XLOG is the centralized log service of the SQL Azure Hyperscale (Hyperscale) distributed database-as-a-service (DBaaS) within Microsoft Azure. It is responsible for disseminating transaction log to all clients within the distributed database, such as Page Servers and secondary Compute replicas. As the size of the Hyperscale database increases, the number of XLOG clients also increases, thus presenting scalability challenges in the request handling and IO subsystem. This paper describes how to address scalability challenges by applying various techniques. To solve the thread exhaustion problem, a coroutine-based asynchronous log processing framework was implemented, allowing better management of long-polling requests and eliminating thread starvation. The XLOG Pool, a centralized I/O management component, was introduced to reduce redundant I/Os and improve cold start performance by consolidating log reads across clients. A RateLimiter using a token bucket algorithm was introduced to prevent throttling by Azure storage. To ensure data integrity, several layers of validation, including egress and end-to-end checksum validation, were added to detect and prevent data corruption. Performance evaluations showed significant improvements in log processing and read rates with the new asynchronous framework, XLOG Pool and RateLimiter supporting larger databases more efficiently, enabling Hyperscale to support up to 128 TB size databases in production. This paper illustrates how a real-world cloud database service, responsible for hosting mission-critical applications and managing hundreds of petabytes of data, innovates its logging service to enhance scalability, reliability, and cost efficiency.

The value proposition for ground vehicle modularity in the U.S. Army and other services has been a topic of continuing debate. Studies to date have largely focused on individual system elements such as manufacturing or maintenance, lacking a holistic perspective of the implications of modularity for the entire fleet operation and life-cycle. The U.S. Army Science and Technology community has demonstrated the technical feasibility of large-scale, transformative ground vehicle modularity, but the business case for modularity remains incomplete. There are multiple criteria tradeoffs between modular and mission-specific (conventional) vehicle platforms, such as total life-cycle cost, mission utility, personnel requirements, and fleet adaptability. This paper presents a system-of-systems framework to address these tradeoffs to support high-level decisions on the strategic feasibility of ground vehicle modularity. We demonstrate this framework with a notional example and an application to the Joint Tactical Transport System (JTTS), a U.S. Army Tank Automotive Research, Development and Engineering Center demonstrator program. Under certain modeling assumptions with regards to the operation of a modular fleet, results for the JTTS study indicate that modularity can lead to significant cost savings at the expense of increased personnel requirements.

The database-as-a-service paradigm in the cloud (DBaaS) is becoming increasingly popular. Organizations adopt this paradigm because they expect higher security, higher availability, and lower and more flexible cost with high performance. It has become clear, however, that these expectations cannot be met in the cloud with the traditional, monolithic database architecture. This paper presents a novel DBaaS architecture, called Socrates. Socrates has been implemented in Microsoft SQL Server and is available in Azure as SQL DB Hyperscale. This paper describes the key ideas and features of Socrates, and it compares the performance of Socrates with the previous SQL DB offering in Azure.