Near-optimal replacement policies for shared caches in multicore processors

2021 ◽  
Author(s):  
Javier Díaz ◽  
Pablo Ibáñez ◽  
Teresa Monreal ◽  
Víctor Viñals ◽  
José M. Llabería
Keyword(s):  
Multicore Processors ◽  
Optimal Replacement ◽  
Shared Caches

On Optimal Replacement Policies

1982 ◽  
Vol 28 (11) ◽  
pp. 1304-1312 ◽  
Author(s):  
D. Assaf ◽  
B. Levikson
Keyword(s):  
Optimal Replacement

A bivariate optimal replacement policy for a repairable system

1994 ◽  
Vol 31 (4) ◽  
pp. 1123-1127 ◽  
Author(s):  
Yuan Lin Zhang
Keyword(s):  
Explicit Expression ◽  
Average Cost ◽  
Replacement Policy ◽  
Repairable System ◽  
Optimal Replacement ◽  
Long Run ◽  
Optimal Replacement Policy ◽  
Working Age ◽  
Better Than

In this paper, a repairable system consisting of one unit and a single repairman is studied. Assume that the system after repair is not as good as new. Under this assumption, a bivariate replacement policy (T, N), where T is the working age and N is the number of failures of the system is studied. The problem is to determine the optimal replacement policy (T, N)∗such that the long-run average cost per unit time is minimized. The explicit expression of the long-run average cost per unit time is derived, and the corresponding optimal replacement policy can be determined analytically or numerically. Finally, under some conditions, we show that the policy (T, N)∗ is better than policies N∗ or T∗.

Burn-in procedures for a generalized model

2001 ◽  
Vol 38 (02) ◽  
pp. 542-553 ◽  
Author(s):  
Ji Hwan Cha
Keyword(s):  
The Other ◽  
Replacement Policy ◽  
Type I ◽  
Type Ii ◽  
Failure Model ◽  
Minimal Repair ◽  
Optimal Replacement ◽  
Complete Repair ◽  
Optimal Replacement Policy ◽  
Type Of Failure

In this paper two burn-in procedures for a general failure model are considered. There are two types of failure in the general failure model. One is Type I failure (minor failure) which can be removed by a minimal repair or a complete repair and the other is Type II failure (catastrophic failure) which can be removed only by a complete repair. During a burn-in process, with burn-in Procedure I, the failed component is repaired completely regardless of the type of failure, whereas, with burn-in Procedure II, only minimal repair is done for the Type I failure and a complete repair is performed for the Type II failure. In field use, the component is replaced by a new burned-in component at the ‘field use age’ T or at the time of the first Type II failure, whichever occurs first. Under the model, the problems of determining optimal burn-in time and optimal replacement policy are considered. The two burn-in procedures are compared in cases when both the procedures are applicable.

Demography and dispersal: invasion speeds and sensitivity analysis in periodic and stochastic environments

2010 ◽  
Vol 4 (4) ◽  
pp. 407-421 ◽  
Author(s):  
Hal Caswell ◽  
Michael G. Neubert ◽  
Christine M. Hunter
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Environments

scholarly journals Optimal replacement of water pipes

2003 ◽  
Vol 39 (5) ◽  
Author(s):  
Alain Mailhot ◽  
Annie Poulin ◽  
Jean-Pierre Villeneuve
Keyword(s):  
Water Pipes ◽  
Optimal Replacement

The Cloud Hunter’s Problem: An Automated Decision Algorithm to Improve the Productivity of Scientific Data Collection in Stochastic Environments

2011 ◽  
Vol 139 (7) ◽  
pp. 2276-2289 ◽  
Author(s):  
Arthur A. Small ◽  
Jason B. Stefik ◽  
Johannes Verlinde ◽  
Nathaniel C. Johnson
Keyword(s):  
Data Collection ◽  
Decision Process ◽  
Scientific Data ◽  
Cognitive Resources ◽  
Self Organizing Maps ◽  
Decision Algorithm ◽  
Weather Forecasts ◽  
Stochastic Environments ◽  
Atmospheric Radiation Measurement ◽  
Data Yield

Abstract A decision algorithm is presented that improves the productivity of data collection activities in stochastic environments. The algorithm was developed in the context of an aircraft field campaign organized to collect data in situ from boundary layer clouds. Required lead times implied that aircraft deployments had to be scheduled in advance, based on imperfect forecasts regarding the presence of conditions meeting specified requirements. Given an overall cap on the number of flights, daily fly/no-fly decisions were taken traditionally using a discussion-intensive process involving heuristic analysis of weather forecasts by a group of skilled human investigators. An alternative automated decision process uses self-organizing maps to convert weather forecasts into quantified probabilities of suitable conditions, together with a dynamic programming procedure to compute the opportunity costs of using up scarce flights from the limited budget. Applied to conditions prevailing during the 2009 Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign of the U.S. Department of Energy’s Atmospheric Radiation Measurement Program, the algorithm shows a 21% increase in data yield and a 66% improvement in skill over the heuristic decision process used traditionally. The algorithmic approach promises to free up investigators’ cognitive resources, reduce stress on flight crews, and increase productivity in a range of data collection applications.

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