The Mobile Server Problem

Feldkord, Björn; Heide, Friedhelm Meyer auf der

doi:10.1145/3087556.3087575

Cited by 4 publications

(10 citation statements)

References 12 publications

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“…In [8] it was already shown that no online algorithm for our problem can be competitive even on the real line and with just k =1 server. As a consequence, we employ the following methods to derive reasonable results for the problem: On the one hand we restrict the adversary to the case with locality of requests, i.e., we introduce a parameter m c by which we can define families of instances classified by the maximum distance between two consecutive requests.…”

Section: Our Results and Outline Of The Papermentioning

confidence: 98%

“…Besides being a direct extension of the Mobile Server Problem [8], our work builds on and is related to results surrounding the k-Server and Page Migration problems. These problems have been examined in many variants and especially for the k-Server Problem there are many algorithms for special metrics.…”

Section: Related Workmentioning

confidence: 99%

“…We also consider the case, that there is no resource augmentation, i.e., the maximum movement distance of the online algorithm and of the offline solution are the same. The following lower bound, originally formulated for k =1, carries over from [8]:…”

Section: Lower Boundsmentioning

confidence: 99%

“…In every round a request appears at some point r, and if the current position of the resource is p, it is served for costs d(p,r). This problem was extended to the Mobile Server Problem [8], which puts a limit on how much the resource (called server) can move in each time step and therefore introduces the idea that the positions of resources can not be arbitrarily changed in each time step.…”

Section: Introductionmentioning

confidence: 99%

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Managing Multiple Mobile Resources

Feldkord

Knollmann

Malatyali

et al. 2020

Approximation and Online Algorithms

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We extend the Mobile Server Problem introduced in [8] to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m s per round for each server. The costs are measured similar to the classical Page Migration Problem, i.e., answering a request induces costs proportional to the distance to the nearest server, and moving a server induces costs proportional to the distance multiplied with a weight D.We show that, in our model, no online algorithm can have a constant competitive ratio, i.e., one which is independent of the input length n, even if an augmented moving distance of (1+δ)m s is allowed for the online algorithm. Therefore we investigate a restriction of the power of the adversary dictating the sequence of requests: We demand locality of requests, i.e., that consecutive requests come from points in the Euclidean space with distance bounded by some constant m c . We show constant lower bounds on the competitiveness in this setting (independent of n, but dependent on k, m s and m c ).On the positive side, we present a deterministic online algorithm with bounded competitiveness when augmented moving distance and locality of requests is assumed. Our algorithm simulates any given algorithm for the classical k-Page Migration problem as guidance for its servers and extends it by a greedy move of one server in every round. The resulting competitive ratio is polynomial in the number of servers k, the ratio between m c and m s , the inverse of the augmentation factor 1 /δ and the competitive ratio of the simulated k-Page Migration algorithm.

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Section: Our Results and Outline Of The Papermentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Lower Boundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Managing Multiple Mobile Resources

Feldkord

Knollmann

Malatyali

et al. 2020

Approximation and Online Algorithms

Self Cite

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“…The offline model of facility location problem has been studied comprehensively in the literature [15,9,40,16]. Unfortunately, it cannot describe IDPSP, becuase this model requires demands and their locations to be known in advanced, but in IDPSP, VMs are installed at any moment and subsequently their demands are not known beforehand.…”

Section: B Increamental Mobile Facility Location Modelmentioning

confidence: 99%

Intrusion Detection and Prevention Systems as a Service in Could-based Environment

Alsubhi¹,

Aljahdali²

2018

ijacsa

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Intrusion Detection and Prevention Systems (IDPSs) are standalone complex hardware, expensive to purchase, change and manage. The emergence of Network Function Virtualization (NFV) and Software Defined Networking (SDN) mitigates these challenges and delivers middlebox functions as virtual instances. Moreover, cloud computing has become a very cost-effective model for sharing large-scale services in recent years. Features such as portability, isolation, live migration, and customizability of virtual machines for high-performance computing have attracted enterprise customers to move their in-house IT data center to the cloud. In this paper, we formulate the placement of Intrusion Detection and Prevention Systems (IDPS) and introduce a model called Incremental Mobile Facility Location Problem (IMFLP) to study the IDPP problem. Moreover, we propose a novel and efficient solution called Adaptive Facility Location (AFL) to efficiently solve the optimization problem introduced in the IMFLP model. The effectiveness of our solution is evaluated through realistic simulation studies compared with other popular online facility location algorithms.

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SPAA 2017 Review

Assadi

2017

SIGACT News

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The Mobile Server Problem

Cited by 4 publications

References 12 publications

Managing Multiple Mobile Resources

Managing Multiple Mobile Resources

Intrusion Detection and Prevention Systems as a Service in Could-based Environment

SPAA 2017 Review

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