Recent changes in data temperatures and storage device characteristics, both mechanical disk-drives (HDDs) and solid-state drives (SSDs), expand the set of deployment options for exascale storage. Until recently, exascale storage systems followed a pattern of placing most data on HDDs with smaller amounts of SSD storage used for caching and performance-critical workloads. Exascale storage provisioning and dataset placement trade-offs have now changed.

Many data services tune and change redundancy configurations of files over their lifetimes to address changes in data temperature and latency requirements. Unfortunately, changing redundancy configs (transcode) is IO-intensive. The Morph cluster file system introduces new transcode-efficient redundancy schemes to minimize overheads as files progress through lifetime phases. For newly ingested data, commonly stored via 3-way replication, Morph introduces a hybrid redundancy scheme that combines a replica with an erasure-coded (EC) stripe, reducing both ingest IO and capacity overheads while enabling free transcode to EC by deleting replicas. For subsequent transcodes to wider, more space-efficient EC configs, Morph exploits Convertible Codes, which minimize data read for EC transcode, and introduces new block placement policies to maximize their effectiveness.

In this paper, we consider the important aspect of quality of service (QoS) in wireless mesh networks, focusing on packet delays and packet drops. We observe that the options of solely focusing on throughput, and of only depending on the QoS type characterization by the application level protocol is not sufficient. We propose the use of multiple queues to hold the packets based on their QoS requirement (not necessarily corresponding to the QoS application types). We suggest the technique of reinforcement learning (RL) for the important step of assigning packet to one of the queues, and present an implementation using the TD(0) algorithm. We present various simulation results comparing our algorithm to other known algorithms and frameworks. Our results indicate that our RL-based algorithm is significantly better than previously known results in packet drop ratio.