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The Generalized Maximum Coverage Problem
"... We define a new problem called the Generalized Maximum Coverage Problem (GMC). GMC is an extension of the Budgeted Maximum Coverage Problem, and it has important applications in wireless OFDMA scheduling. We use a variation of the greedy algorithm to produce a ( 2e−1 e−1 +ɛ)approximation for every ..."
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We define a new problem called the Generalized Maximum Coverage Problem (GMC). GMC is an extension of the Budgeted Maximum Coverage Problem, and it has important applications in wireless OFDMA scheduling. We use a variation of the greedy algorithm to produce a ( 2e−1 e−1 +ɛ)approximation for every ɛ> 0, and then use partial enumeration to reduce + ɛ. the approximation ratio to e e−1 1
Frugal sensor assignment
 In 4th IEEE International Conference on Distributed Computing in Sensor Systems
, 2008
"... Abstract. When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensorassignment ..."
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Abstract. When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensorassignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In general, the problems we study are NPhard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In the dynamic setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help based on the value of the mission, the sensor’s remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations. 1
Scalable rule management for data centers
 in NSDI
, 2013
"... Cloud operators increasingly need more and more finegrained rules to better control individual network flows for various traffic management policies. In this paper, we explore automated rule management in the context of a system called vCRIB (a virtual Cloud Rule Information Base), which provides ..."
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Cited by 12 (2 self)
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Cloud operators increasingly need more and more finegrained rules to better control individual network flows for various traffic management policies. In this paper, we explore automated rule management in the context of a system called vCRIB (a virtual Cloud Rule Information Base), which provides the abstraction of a centralized rule repository. The challenge in our approach is the design of algorithms that automatically offload rule processing to overcome resource constraints on hypervisors and/or switches, while minimizing redirection traffic overhead and responding to system dynamics. vCRIB contains novel algorithms for finding feasible rule placements and adapting traffic overhead induced by rule placement in the face of traffic changes and VM migration. We demonstrate that vCRIB can find feasible rule placements with less than 10 % traffic overhead even in cases where the trafficoptimal rule placement may be infeasible with respect to hypervisor CPU or memory constraints. 1
Agentbased sensormission assignment for tasks sharing assets
 in Third International Workshop on Agent Technology for Sensor Networks
, 2009
"... A sensor network may be required to support multiple mission to be accomplished simultaneously. Furthermore, the environment may change at any time; i.e. a new mission may arrive at any time. In solving this manymission, manysensor problem in dynamic environments, conflicts between missions may o ..."
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Cited by 8 (4 self)
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A sensor network may be required to support multiple mission to be accomplished simultaneously. Furthermore, the environment may change at any time; i.e. a new mission may arrive at any time. In solving this manymission, manysensor problem in dynamic environments, conflicts between missions may occur for the use of sensor resources. A mechanism to match sensor resources to mission demands thus becomes necessary. In this paper, motivated by the conservation of resources, we consider the problem of sensormission assignment, in which sensors may be shared and reassigned between tasks. To achieve this, sensors are represented by agents, which coordinate to establish virtual organizations to meet mission requirements. The agent coordinating the achievement of a mission utilises a novel multiround, Knapsack based algorithm, GAPE, to allocate sensor agents to tasks based on bids received. Through simulations, we empirically demonstrate that this model provides a significant improvement in the number of completed missions as well as execution time.
Cell selection in 4g cellular networks
 In Proceedings of the Annual IEEE 27th INFOCOM
, 2008
"... Abstract—Cell selection is the process of determining the cell(s) that provide service to each mobile station. Optimizing these processes is an important step toward maximizing the utilization of current and future cellular networks. We study the potential benefit of global cell selection versus the ..."
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Abstract—Cell selection is the process of determining the cell(s) that provide service to each mobile station. Optimizing these processes is an important step toward maximizing the utilization of current and future cellular networks. We study the potential benefit of global cell selection versus the current local mobile SNRbased decision protocol. In particular, we study the new possibility available in OFDMAbased systems, such as IEEE 802.16m and LTEAdvanced, of satisfying the minimal demand of a mobile station simultaneously by more than one base station. We formalize the problem as an optimization problem, and show that in the general case this problem is not only NPhard but also cannot be approximated within any reasonable factor. In contrast, under the very practical assumption that the maximum required bandwidth of a single mobile station is at most an rfraction of the capacity of a base station, we present two different algorithms for cell selection. The first algorithm produces a ð1 rÞapproximate solution, where a mobile station can be covered simultaneously by more than one base station. The second algorithm produces a 1 r 2 rapproximate solution, while every mobile station is covered by at most one base station. We complete our study by an extensive simulation study demonstrating the benefits of using our algorithms in highloaded capacityconstrained future 4G networks, compared to currently used methods. Specifically, our algorithms obtain up to 20 percent better usage of the network’s capacity, in comparison with the current cell selection algorithms.
Wimax/ofdma burst scheduling algorithm to maximize scheduled data
 IEEE Transactions on Mobile Computing
, 2011
"... Abstract—OFDMA resource allocation algorithms manage the distribution and assignment of shared OFDMA resources among the users serviced by the basestation. The OFDMA resource allocation algorithms determine which users to schedule, how to allocate subcarriers to them, and how to determine the approp ..."
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Abstract—OFDMA resource allocation algorithms manage the distribution and assignment of shared OFDMA resources among the users serviced by the basestation. The OFDMA resource allocation algorithms determine which users to schedule, how to allocate subcarriers to them, and how to determine the appropriate power levels for each user on each subcarrier. In WiMAX, the downlink (DL) TDD OFDMA subframe structure is a rectangular area of N subchannels K time slots. Users are assigned rectangular bursts in the downlink subframe. The size of burst, in terms of number of subchannels and number of time slots, varies based on the user’s channel quality and data to be transmitted for the assigned user. In this paper, we study the problem of assigning users to bursts in WiMAX TDD OFDMA system with the objective of maximizing downlink system throughput for the Partially Used subcarrier (PUSC) subchannalization permutation mode. Our main contributions in this paper are: 1) we propose different methods to assign bursts to users, 2) we prove that our Best Channel burst assignment method achieves throughput within a constant factor of the optimal, 3) through extensive simulations with real system parameters, we study the performance of the Best Channel burst assignment method. To the best of our knowledge, we are the first to study the problem of DL Burst Assignment in the downlink OFDMA subframe for PUSC subchannalization permutation mode taking user’s channel quality into consideration in the assignment process. Index Terms—WiMAX, OFDMA, wireless scheduling, burst scheduling, throughput maximization Ç 1
DailyDeal Selection for Revenue Maximization
"... DailyDeal Sites (DDS) like Groupon, LivingSocial, Amazon’s Goldbox, and many more, have become particularly popular over the last three years, providing discounted offers to customers for restaurants, ticketed events, services etc. In this paper, we study the following problem: among a set of candi ..."
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DailyDeal Sites (DDS) like Groupon, LivingSocial, Amazon’s Goldbox, and many more, have become particularly popular over the last three years, providing discounted offers to customers for restaurants, ticketed events, services etc. In this paper, we study the following problem: among a set of candidate deals, which are the ones that a DDS should featureas dailydealsinorderto maximizeits revenue? Ourfirst contribution lies in providing two combinatorial formulations of this problem. Both formulations take into account factors like the diversification of daily deals and the limited consuming capacity of the userbase. We prove that our problems are NPhard and devise pseudopolynomial – time approximation algorithms for their solution. We also propose a set of heuristics, and demonstrate their efficiency in our experiments. In the context of deal selection and scheduling, we acknowledge the importance of the ability to estimate the expected revenue of a candidate deal. We explore the nature of this task in thecontextof real data, andpropose a framework for revenueestimation. We demonstrate the effectiveness of our entire methodology in an experimental evaluation on a large dataset of dailydeals from Groupon.
1 Joint Scheduling and Fast Cell Selection in OFDMA Wireless Networks
"... Abstract—In modern broadband cellular networks, the omnidirectional antenna at each cell is replaced by 3 or 6 directional antennas, one in every sector. While every sector can run its own scheduling algorithm, bandwidth utilization can be significantly increased if a joint scheduler makes these de ..."
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Abstract—In modern broadband cellular networks, the omnidirectional antenna at each cell is replaced by 3 or 6 directional antennas, one in every sector. While every sector can run its own scheduling algorithm, bandwidth utilization can be significantly increased if a joint scheduler makes these decisions for all the sectors. This gives rise to a new problem, referred to as “joint scheduling, ” addressed in this paper for the first time. The problem is proven to be NPhard, but we propose efficient algorithms with a worstcase performance guarantee for solving it. We then show that the proposed algorithms indeed substantially increase the network throughput. Index Terms—Cellular networks, 4G mobile communication, Optimal scheduling. I.
On Lagrangian relaxation and subset selection problems
 In Proc. 6th Workshop on Approximation and Online Algorithms
, 2009
"... We prove a general result demonstrating the power of Lagrangian relaxation in solving constrained maximization problems with arbitrary objective functions. This yields a unified approach for solving a wide class of subset selection problems with linear constraints. Given a problem in this class and ..."
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Cited by 2 (1 self)
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We prove a general result demonstrating the power of Lagrangian relaxation in solving constrained maximization problems with arbitrary objective functions. This yields a unified approach for solving a wide class of subset selection problems with linear constraints. Given a problem in this class and some small ε ∈ (0, 1), we show that if there exists a ρapproximation algorithm for the Lagrangian relaxation of the problem, for some ρ ∈ (0, 1), then our technique ρ ρ+1 achieves a ratio of −ε to the optimal, and this ratio is tight. The number of calls to the ρapproximation algorithm, used by our algorithms, is linear in the input size and in log(1/ε) for inputs with cardinality constraint, and polynomial in the input size and in log(1/ε) for inputs with arbitrary linear constraint. Using the technique we obtain approximation algorithms for natural variants of classic subset selection problems, including realtime scheduling, the maximum generalized assignment problem (GAP) and maximum weight independent set. 1
Distributed algorithm design for multirobot generalized task assignment
 In Proceedings of IEEE International Conference on Intelligent Robots and Systems
, 2013
"... Abstract — We present a provablygood distributed algorithm for generalized task assignment problem in the context of multirobot systems, where robots cooperate to complete a set of given tasks. In multirobot generalized assignment problem (MRGAP), each robot has its own resource constraint (e.g. ..."
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Abstract — We present a provablygood distributed algorithm for generalized task assignment problem in the context of multirobot systems, where robots cooperate to complete a set of given tasks. In multirobot generalized assignment problem (MRGAP), each robot has its own resource constraint (e.g., energy constraint), and needs to consume a certain amount of resource to obtain a payoff for each task. The objective is to find a maximum payoff assignment of tasks to robots such that each task is assigned to at most one robot while respecting robots ’ resource constraints. MRGAP is a NPhard problem. It is an extension of multirobot linear assignment problem since different robots can use different amount of resource for doing a task (due to the heterogeneity of robots and tasks). We first present an auctionbased iterative algorithm for MRGAP assuming the presence of a shared memory (or centralized auctioneer), where each robot uses a knapsack algorithm as a subroutine to iteratively maximize its own objective (using a modified payoff function based on an auxiliary variable, called price of a task). Our iterative algorithm can be viewed as (an approximation of) best response assignment update rule of each robot to the assignment of other robots at that iteration. We prove that our algorithm converges to an assignment (approximately) at equilibrium under the assignment update rule, with an approximation ratio of 1 + α (where α is the approximation ratio for the Knapsack problem). We also combine our algorithm with a message passing mechanism to remove the requirement of a shared memory and make our algorithm totally distributed assuming the robots ’ communication network is connected. Finally, we present simulation results to depict our algorithm’s performance. I.