ABSTRACTION CHECKPOINTING LEVELS: PROBLEMS AND SOLUTIONS

A common approach to guarantee an acceptable level of fault tolerance in scientific computing is the checkpointing. In this strategy: when a task fails, it is allowed to be restarted from the recently checked pointed state rather than from the beginning, which reduces the system loss and ensures the reliability. Several systems use the checkpointing to ensure the fault tolerance such as HPC, distributed discrete event simulation and Clouds. The literature proposes several classifications of checkpointing techniques using different metrics and criteria. In this paper we focus on the classification based on abstraction level. In this classification the checkpointing is categorized into two principal types: application level and system level. Each of these levels has its advantages and suffers from many problems. The difference between our present paper and the others surveys proposed in the literature is that: in this paper we will study each level in details. We will also study and analyze some works that propose solutions to solve the problems and exceed the limits of each abstraction level.

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Multi-Methodology Approach

Applications of Soft Systems Methodology for Organizational Change - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-7998-4504-1.ch005 ◽

2021 ◽

pp. 98-128

Keyword(s):

System Dynamics ◽

Discrete Event Simulation ◽

Discrete Event ◽

Agent Based Modeling ◽

Multiple Methods ◽

Soft Systems ◽

Agent Based ◽

Event Simulation ◽

Pluralistic Approach ◽

The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

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Discrete-Event Simulation of Ground-Based Timber Harvesting Operations

Forests ◽

10.3390/f9110683 ◽

2018 ◽

Vol 9 (11) ◽

pp. 683 ◽

Cited By ~ 6

Author(s):

Ji She ◽

Woodam Chung ◽

David Kim

Keyword(s):

Sensitivity Analysis ◽

Discrete Event Simulation ◽

Discrete Event ◽

Timber Harvesting ◽

Cycle Times ◽

Event Simulation ◽

Machine Cycle ◽

Forest Industries ◽

Machine Utilization ◽

The Difference

Operational studies are necessary to support production and management decisions of forest industries. A time study (TS) approach is widely used in timber harvesting operations to understand the performance of individual harvesting machines as well as the entire system. However, several limitations of the TS approach include the use of generalized utilization rates, incapability of capturing interactions among equipment, and model extrapolation in sensitivity analysis. In this study, we demonstrated the use of discrete event simulation (DES) techniques in modeling a ground-based timber harvesting system, and compared the DES results with those of the TS model developed with the same observed data. Although both TS and DES models provided similar estimation results for individual machine cycle times and productivities, the estimated machine utilization rates were somewhat different due to the difference in synthesizing machine processes in each approach. Our sensitivity analysis and model expansion to simulate a hypothetical harvesting system suggest that the DES approach may become an appropriate method for analyzing complex systems especially where interactions among different machine processes are unknown.

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