  Resource disaggregation offers a cost effective solution to resource scaling,
utilization, and failure-handling in data centers by physically separating
hardware devices in a server. Servers are architected as pools of processor,
memory, and storage devices, organized as independent failure-isolated
components interconnected by a high-bandwidth network. A critical challenge,
however, is the high performance penalty of accessing data from a remote memory
module over the network. Addressing this challenge is difficult as
disaggregated systems have high runtime variability in network
latencies/bandwidth, and page migration can significantly delay critical path
cache line accesses in other pages. This paper introduces DaeMon, the first
software-transparent and robust mechanism to significantly alleviate data
movement overheads in fully disaggregated systems. First, to enable scalability
to multiple hardware components in the system, we enhance each compute and
memory unit with specialized engines that transparently handle data migrations.
Second, to achieve high performance and provide robustness across various
network, architecture and application characteristics, we implement a
synergistic approach of bandwidth partitioning, link compression, decoupled
data movement of multiple granularities, and adaptive granularity selection in
data movements. We evaluate DaeMon in a wide variety of workloads at different
network and architecture configurations using a state-of-the-art accurate
simulator and demonstrate that DaeMon significantly improves system performance
and data access costs over the widely-adopted approach of moving data at page
granularity.

Hardware Architecture;Distributed, Parallel, and Cluster Computing;Performance