The core of cloud computing in my understanding

本文探讨了云计算核心基础设施,如云服务器集群、存储、数据库及消息队列,并强调了无状态应用的重要性及其如何通过微服务架构实现良好的扩展性。此外还讨论了容器技术如 Docker 的标准化部署与隔离优势,数据库的分片策略以及多主事务处理机制。文章最后提到了云计算对于大数据和 AI 的支持。

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The core of cloud computing is the infrastructure. Cloud server farms, cloud storage, cloud database and message queues. And then, for traditional applications to run on the cloud, they should be implemented stateless (all data, including session data, should be stored in the database backend, rather than in the application memory; the user should be able to switch between different application instances from request to request without leading into any incorrect results). Better, they are implemented in the form of microservices, making it easier to scale. For IT professionals to easily manage a lot of services and microservices, they should use containers, such as Docker, which is standardized in regards of deployment, and safeguarded by isolation. Database tables should be sharded to gain high scalability. Database management systems should support multimaster transaction (rather than the less scalable master-slave model), while in most of the time they perform in READ COMMITTED transaction isolation level (rather than the slow but secure SERIALIZABLE isolation level, which is important for certain transactions such as purchasing) for most applications. A paxos model for cloud management services should be used to avoid single-point failures.
 
On the other hand, big data and AI can also benefit from cloud computing. However, sometimes high capacity computers (high memory capacity and many cores) are still needed.
 
So to our application developers, what we concern most is stateless applications and database transaction control, plus HTTP requests/ SOAP/ AJAX to external services.

### Ollama Key in IT Context In the realm of information technology (IT), an ollama key does not directly correspond to any widely recognized standard term or concept within mainstream literature on IT infrastructure, software development practices, cybersecurity measures, database management systems, cloud computing services, networking protocols, or other core areas of focus in this field. However, considering potential typographical errors or specific niche applications: If referring to a similar-sounding term like "olla" keys which might be mistaken for "olima," it still lacks relevance in established IT contexts. Given the provided references about massive multitask language understanding[^1], one could speculate that if there were indeed an entity named 'ollama' involved in advanced computational linguistics research projects, its associated 'key' may denote critical parameters used during model training processes or unique identifiers utilized by APIs facilitating access to such models. Yet, no direct evidence supports this hypothesis based on current knowledge bases available up until my last update cycle. Regarding message brokering as described where a system manages distribution of messages ensuring reliable delivery mechanisms including handling failures gracefully through topic subscriptions[^2], integrating hypothetical 'ollama key' elements would imply specialized encryption/decryption routines applied at various stages of communication pipelines securing data integrity while maintaining confidentiality between publishers and consumers within distributed architectures. ```python # Hypothetical Python code snippet demonstrating secure messaging with an assumed 'ollama_key' import hashlib def generate_ollama_secure_hash(message, ollama_key): hasher = hashlib.sha256() combined_input = f"{message}:{ollama_key}".encode('utf-8') hasher.update(combined_input) return hasher.hexdigest() secure_message_id = generate_ollama_secure_hash("example_payload", "hypothetical_ollama_key_value") print(f"Secure Hashed ID: {secure_message_id}") ``` --related questions-- 1. What are common cryptographic methods employed in modern messaging platforms? 2. How do API keys function in web service authentication schemes? 3. Can you provide examples of terms easily confused due to phonetic similarities in technical documentation? 4. In what ways can machine learning models benefit from robust security features when deployed across networks?
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