Coordination services and protocols are critical components of distributed systems and are essential for providing consistency, fault tolerance, and scalability. However, due to the lack of standard benchmarking and evaluation tools for distributed coordination services, coordination service developers/researchers either use a NoSQL standard benchmark and omit evaluating consistency, distribution, and fault tolerance; or create their own ad-hoc microbenchmarks and skip comparability with other services. In this study, we analyze and compare the evaluation mechanisms for known and widely used consensus algorithms, distributed coordination services, and distributed applications built on top of these services. We identify the most important requirements of distributed coordination service benchmarking, such as the metrics and parameters for the evaluation of the performance, scalability, availability, and consistency of these systems. Finally, we discuss why the existing benchmarks fail to address the complex requirements of distributed coordination system evaluation.

Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisions, outperforming common heuristic algorithms. LinTS can lower carbon emissions during inter-datacenter transfers by up to 66 % compared to the worst case and up to 15 % compared to other solutions while preserving all deadline constraints.

Efficiently transferring data over long-distance, high-speed networks requires optimal utilization of available network bandwidth. One effective method to achieve this is through the use of parallel TCP streams. This approach allows applications to leverage network parallelism, thereby enhancing transfer throughput. However, determining the ideal number of parallel TCP streams can be challenging due to non-deterministic background traffic sharing the network, as well as non-stationary and partially observable network signals. We present a novel learning-based approach that utilizes deep reinforcement learning (DRL) to determine the optimal number of parallel TCP streams. Our DRL-based algorithm is designed to intelligently utilize available network bandwidth while adapting to different network conditions. Unlike rule-based heuristics, which lack generalization in unknown network scenarios, our DRL-based solution can dynamically adjust the parallel TCP stream numbers to optimize network bandwidth utilization without causing network congestion and ensuring fairness among competing transfers. We conducted extensive experiments to evaluate our DRL-based algorithm's performance and compared it with several state-of-the-art online optimization algorithms. The results demonstrate that our algorithm can identify nearly optimal solutions 40 % faster while achieving up to 15 % higher throughput. Further-more, we show that our solution can prevent network congestion and distribute the available network resources fairly among competing transfers, unlike a discriminatory algorithm.