling ? ○ Use unique Object IDs to track renames ● Store the Computed Snapdiffs ● Compute Snapdiffs using existing Snapdiffs ○ Diff(snap1,snap3) = Merge{Diff(snap1, snap2), Diff(snap2, snap3)}

arbitrary application-supplied column names to store data. A dynamic column family allows you to pre-compute result sets and store them in a single row for efficient data retrieval. Each row is a snapshot of

box, select Primary Partition. 6. Next, specify the file system. Select ntfs. 7. Click Add to compute the partition. The graphical display updates to show a new partition on the disk. 8. If you are