18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.
DOI: 10.1109/ipdps.2004.1303074
|View full text |Cite
|
Sign up to set email alerts
|

Iterative integer programming formulation for robust resource allocation in dynamic real-time systems

Abstract: Dynamic real-time systems often operate in a continuously changing environment, causing workload of the system to fluctuate. An initial resource allocation for these systems should be robust with respect to the variation in workload. Using the amount of additional workload that an allocation can accommodate as a measure of robustness, we develop an iterative integer programming approach, called IIP, to determine a robust resource allocation. IIP guarantees to produce an allocation with the measure of robustnes… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(2 citation statements)
references
References 12 publications
0
1
0
Order By: Relevance
“…A stochastic‐mixed integer programming approach has also been used to obtain a robust resource allocation for parallel and distributed systems . As a subsequent work, the authors generate a more robust resource allocation using an iterative integer programming approach . The resource allocation obtained upon the iterative integer programming formulation of the mapping problem allows some slackness ( δ ), which is provided by the user of the application.…”
Section: Background and Related Workmentioning
confidence: 99%
“…A stochastic‐mixed integer programming approach has also been used to obtain a robust resource allocation for parallel and distributed systems . As a subsequent work, the authors generate a more robust resource allocation using an iterative integer programming approach . The resource allocation obtained upon the iterative integer programming formulation of the mapping problem allows some slackness ( δ ), which is provided by the user of the application.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Integer and Linear programming have been applied to a variety of problems in networking. ViNEYard [8] uses mixed integer programming to coordinate node and link mapping in virtual network embedding, while [23] and [24] use mathematical programming for dynamic resource allocation in networks. [25] uses an optimization technique for link mapping (assuming that the virtual nodes have already been mapped to substrate nodes), while [26] incorporates substrate failures in the virtual network embedding problem by formulating the link mapping problem as a path-based multi-commodity flow (MCF) [27] problem.…”
Section: Application Of Column Generation To Vnementioning
confidence: 99%