Network Function Virtualization (NFV) enables flexible implementation of network functions, also called middleboxes, as virtual machines running on standard servers. However, the flexibility also makes it a challenge ...
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Network Function Virtualization (NFV) enables flexible implementation of network functions, also called middleboxes, as virtual machines running on standard servers. However, the flexibility also makes it a challenge to optimally place middleboxes, because a middlebox may be hosted by different servers at different locations. The middlebox placement challenge is further complicated by additional constraints, including the capability of middleboxes to change traffic volumes and dependency between them. In this paper, we address the optimal placement challenge of NFV middleboxes for the data plane using a software-defined networking (SDN) approach. First, we formulate the optimization problem to place traffic-changing and interdependent middleboxes. When the flow path is predetermined, we design optimal algorithms to place a non-ordered or totally-ordered middlebox set, and propose a low-complexity solution for the general scenario of a partially-ordered middlebox set after proving its NP-hardness. When the flow path is not predetermined, we show that the problem is NP-hard even for a non-ordered or totally-ordered middlebox set, and propose an efficient traffic and space aware routing algorithm. We have evaluated the proposed algorithms using large scale simulations and a real application based SDN prototype, and present extensive evaluation results to demonstrate the superiority of our design over benchmark solutions.
A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utili...
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A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utilize visual information captured by the camera to estimate first the incidence angles of visible lights. Based on the incidence angles, eCA-RSSR utilizes the received signal strength ratio (RSSR) calculated by the photodiode (PD) to estimate the ratios of the distances between the LEDs and the receiver. Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of the distances into the absolute values. In this way, eCA-RSSR only requires three LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR can achieve high coverage. Based on the absolute values of the distances, the linear least square method is employed to estimate the position of the receiver. Therefore, for the receiver having a small distance between the PD and the camera, the accuracy of eCA-RSSR does not depend on the starting values of the non-linear least square method and the complexity of eCA-RSSR is low. Furthermore, since the distance between the PD and camera can significantly affect the performance of eCA-RSSR, we further propose a compensation algorithm for eCA-RSSR based on the single-view geometry. Experiment results show that positioning errors of less than five centimeters is achievable for eCA-RSSR. Simulation results show that eCA-RSSR can achieve 80th percentile accuracy of about four centimeters and can improve the coverage ratio at low cost.
This paper presents a framework for approximating NP-hard problems that can be formulated as integer-covering programs, possibly with additional side constraints, and the number of covering options is restricted in so...
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This paper presents a framework for approximating NP-hard problems that can be formulated as integer-covering programs, possibly with additional side constraints, and the number of covering options is restricted in some sense, although this property may be well hidden. (C) 2007 Elsevier B.V. All rights reserved.
The input to the MAXIMUM SAVING PARTITION PROBLEM consists of a set V = {1, ..., n}, weights w(i), a function f and a family J of feasible subsets of V. The output is a partition (S-1, ..., S-l) such that S-i is an el...
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The input to the MAXIMUM SAVING PARTITION PROBLEM consists of a set V = {1, ..., n}, weights w(i), a function f and a family J of feasible subsets of V. The output is a partition (S-1, ..., S-l) such that S-i is an element of J, and Sigma(jis an element ofV)w(j) - Sigma(i=1)(l) f (S-i) is maximized. We present a general 1/2-approximation algorithm, and improved algorithms for special cases of the function (C) 2004 Elsevier B.V. All rights reserved.
We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resou...
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We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource;some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings.
Recent years have witnessed the fast proliferation of camera sensors networks (CSNs) in numerous Internet of Things (IoT) applications. In order to a capture distinct image of targets from interesting directions, we l...
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Recent years have witnessed the fast proliferation of camera sensors networks (CSNs) in numerous Internet of Things (IoT) applications. In order to a capture distinct image of targets from interesting directions, we leverage a special type of coverage called full-view coverage. Full-view coverage guarantees to obtain the images of a point from every direction, whereas it demands much more sensors than a conventional coverage. To this end, we investigate the problem of deploying the minimum number of rotatable camera sensors to achieve the full-view coverage of a set of target points, namely, optimal deployment for the full-view point coverage (OFP) problem. In this work, camera sensors are capable of rotating freely with infinite orientations, thus not only the deployment locations but also the orientations for each camera sensor are required to be optimized. To tackle this challenging problem, we first prove that the OFP problem is NP-hard. Then, we propose two approximation algorithms-iterative screening algorithm (ISA) and improved ISA (IISA) to solve the OFP. We further perform extensive simulations and conduct physical testings to demonstrate the superiority and effectiveness of our proposed solutions. Experimental results show that IISA can generally reduce the total number of required camera sensors by more than 20% compared with the state-of-the-art work.
Emerging applications of control, estimation, and machine learning, from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be s...
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Emerging applications of control, estimation, and machine learning, from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used across time. Therefore, many researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. By exploiting notions of discrete convexity, such as submodularity, the researchers have been able to provide scalable algorithms with provable suboptimality bounds. In this article, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem robust sequential submodular maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. However, in this article, we propose robust and adaptive maximization (RAM), the first scalable algorithm. RAM runs in an online fashion, adapting in every step to the history of failures. Also, it guarantees a near-optimal performance, even against any number of failures among the used elements. Particularly, RAM has both provable per-instance a priori bounds and tight and/or optimal a posteriori bounds. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.
We consider the problem of channel assignment in cellular networks with arbitrary reuse distance. We show upper and lower bounds for the competitive ratio of a previously proposed and widely studied version of dynamic...
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We consider the problem of channel assignment in cellular networks with arbitrary reuse distance. We show upper and lower bounds for the competitive ratio of a previously proposed and widely studied version of dynamic channel assignment, which we refer to as the greedy algorithm. We study two versions of this algorithm: one that performs reassignment of channels, and one that never reassigns channels to calls. For reuse distance 2, we show tight bounds on the competitive ratio of both versions of the algorithm. For reuse distance 3, we show non-trivial lower bounds for both versions of the algorithm. (C) 2003 Elsevier B.V. All rights reserved.
作者：
Dror, MosheOrlin, James B.Univ Arizona
Coll Business & Publ Adm MIS Dept Tucson AZ 85721 USA MIT
Ctr Operat Res Cambridge MA 02139 USA MIT
Sloan Sch Management Cambridge MA 02139 USA
We examine a selective list of combinatorial optimization problems in NP with respect to inapproximability (Arora and Lund (1997)) given that the ground set of elements N has additional characteristics. For each probl...
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We examine a selective list of combinatorial optimization problems in NP with respect to inapproximability (Arora and Lund (1997)) given that the ground set of elements N has additional characteristics. For each problem in this paper, the set N is expressed explicitly by subsets of N either as a partition or in the form of a cover. The problems examined are generalizations of well-known classical graph problems and include the minimal spanning tree problem, a number of elementary machine scheduling problems, the bin-packing problem, and the travelling salesman problem (TSP). We conclude that for all these generalized problems the existence of a polynomial time approximation scheme (PTAS) is impossible unless P=NP. This suggests a partial characterization for a family of inapproximable problems. For the generalized Euclidean TSP we prove inapproximability even if the subsets are of cardinality
A method to find the minimized state machine cover of live and safe Petri nets (PNs) is proposed in this paper. It is required that a cover be obtained in some implementation methods of PN-based control algorithms and...
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A method to find the minimized state machine cover of live and safe Petri nets (PNs) is proposed in this paper. It is required that a cover be obtained in some implementation methods of PN-based control algorithms and other applications such as data mining. Most of the known methods of its computation have at least exponential time and space complexity;this is true not only for the methods which guarantee a minimal cover is obtained but also for some methods of finding approximate solutions. The proposed algorithm is an approximation algorithm and has polynomial computational complexity. The experimental verification shows a very high efficiency of the presented technique. The proposed method is especially valuable in the case of large systems where a solution is obtained hundreds of times quicker than by other methods.
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