The Quadratic Assignment Problem

Çela, Eranda

doi:10.1007/978-1-4757-2787-6

Cited by 200 publications

(89 citation statements)

References 9 publications

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“…We note that by conditional congestion constraints (8), L e (τ ( ) , p i−1 , p i ) is well-defined and always bounded by c e .…”

Section: Bounding Edge and Node Congestionmentioning

confidence: 99%

Minimum Congestion Mapping in a Cloud

Bansal¹,

Lee²,

Nagarajan³

et al. 2015

SIAM J. Comput.

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We study a basic resource allocation problem that arises in cloud computing environments. The physical network of the cloud is represented as a graph with vertices denoting servers and edges corresponding to communication links. A workload is a set of processes with processing requirements and mutual communication requirements. The workloads arrive and depart over time, and the resource allocator must map each workload upon arrival to the physical network. We consider the objective of minimizing the congestion.We show that solving a subproblem (SingleMap) about mapping a single workload to the physical graph essentially suffices to solve the general problem. In particular, an α-approximation for SingleMap gives an O(α log nD) competitive algorithm for the general problem, where n is the number of nodes in the physical network and D is the maximum to minimum workload duration ratio.We also show how to solve SingleMap for two natural class of workloads, namely depth-d trees and complete-graph workloads. For depth-d tree, we give an n O(d) time O(d 2 log(nd))-approximation based on a strong LP relaxation inspired by the Sherali-Adams hierarchy.

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“…We note that by conditional congestion constraints (8), L e (τ ( ) , p i−1 , p i ) is well-defined and always bounded by c e .…”

Section: Bounding Edge and Node Congestionmentioning

confidence: 99%

Minimum Congestion Mapping in a Cloud

Bansal¹,

Lee²,

Nagarajan³

et al. 2015

SIAM J. Comput.

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“…The QAP has been considered as one of the most difficult problems and extensively studied in optimization literatures [19,20]. Most exact solutions to the QAP typically involve branch-and-bound searching with a computational lower bound of O(M 5 ).…”

Section: A Map Generation and Indexmentioning

confidence: 99%

MISC: Merging incorrect symbols using constellation diversity for 802.11 retransmission

Zheng

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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-802.11 WLANs suffer from high packet losses due to interference and noise. Packet retransmission is a fundamental way to recover a lost packet. To extract useful information from incorrect symbols and improve retransmission efficiency, we present MISC, a packet retransmission scheme that merges incorrect symbols from multiple transmissions to produce correct ones. MISC proactively creates constellation diversity by rearranging the constellation maps in retransmissions. MISC addresses practical implementation issues and makes minimum amendments to integrate into current 802.11 WLAN framework. We implement MISC in an 802.11-based GNURadio/USRP platform and conduct extensive experiments to evaluate its efficacy. Experiment results demonstrate that MISC can substantially improve the throughput.I. INTRODUCTION 802.11 WLANs suffer from high transmission errors due to interference and noise. When a packet gets corrupted and fails the checksum test, current 802.11 receiver discards the corrupted packet and attempts to receive a clean packet through packet retransmission. As a growing number of wireless devices contend limited unlicensed spectrum (e.g., 2.4G ISM band), such a scheme is inefficient and results in even more retransmissions [8].In this paper, we propose MISC, an extension to current 802.11 framework, which merges incorrect symbols leveraging constellation diversity to improve packet retransmission efficiency. MISC allows the receiver to jointly decode a packet based on multiple retransmissions. The constellation points are shuffled for successive retransmissions in a way that each constellation point will have completely different neighboring points from the former transmission. For each received symbol in a packet, we store the distances from this PHY symbol to each point on the constellation map. To combine multiple packets, we sum up the distances in different transmissions and pick the constellation point with the minimum overall distance. As MISC proactively creates constellation diversity by using alternative comstellation maps for retransmissions, the symbol distances between the received symbol and potential incorrect candidates rapidly increase, while the distance to the correct point remains statistically low. By doing so, the receiver can correct symbol errors leveraging constellation diversity and achieve higher retransmission efficiency.We design MISC in a way that minimum amendments are needed to integrate into current 802.11 WLAN framework. Practical challenges are addressed in implementing MISC under 802.11 framework. In particular, we deeply study the way of constellation map alternation on sequential transmissions that achieves balance between decoding performance and overhead, and allows easy integration into 802.11 protocol stack. We consider a practical extension on 802.11 PHY

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“…It is well-known, that the single-slot GAP can be modeled in analogy to the NP-hard quadratic assignment problem [14], [38], [35] which is a facility location problem where the cost of placing a flight at a gate depends on the placement of other facilities and transport volume between two facilities (see also [34]). …”

Section: Types Of Objectivesmentioning

confidence: 99%

Flight gate scheduling: State-of-the-art and recent developments

Dorndorf

Drexl

Nikulin

et al. 2007

Omega

126

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This work has been supported by the German Science Foundation (DFG) through the grant "Planung der Bodenabfertigung an Flughäfen" (Dr 170/9-1, 9-2 and Pe 514/10-2). Terms of use: Documents in2 Corresponding author Abstract This paper surveys a large variety of mathematical models and up-to-date Solution techniques developed for solving a general flight gate scheduling problem that deals with assigning difFerent aircraft activities (arrival, departure and intermediate parking) to distinct aircraft stands or gates. The aim of the work is both to present various models and Solution techniques which are available in nowadays literature and to give a general idea about new open problems that arise in practise. We restrict the scope of the paper to flight gate Management without touching scheduling of ground handling Operations.Keywords: flight gate scheduling, assignment of aircraft activities to terminals, survey of models and algorithms.

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The Quadratic Assignment Problem

Cited by 200 publications

References 9 publications

Minimum Congestion Mapping in a Cloud

Minimum Congestion Mapping in a Cloud

MISC: Merging incorrect symbols using constellation diversity for 802.11 retransmission

Flight gate scheduling: State-of-the-art and recent developments

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