The need for fail-slow fault tolerance in modern distributed systems is highlighted by the increasingly reported fail-slow hardware/software components that lead to poor performance system-wide. We argue that fail-slow fault tolerance not only needs new distributed protocol designs, but also desires programming support for implementing and verifying fail-slow fault-tolerant code. Our observation is that the inability of tolerating fail-slow faults in existing distributed systems is often rooted in the implementations and is difficult to understand and debug. We designed the Dependably Fast Library (DepFast) for implementing fail-slow tolerant distributed systems. DepFast provides expressive interfaces for taking control of possible fail-slow points in the program to prevent unexpected slowness propagation once and for all. We use DepFast to implement a distributed replicated state machine (RSM) and show that it can tolerate various types of fail-slow faults that affect existing RSM implementations.

360-degree video has been popular due to the immersive experience it provides to the viewer. While watching, viewer can control the field of view (FoV) in the range of 360° by 180°. As this trend continues, adaptive bitrate (ABR) streaming is becoming a prevalent issue. Most existing ABR algorithms for 360 videos (360 ABR algorithms) require real-time head traces and certain computation resource from the client for streaming, which largely constrains the range of audience. Also, while more 360 ABR algorithms rely upon machine learning (ML) for viewport prediction, ML and ABR are research topics that grow mostly independently. In this paper, we use viewport interchangeably with FoV in the range of 360° by 180°. As this trend continues, adaptive bitrate (ABR) streaming is becoming a prevalent issue. Most existing ABR algorithms for 360 videos (360 ABR algorithms) require real-time head traces and certain computation resource from the client for streaming, which largely constrains the range of audience. Also, while more 360 ABR algorithms rely upon machine learning (ML) for viewport prediction, ML and ABR are research topics that grow mostly independently. In this paper, we propose a two-fold ABR algorithm for 360 video streaming that utilizes 1) an off-the-shelf ABR algorithm for ordinary videos, and 2) an off-the-shelf viewport prediction model. Our algorithm requires neither real-time head traces nor additional computation from the viewing device. In addition, it adapts easily to the newest developments in viewport prediction and ABR. As a consequence, the proposed method fits nicely to the existing streaming framework and any advancement in viewport prediction and ABR could enhance its performance. With the quantitative experiments, we demonstrate that the proposed method achieves twice the quality of experience (QoE) compared to the baseline.