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Duc A Tran - One of the best experts on this subject based on the ideXlab platform.
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a geo aware Server Assignment problem for mobile edge computing
International Journal of Parallel Emergent and Distributed Systems, 2020Co-Authors: Duc A TranAbstract:As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commod...
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a geo aware Server Assignment problem for mobile edge computing
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Duc A TranAbstract:As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity Servers are deployed in the edge to form a decentralized network of mini datacenters each serving a localized region. A challenge is how to place these Servers geographically to maximize the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets.
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distributed client Server Assignment for online social network applications
IEEE Transactions on Emerging Topics in Computing, 2014Co-Authors: Thuan Duongba, Thinh Nguyen, B Bose, Duc A TranAbstract:We study the problem of assigning users to Servers with an emphasis on the distributed algorithmic solutions. Typical online social network applications, such as Facebook and Twitter, are built on top of an infrastructure of Servers that provides the services on behalf of the users. For a given communication pattern among users, the loads of the Servers depend critically on how the users are assigned to the Servers. A good Assignment will reduce the overall load of the system while balancing the loads among the Servers. Unfortunately, this optimal Assignment problem is NP-hard. Therefore, we investigate three heuristic algorithms for solving the user Server Assignment problem: 1) the centralized simulated annealing (CSA) algorithm; 2) the distributed simulated annealing (DSA) algorithm; and 3) the distributed perturbed greedy search (DPGS). The CSA algorithm produces good solution in the fastest time, however it relies on timely accurate global system information, and is practical only for small and static systems. In contrast, the two distributed algorithms, DSA and DPGS, exploit local information at each Server during their search for the optimal Assignment, and thus can scale well with the number of users and Servers as well as adapting to the system dynamics. Simulation results show that the performance of the distributed algorithms, specifically the DPGS algorithm, is very competitive with that of the centralized algorithm while providing the advantage of naturally adapting to time-varying communication patterns of users.
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daros distributed user Server Assignment and replication for online social networking applications
2013 International Conference on Computing Networking and Communications (ICNC), 2013Co-Authors: Thuan Duongba, Thinh Nguyen, Duc A TranAbstract:In this paper we study the problem of assigning users to Servers and data replication in a distributed manner for online social networking (OSN) applications. Typical OSN applications such as Facebook and Twitter are built on top of an infrastructure of Servers, which handle the users data storage and communications. Thus, for a given user's communication pattern, the loads of the Servers depend critically on the Assignment of users to Servers. A good Assignment will reduce the overall load of the system. Furthermore, by replicating data across the Servers judiciously, the overall load can also be further reduced. Unfortunately, this optimal Assignment and data replication problem is NP-hard. Therefore, we introduce a distributed heuristic algorithm in which the Servers perform local computations and exchange information among each other iteratively in such a way that the algorithm converges to a good Assignment and replication in terms of reducing the overall system load as well as balancing the loads among the Servers. In contrast with a centralized algorithm, a distributed algorithm offers the advantage of balancing the computations among all the Servers as well as the ability to naturally adapt to time-varying user's communication patterns. Simulations results show promising performance for the proposed algorithm.
Thinh Nguyen - One of the best experts on this subject based on the ideXlab platform.
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distributed client Server Assignment for online social network applications
IEEE Transactions on Emerging Topics in Computing, 2014Co-Authors: Thuan Duongba, Thinh Nguyen, B Bose, Duc A TranAbstract:We study the problem of assigning users to Servers with an emphasis on the distributed algorithmic solutions. Typical online social network applications, such as Facebook and Twitter, are built on top of an infrastructure of Servers that provides the services on behalf of the users. For a given communication pattern among users, the loads of the Servers depend critically on how the users are assigned to the Servers. A good Assignment will reduce the overall load of the system while balancing the loads among the Servers. Unfortunately, this optimal Assignment problem is NP-hard. Therefore, we investigate three heuristic algorithms for solving the user Server Assignment problem: 1) the centralized simulated annealing (CSA) algorithm; 2) the distributed simulated annealing (DSA) algorithm; and 3) the distributed perturbed greedy search (DPGS). The CSA algorithm produces good solution in the fastest time, however it relies on timely accurate global system information, and is practical only for small and static systems. In contrast, the two distributed algorithms, DSA and DPGS, exploit local information at each Server during their search for the optimal Assignment, and thus can scale well with the number of users and Servers as well as adapting to the system dynamics. Simulation results show that the performance of the distributed algorithms, specifically the DPGS algorithm, is very competitive with that of the centralized algorithm while providing the advantage of naturally adapting to time-varying communication patterns of users.
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daros distributed user Server Assignment and replication for online social networking applications
2013 International Conference on Computing Networking and Communications (ICNC), 2013Co-Authors: Thuan Duongba, Thinh Nguyen, Duc A TranAbstract:In this paper we study the problem of assigning users to Servers and data replication in a distributed manner for online social networking (OSN) applications. Typical OSN applications such as Facebook and Twitter are built on top of an infrastructure of Servers, which handle the users data storage and communications. Thus, for a given user's communication pattern, the loads of the Servers depend critically on the Assignment of users to Servers. A good Assignment will reduce the overall load of the system. Furthermore, by replicating data across the Servers judiciously, the overall load can also be further reduced. Unfortunately, this optimal Assignment and data replication problem is NP-hard. Therefore, we introduce a distributed heuristic algorithm in which the Servers perform local computations and exchange information among each other iteratively in such a way that the algorithm converges to a good Assignment and replication in terms of reducing the overall system load as well as balancing the loads among the Servers. In contrast with a centralized algorithm, a distributed algorithm offers the advantage of balancing the computations among all the Servers as well as the ability to naturally adapt to time-varying user's communication patterns. Simulations results show promising performance for the proposed algorithm.
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Optimal Client-Server Assignment for Internet Distributed Systems
IEEE Transactions on Parallel and Distributed Systems, 2013Co-Authors: Hiroshi Nishida, Thinh NguyenAbstract:We investigate an underlying mathematical model and algorithms for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate Servers. Optimizing the overall performance of such a system then can be formulated as a client-Server Assignment problem whose aim is to assign the clients to the Servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their tradeoff is inherent in this class of distributed systems; 2) in general, finding the optimal client-Server Assignment for some prespecified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.
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optimal client Server Assignment for internet distributed systems
International Conference on Computer Communications and Networks, 2011Co-Authors: Hiroshi Nishida, Thinh NguyenAbstract:We investigate an underlying mathematical model and algorithm for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate Servers. Optimizing the overall performance of such a system then can be formulated as a client Server Assignment problem whose aim is to assign the clients to the Servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their trade-off is inherent to this class of distributed systems; 2) in general, finding the optimal client-Server Assignment for some pre-specified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution to the client-Server Assignment problem. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.
Douglas G Down - One of the best experts on this subject based on the ideXlab platform.
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Maximizing throughput in zero-buffer tandem lines with dedicated and flexible Servers
2020Co-Authors: Mohammad H Yarmand, Douglas G DownAbstract:Abstract For tandem queues with no buffer spaces and both dedicated and flexible Servers, we study how flexible Servers should be assigned to maximize the throughput. When there is one flexible Server and two stations each with a dedicated Server, we completely characterize the optimal policy. We use the insights gained from applying the Policy Iteration algorithm on systems with three, four, and five stations to devise heuristics for systems of arbitrary size. These heuristics are verified by numerical analysis. We also discuss the throughput improvement, when for a given Server Assignment, dedicated Servers are changed to flexible Servers
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dynamic Server allocation for queueing networks with flexible Servers
Operations Research, 2003Co-Authors: Sigrun Andradottir, Hayriye Ayhan, Douglas G DownAbstract:This paper is concerned with the design of dynamic Server Assignment policies that maximize the capacity of queueing networks with flexible Servers. Flexibility here means that each Server may be capable of performing service at several different classes in the network. We assume that the interarrival times and the service times are independent and identically distributed, and that routing is probabilistic. We also allow for Server switching times, which we assume to be independent and identically distributed. We deduce the value of a tight upper bound on the achievable capacity by equating the capacity of the queueing network model with that of a limiting deterministic fluid model. The maximal capacity of the deterministic model is given by the solution to a linear programming problem that also provides optimal allocations of Servers to classes. We construct particular Server Assignment policies, called generalized round-robin policies, that guarantee that the capacity of the queueing network will be arbitrarily close to the computed upper bound. The performance of such policies is studied using numerical examples.
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Server Assignment policies for maximizing the steady state throughput of finite queueing systems
Management Science, 2001Co-Authors: Sigrun Andradottir, Hayriye Ayhan, Douglas G DownAbstract:For a system of finite queues, we study how Servers should be assigned dynamically to stations in order to obtain optimal (or near-optimal) long-run average throughput. We assume that travel times between different service facilities are negligible, that each Server can work on only one job at a time, and that several Servers can work together on one job. We show that when the service rates depend only on either the Server or the station (and not both), then all nonidling Server Assignment policies are optimal. Moreover, for a Markovian system with two stations in tandem and two Servers, we show that the optimal policy assigns one Server to each station unless that station is blocked or starved (in which case the Server helps at the other station), and we specify the criterion used for assigning Servers to stations. Finally, we propose a simple Server Assignment policy for tandem systems in which the number of stations equals the number of Servers, and we present numerical results that show that our policy appears to yield near-optimal throughput under general conditions.
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dynamic Server Assignment in a two queue model
Department of Operations Research Statistics and System Theory [BS], 1995Co-Authors: O J Boxma, Douglas G DownAbstract:We consider a polling model of two $M/G/1$ queues, served by a single Server. The service policy for this polling model is of threshold type. Service at queue 1 is exhaustive. Service at queue 2 is exhaustive unless the size of queue 1 reaches some level $T$ during a service at queue 2; in the latter case the Server switches to queue 1 at the end of that service. Both zero- and nonzero switchover times are considered. We derive exact expressions for the joint queue length distribution at customer departure epochs, and for the steady-state queue-length and sojourn time distributions. In addition, we supply a simple and very accurate approximation for the mean queue lengths, which is suitable for optimization purposes.
Abdullah Konak - One of the best experts on this subject based on the ideXlab platform.
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a game theoretic genetic algorithm for the reliable Server Assignment problem under attacks
Computers & Industrial Engineering, 2015Co-Authors: Abdullah Konak, Sadan Kulturelkonak, Lawrence V SnyderAbstract:We introduce the reliable Server Assignment problem under attacks.A bi-level modeling framework with two decision makers is used.We propose a Game-Theoretic Genetic Algorithm.A simulation method is developed to evaluate service reliability under attacks.Computational studies confirm the effectiveness of the proposed approach. We introduce the reliable Server Assignment problem considering attacks, which seeks to choose the locations of Servers on a network in order to maximize the network reliability that results from a worst-case attack on the edges of the network. The problem is formulated on an unreliable network such that edges are subject to fail independently, and attacks increase the probability of failure for the attacked network edges. The reliability is measured by the critical service rate, which equals the probability that at least a fraction α of the nodes in the network are connected to a Server. We model this problem as a bi-level optimization problem, with the network designer acting as the leader and the attacker acting as the follower. The problem is very difficult to solve, both because of its bi-level structure and because simply evaluating the critical service rate for a single network configuration and attack is NP-hard. We propose a novel Game-Theoretic Genetic Algorithm (GTGA) that simultaneously maintains two populations, one for each player, which interact through a joint payoff matrix. We benchmark the GTGA against a more straightforward Nested GA (NGA) and find that the GTGA significantly outperforms the NGA in terms of solution quality with nearly identical CPU times. We also introduce an efficient simulation method to estimate the reliability for a given set of Servers and integrate this into the GAs. We contribute to the literature on the reliable Server Assignment problem, as well as introducing a novel algorithmic approach that can be adapted to other bi-level optimization problems.
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Reliable Server Assignment in Networks Using Nature Inspired Metaheuristics
IEEE Transactions on Reliability, 2011Co-Authors: Abdullah Konak, Sadan Kulturel-konakAbstract:In this paper, a reliable Server Assignment problem in networks is defined as determining a deployment of identical Servers to maximize a measure of service availability, and solved using nature-inspired metaheuristic approaches, namely Ant Colony Optimization, Particle Swarm Optimization, and Clonal Selection Principle of Artificial Immune Systems. In networks, the communication between a client and a Server might be interrupted because the Server itself is offline or unreachable as a result of catastrophic network failures. Therefore, it is very important to deploy Servers at critical network nodes so that the reliability of the system is maximized. A new reliability measure, called critical service rate, is defined to evaluate alternative Server Assignments with respect to the network's ability to provide services in the case of catastrophic component failures. The structure of the optimal Server Assignments is studied, and the performances of three nature inspired metaheuristics are investigated in a rigorous experimental study. Based on the computational studies, their advantages and disadvantages are discussed.
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simulation optimization embedded particle swarm optimization for reliable Server Assignment
Winter Simulation Conference, 2010Co-Authors: Sadan Kulturelkonak, Abdullah KonakAbstract:A reliable Server Assignment (RSA) problem in networks is defined as determining a deployment of identical Servers to maximize a measure of service availability. In networks, the communication between a client and a Server might be interrupted since the Server itself is offline or unreachable as a result of catastrophic network failures. In this paper, a novel simulation optimization approach is developed based on a Monte Carlo (MC) simulation and embedded into Particle Swarm Optimization (PSO) to solve the RSA problem. The experimental results show that the simulation optimization embedded PSO is an effective heuristic method.
Hayriye Ayhan - One of the best experts on this subject based on the ideXlab platform.
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dynamic Server Assignment with task dependent Server synergy
IEEE Transactions on Automatic Control, 2015Co-Authors: Xinchang Wang, Sigrun Andradottir, Hayriye AyhanAbstract:We study tandem queueing systems with finite buffers in which Servers work more efficiently in teams than on their own and the synergy among collaborating Servers can be task-dependent. Our goal is to determine the dynamic Server Assignment policy that maximizes the long-run average throughput. When each Server works with the same ability at each task that she/he is assigned to, we show that any nonidling policy where all Servers work in teams of two or more at all times is optimal. On the other hand, when the Server abilities are task-dependent, we show that for Markovian systems with two stations and two Servers, depending on the synergy among the Servers, the optimal policy either assigns the two Servers to different stations when possible, or lets them work in a team at all times. Finally, for larger Markovian systems, we provide sufficient conditions that guarantee that the optimal policy has all Servers working together at all times.
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robustness of efficient Server Assignment policies to service time distributions in finite buffered lines
Naval Research Logistics, 2010Co-Authors: Eser Kirkizlar, Sigrun Andradottir, Hayriye AyhanAbstract:We study the Assignment of flexible Servers to stations in tandem lines with service times that are not necessarily exponentially distributed. Our goal is to achieve optimal or near-optimal throughput. For systems with infinite buffers, it is already known that the effective Assignment of flexible Servers is robust to the service time distributions. We provide analytical results for small systems and numerical results for larger systems that support the same conclusion for tandem lines with finite buffers. In the process, we propose Server Assignment heuristics that perform well for systems with different service time distributions. Our research suggests that policies known to be optimal or near-optimal for Markovian systems are also likely to be effective when used to assign Servers to tasks in non-Markovian systems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010
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dynamic Server allocation for queueing networks with flexible Servers
Operations Research, 2003Co-Authors: Sigrun Andradottir, Hayriye Ayhan, Douglas G DownAbstract:This paper is concerned with the design of dynamic Server Assignment policies that maximize the capacity of queueing networks with flexible Servers. Flexibility here means that each Server may be capable of performing service at several different classes in the network. We assume that the interarrival times and the service times are independent and identically distributed, and that routing is probabilistic. We also allow for Server switching times, which we assume to be independent and identically distributed. We deduce the value of a tight upper bound on the achievable capacity by equating the capacity of the queueing network model with that of a limiting deterministic fluid model. The maximal capacity of the deterministic model is given by the solution to a linear programming problem that also provides optimal allocations of Servers to classes. We construct particular Server Assignment policies, called generalized round-robin policies, that guarantee that the capacity of the queueing network will be arbitrarily close to the computed upper bound. The performance of such policies is studied using numerical examples.
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Server Assignment policies for maximizing the steady state throughput of finite queueing systems
Management Science, 2001Co-Authors: Sigrun Andradottir, Hayriye Ayhan, Douglas G DownAbstract:For a system of finite queues, we study how Servers should be assigned dynamically to stations in order to obtain optimal (or near-optimal) long-run average throughput. We assume that travel times between different service facilities are negligible, that each Server can work on only one job at a time, and that several Servers can work together on one job. We show that when the service rates depend only on either the Server or the station (and not both), then all nonidling Server Assignment policies are optimal. Moreover, for a Markovian system with two stations in tandem and two Servers, we show that the optimal policy assigns one Server to each station unless that station is blocked or starved (in which case the Server helps at the other station), and we specify the criterion used for assigning Servers to stations. Finally, we propose a simple Server Assignment policy for tandem systems in which the number of stations equals the number of Servers, and we present numerical results that show that our policy appears to yield near-optimal throughput under general conditions.
