We introduce SibylOpt, a system that applies deep reinforcement learning (DRL) to optimize the operation of a green data center (DC). SibylOpt uses an offline reinforcement learning algorithm, Advantage-Weighted Actor-Critic (AWAC), that requires historical data for training but does not depend on a DC simulator, which is effort intensive to build and keep updated as the DC evolves. SibylOpt then augments the offline training with online learning for refinement and adaptation to changes. We apply SibylOpt to the management of a small green DC that has onsite solar energy generation and a hybrid cooling system that includes "free-cooling." Evaluation results (using simulation) show that the offline trained SibylOpt achieves higher rewards trading off job wait time, cooling, and grid electricity consumption compared to two baseline policies. It is also competitive with PPO, a DRL approach that requires an accurate DC simulator for training.

The problem of hotspots remains a critical challenge in high-contention workloads for concurrency control (CC) protocols. Traditional concurrency control approaches encounter significant difficulties under high contention, resulting in excessive transaction aborts and deadlocks. In this paper, we propose Brook-2PL , a novel two-phase locking (2PL) protocol that (1) introduces SLW-Graph for deadlock-free transaction execution, and (2) proposes partial transaction chopping for early lock release. Previous methods suffer from transaction aborts that lead to wasted work and can further burden the system due to their cascading effects. Brook-2PL addresses this limitation by statically analyzing a new graph-based dependency structure called SLW-Graph , enabling deadlock-free two-phase locking through predetermined lock acquisition. Brook-2PL also reduces contention by enabling early lock release using partial transaction chopping and static transaction analysis. We overcome the inherent limitations of traditional transaction chopping by providing a more flexible chopping method. Evaluation using both our synthetic online game store workload and the TPC-C benchmark shows that Brook-2PL significantly outperforms state-of-the-art CC protocols. Brook-2PL achieves an average speed-up of (2.86x) while reducing tail latency (p95) by (48%) in the TPC-C benchmark.

Content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of deep reinforcement learning to tackle the changes in users' access patterns and automatically adapt over time to predict future requests. We propose DeePref, an auto-aggressive video prefetcher that is sensitive to different storage capacities at edge networks. DeePref is agnostic to hardware design, operating systems, and applications in which it utilizes only the video ID to make prefetching decisions online. DeePref outperforms baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. Our results show that DeePref, using a real-world dataset, achieves 17% increase in terms of prefetching accuracy and 28% increase in prefetching coverage. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. Our results indicate that DeePref effectively adapts to distribution shifts where the increase in prefetching accuracy and prefetching coverage are [30%, 10%], respectively.

Metastable failure is a recent abstraction of a pattern of failures that occurs frequently in real-world distributed storage systems. In this paper, we propose a formal analysis and modeling of metastable failures in replicated storage systems. We focus on a foundational problem in distributed systems—the problem of consensus—to have an impact on a large class of systems. Our main contribution is the development of a queuing-based analytical model, MSF-Model, that can be used to characterize and predict metastable failures. MSF-Model integrates novel modeling concepts that allow modeling metastable failures, which was intractable to model prior to our work. We also perform real experiments to reproduce and validate our model. Our real experiments show that MSF-Model predicts metastable failures with high accuracy by comparing the real experiment with the predictions from the queuing-based model.

Microservices architecture is quickly replacing monolithic and multi-tier architectures as the implementation choice for large-scale web applications as it allows independent development, scalability, and maintenance. However, even with careful node scheduling and scaling, the microservices applications are still vulnerable to performance degradation due to unexpected (dependent or independent) events like anomalous node behavior, workload interference, or sudden spikes in requests or retries. These events can adversely affect the performance of one or more microservices (bottlenecks), degrading the overall application performance. To ensure a good customer experience and avoid revenue loss, it is crucial to detect and mitigate all bottlenecks swiftly.

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. \emphIn the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model'') and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries.