318 research outputs found
PASCAL: A Learning-aided Cooperative Bandwidth Control Policy for Hierarchical Storage Systems
Nowadays, the Hierarchical Storage System (HSS) is considered as an ideal
model to meet the cost-performance demand. The data migration between storing
tiers of HSS is the way to achieve the cost-performance goal. The bandwidth
control is to limit the maximum amount of data migration. Most of previous
research about HSS focus on studying the data migration policy instead of
bandwidth control. However, the recent research about cache and networking
optimization suggest that the bandwidth control has significant impact on the
system performance. Few previous work achieves a satisfactory bandwidth control
in HSS since it is hard to control bandwidth for so many data migration tasks
simultaneously. In this paper, we first give a stochastic programming model to
formalize the bandwidth control problem in HSS. Then we propose a
learning-aided bandwidth control policy for HSS, named \Pascal{}, which learns
to control the bandwidth of different data migration task in an cooperative
way. We implement \Pascal{} on a commercial HSS and compare it with three
strong baselines over a group of workloads. Our evaluation on the physical
system shows that \Pascal{} can effectively decrease 1.95X the tail latency and
greatly improve throughput stability (2X throughput jitter), and
meanwhile keep the throughput at a relatively high level
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Carbon Monoxide Oxidation Promoted by Surface Polarization Charges in a CuO/Ag Hybrid Catalyst.
Composite structures have been widely utilized to improve material performance. Here we report a semiconductor-metal hybrid structure (CuO/Ag) for CO oxidation that possesses very promising activity. Our first-principles calculations demonstrate that the significant improvement in this system's catalytic performance mainly comes from the polarized charge injection that results from the Schottky barrier formed at the CuO/Ag interface due to the work function differential there. Moreover, we propose a synergistic mechanism underlying the recovery process of this catalyst, which could significantly promote the recovery of oxygen vacancy created via the M-vK mechanism. These findings provide a new strategy for designing high performance heterogeneous catalysts
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
In the past few years, there has been an explosive surge in the use of
machine learning (ML) techniques to address combinatorial optimization (CO)
problems, especially mixed-integer linear programs (MILPs). Despite the
achievements, the limited availability of real-world instances often leads to
sub-optimal decisions and biased solver assessments, which motivates a suite of
synthetic MILP instance generation techniques. However, existing methods either
rely heavily on expert-designed formulations or struggle to capture the rich
features of real-world instances. To tackle this problem, we propose G2MILP,
the first deep generative framework for MILP instances. Specifically, G2MILP
represents MILP instances as bipartite graphs, and applies a masked variational
autoencoder to iteratively corrupt and replace parts of the original graphs to
generate new ones. The appealing feature of G2MILP is that it can learn to
generate novel and realistic MILP instances without prior expert-designed
formulations, while preserving the structures and computational hardness of
real-world datasets, simultaneously. Thus the generated instances can
facilitate downstream tasks for enhancing MILP solvers under limited data
availability. We design a suite of benchmarks to evaluate the quality of the
generated MILP instances. Experiments demonstrate that our method can produce
instances that closely resemble real-world datasets in terms of both structures
and computational hardness. The deliverables are released at
https://miralab-ustc.github.io/L2O-G2MILP
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