Choosing the right high availability strategy

This document discusses load balancing in cloud computing. It begins by defining cloud computing and some of its key characteristics like broad network access, rapid elasticity, and pay-as-you-go pricing. It then discusses how load balancing can improve performance in distributed cloud environments by redistributing load, improving response times, and better utilizing resources. The document outlines different load balancing techniques like virtual machine migration and throttled load balancing using a load balancer, virtual machines, and a data center controller. It also proposes a trust and reliability based algorithm that prioritizes data centers for load balancing based on calculated trust values that consider factors like initialization time, machine performance, and fault rates.

Resource and process management approaches include task assignment, load balancing, and load sharing. Task assignment involves assigning tasks to suitable nodes. Load balancing distributes processes to balance load across nodes. Load sharing equitably distributes processes so no node remains idle. Good scheduling considers factors like dynamic decision-making, balanced performance/overhead, fairness, and scalability. Process migration moves processes between nodes for load balancing. Issues include freezing processes during transfer, address space transfer mechanisms, and maintaining communication between related processes. Threads allow finer-grained parallelism and resource sharing within a process. They present challenges for synchronization, scheduling, and signal handling.

Presented at Interconnect in Feb 2015 This session discusses how best to design MQ systems for Disaster Recovery.

Server load balancing (SLB) distributes network traffic across multiple servers to optimize resource utilization and maximize throughput. It intercepts traffic destined for a website and redirects requests to various backend servers using techniques like network address translation. SLB aims to improve performance, increase scalability, and maintain high availability by monitoring servers and routing traffic around failures to keep applications running if servers go down. Both hardware and software-based solutions exist, with hardware providing higher performance but at greater cost than software-based options.

The document discusses cloud providers and services available on Amazon Web Services. It provides an overview of compute, storage, database, and other services and how they can provide redundancy across availability zones and regions. Examples are given of different outage scenarios that can occur at the zone, region, or provider level and strategies for architecting applications to mitigate risks from these outages.

The document summarizes 24/7 Customer's experience migrating from Apache Kafka 0.7 and 0.8 to the newer 0.10.0.1 version. It describes the challenges faced with sticky partitions and range-based mirror makers in 0.8. It details 24/7 Customer's upgrade path from 0.8 to 0.8.2.2 to 0.9 to the current 0.10.0.1 version. It also discusses the configurations, monitoring, and design considerations for running Kafka reliably across multiple data centers.

The document discusses various topics related to the Tungsten Connector including: - The role of the Connector in routing connections to the appropriate database nodes. - Best practices for deploying Connectors in different topologies including on application servers, dedicated nodes, or database nodes with load balancing. - How to perform zero-downtime maintenance on a Tungsten cluster by manually switching the master role between nodes using the cctrl utility. - How Connectors route connections in a composite cluster with multiple local clusters and how affinity can be set to prefer local reads from a particular cluster. - That a Connector can provide access to multiple clusters or composite clusters by configuring the dat

slides are about load balancing as a concept and implementation of load balancing on computer technical level slides show the server load balancing different architectures , algorithms and examples

1) The document discusses Time-Sensitive Linux (TSL), which aims to support time-sensitive applications on commodity operating systems like Linux. 2) TSL improves kernel latency through an accurate timer mechanism called firm timers, a responsive kernel using lock-breaking preemption, and effective scheduling using proportion-based and priority-based algorithms. 3) Evaluation shows TSL reduces timer latency to under 1ms and preemption latency to under 1ms, improving synchronization of media playback under load compared to standard Linux.

This document discusses high availability and disaster recovery strategies for IBM MQ. It introduces concepts like multi-instance queue managers, HA clusters, and MQ appliance HA groups that provide redundancy and failover capabilities. Automatic client reconnection is also covered, which allows MQ clients to seamlessly reconnect after a queue manager failure.

Concurrency and state management are important considerations for achieving elasticity in cloud systems. There are three types of state: session state kept by clients, server-side state kept in processes, and persistent state stored externally. Server-side state makes scaling difficult, while stateless servers allow elasticity. Memcached provides a way to synchronize small amounts of in-memory state across servers to support stateless services running elastically in the cloud.

Presented by Mark Addy - Senior Middleware Consultant at C2B2 Presented at JBUG London and JUDCon Boston in June 2012

The key needs of SaaS vendors include: i) managing multi-tenant architectures with shared DBMS, ii) maintaining customer SLAs for uptime and performance and iii) optimized, efficient operations. The key benefits Continuent Tungsten offers SaaS vendors are: i) high availability and protection from data loss, ii) simple, efficient cluster management and iii) enable complex database topologies. Tungsten offers high-availability, database cluster management and management of complex topologies for multi-tenant architectures. Tungsten high availability and data protection features include maintaining live copies with data consistency checking and tightly coupled backup/restore integration with cluster management tools. Tungsten cluster management allows SaaS vendors to migrate customers and perform system upgrades without downtime, thus enabling these maintenance operations during normal business hours. Tungsten also enables complex replication topologies, including data filtering and data archiving strategies, maintaining extra data copies for data-marts, routing different customers to different DBMS copies, and providing cross-site multi-master replication.

This document provides an overview of Apache Kafka. It describes Kafka as a distributed publish-subscribe messaging system with a distributed commit log that provides high-throughput and low-latency processing of streaming data. The document covers Kafka concepts like topics, partitions, producers, consumers, replication, and reliability guarantees. It also discusses Kafka architecture, performance optimizations, configuration parameters for durability and reliability, and use cases for activity tracking, messaging, metrics, and stream processing.

The MQ Appliance includes two key facilities for maintaining the availability of your messaging infrastructure across expected and unexpected outages. This session looks in depth at the HA and DR capabilities of the MQ Appliance and application considerations when using them.

Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.

IBM Messaging provides market-leading capabilities for anywhere-to-anywhere integration across mobile, cloud, and enterprise platforms - from the simplest pair of applications requiring basic connectivity and data exchange, to the most complex business process management environments. Come to this session to understand the value and rationale of message/queuing and the IBM Messaging family of products; its key features and functions; and how it can be used to build a secure, flexible, and scalable messaging backbone for a business.

- MariaDB provides several high availability options including asynchronous replication, semi-synchronous replication, Galera synchronous replication, and MaxScale for load balancing and failover. - Asynchronous replication allows for read scaling but carries a risk of data loss during failover. Semi-synchronous replication reduces this risk by ensuring data is written to at least one slave before confirming to the client. - Galera synchronous multi-master replication ensures all nodes remain in sync with no data loss but can impact performance. MaxScale helps manage replication topology and perform automated failovers.

This document discusses high availability and MariaDB replication. It defines high availability and outlines key components like data redundancy, failover solutions, and monitoring. It then describes MariaDB replication in detail, covering asynchronous and semi-synchronous replication as well as Galera cluster synchronous replication. MaxScale is introduced as a tool for load balancing, monitoring, and facilitating failovers in MariaDB replication topologies.

Learn strategies to maintain your database's high availability even during peak use periods. MariaDB's Field CTO Max Mether offers best practices for high availability, disaster recovery and more.

This document discusses different approaches to achieving high availability with MariaDB databases, including replication, Galera clusters, and MaxScale load balancing. It describes asynchronous and semi-synchronous replication topologies that provide redundancy and enable failover. Synchronous replication with Galera clusters is discussed as another option that ensures all nodes remain in sync with no data loss. MaxScale is presented as a tool that can monitor MariaDB topologies and perform load balancing and failover across nodes.