In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

Memory-intensive applications suffer large performance loss when their working sets do not
fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging …

Serverless platforms facilitate transparent resource elasticity and fine-grained billing, making
them an attractive choice for data analytics. We find that while server-centric analytics …

There is a lot of interest today in building autonomous (or, self-driving) data processing
systems. An emerging school of thought is to leverage AI-driven" black box" algorithms for …

Memory usage imbalance has been consistently observed in many virtualized Clouds and
production datacenters. Such temporal memory utilization variance is a major root cause for …

This article presents XMemPod, a hierarchical disaggregated memory orchestration system.
XMemPod virtualizes cluster wide memory to scale large memory workloads in virtualized …

Caches at CPU nodes in disaggregated memory architectures amortize the high data
access latency over the network. However, such caches are fundamentally unable to …

Applications with large-scale data are processed on a distributed system, such as Spark, as
they are data-and computation-intensive. Predicting the performance of such applications is …

A dataflow execution environment is provided with dynamic placement of cache operations.
An exemplary method comprises: obtaining a first cache placement plan for a dataflow …

A lack of memory can lead to job failures or increase processing times for garbage
collection. However, if too much memory is provided, the processing time is only marginally …