The widespread adoption of public and hybrid clouds, along with elastic resources and various automation tools for dynamic deployment, has accelerated the rapid provisioning of compute resources as needed. Despite these advancements, numerous resources persist unnecessarily due to factors such as poor digital hygiene, risk aversion, or the absence of effective tools, resulting in substantial costs and energy consumption. Existing threshold-based techniques prove inadequate in effectively addressing this challenge. To address this issue, we propose an unsupervised machine learning framework to automatically identify resources that can be de-provisioned completely or summoned on a schedule. Application of this approach to enterprise data has yielded promising initial results, facilitating the segregation of productive workloads with recurring demands from non-productive ones.

In a recent paper [17], we described experiments using the VAX LINDA system. VAX LINDA allows a single application program to utilize many machines on a network simultaneously. Applications implemented on the network at Sandia National Laboratories have achieved speeds considerably greater than that of a Cray-1S.

A distributed parallel processing system based on the Linda programming constructs has been implemented on a local area network. The system allows a single application program to utilize many machines on the network simultaneously. Several applications have been implemented on the network at Sandia National Laboratories and have achieved performances considerably faster than that of a Cray-1S. Several collections of machines have been used, including up to 11 DEC VAXes, three Sun/3 workstations, and a PC.>

A distributed data structure is a data structure that can be manipulated by many parallel processes simultaneously. Distributed data structures are the natural complement to parallel program structures, where a parallel program (for our purposes) is one that is made up of many simultaneously active, communicating processes. Distributed data structures are impossible in most parallel programming languages, but they are supported in the parallel language Linda and they are central to Linda programming style. We outline Linda, then discuss some distributed data structures that have arisen in Linda programming experiments to date. Our intent is neither to discuss the design of the Linda system nor the performance of Linda programs, though we do comment on both topics; we are concerned instead with a few of the simpler and more basic techniques made possible by a language model that, we argue, is subtly but fundamentally different in its implications from most others.This material is based upon work supported by the National Science Foundation under Grant No. MCS-8303905. Jerry Leichter is supported by a Digital Equipment Corporation Graduate Engineering Education Program fellowship.