To take full advantage of the available network capacity, connections need to be able to use multiple paths to route their packets. Max-min fairness (MMF) can be effectively applied to single-path networks, but comput...
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To take full advantage of the available network capacity, connections need to be able to use multiple paths to route their packets. Max-min fairness (MMF) can be effectively applied to single-path networks, but computing MMF rates in multipath networks requires solving a series of linear programming (LP) problems with high computational cost. Thus, a relaxation of MMF has been proposed, namely, upward max-min fairness (UMMF), which can be solved by simple combinatorial algorithms. Current proposals carry out incremental approximations emulating the waterfilling algorithm, which inherently establishes a dependency between the time required to achieve the optimal solution and the capacity of the links. Thus, the more capacity the network has, the less efficient the algorithms are. We defined the concept of the saturation level as the basis for the computation of fair shares. We developed the first centralized algorithm based on this concept, which we call c-SLEN. Unlike its predecessors, its convergence time does not depend on network capacity, and it does not incur link oversaturation. Based on c-SLEN, we derived d-SLEN, a distributed protocol that does not need to maintain per-subflow information in routers and guarantees constant processing time for control packets, making it a good candidate for practical use. Finally, through extensive simulations, we showed that d-SLEN is faster, lighter, and more accurate than its counterparts. Owing to its accuracy and convergence speed, it is able to maintain the size of link queues at minimal values at all times, thus proactively avoiding network congestion.
This paper analyses how the symmetry of a processor network influences the existence of a solution for the network orientation problem. The orientation of hypercubes and tori is the problem of assigning labels to each...
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This paper analyses how the symmetry of a processor network influences the existence of a solution for the network orientation problem. The orientation of hypercubes and tori is the problem of assigning labels to each link of each processor, in such a way that a sense of direction is given to the network. In this paper the problem of network orientation for these two topologies is studied under the assumption that the network contains a single leader, under the assumption that the processors possess unique identities, and under the assumption that the network is anonymous. The distinction between these three models is considered fundamental in distributed computing. It is shown that orientations can be computed by deterministic algorithms only when either a leader or unique identities are available. Orientations can be computed for anonymous networks by randomized algorithms, but only when the number of processors is known. When the number of processors is not known, even randomized algorithms cannot comp...
The design and verification of fault-tolerant distributed algorithms is a complicated task. Usually, the proof of correctness is done manually, and thus depends on the skill of the prover. Using automated verification...
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The design and verification of fault-tolerant distributed algorithms is a complicated task. Usually, the proof of correctness is done manually, and thus depends on the skill of the prover. Using automated verification methods, such as model checking, can greatly simplify the verification. However, model checking of distributed algorithms is often intractable because of the state-explosion problem. In this paper we present a novel approach to verification of quorum-based distributed register emulation algorithms with undetectable crash failures of processes. Our approach is based on projection and abstraction and allows us to reduce the task of model-checking the whole system to fair model-checking of subsystems consisting of a constant number of processes. Our method is highly scalable and can be applied to a large class of algorithms. Aside from efficient verification, it can also be used for finding redundancies in existing algorithms.
We propose a novel approach for shortest path routing in wireless mobile networks. The approach makes use of n mobile agents initially launched from n mobile nodes forming the network. The agents move randomly from no...
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We propose a novel approach for shortest path routing in wireless mobile networks. The approach makes use of n mobile agents initially launched from n mobile nodes forming the network. The agents move randomly from node to node and update routing information as they go. The approach is presented in this paper with two protocols. Both of them exhibit good performance in terms of the network and computing resource consumptions. The first protocol relies on independent mobile agents and imposes a minimum bandwidth requirement on individual mobile agents. Each agent carries the link state of its creator and this information remains unchanged except when the mobile agent returns to the home node. The second protocol is a refinement of the first protocol, with some form of interaction between the mobile agents. Each agent maintains the routing table of its creator instead of link state. The randomly walking agents spread the update information and compute the shortest paths via exchanging network state information between the routing tables they carry and the routing tables at the nodes they traverse. The correctness of the protocols is proven. Our analysis shows that the agent cooperation improves the system performance when dealing with topology and link cost changes.
Abstract In this paper we propose a distributed algorithm for solving separable convex optimization problems on graphs arising for example from estimation and control in networks. We derive a primal-dual decomposition...
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Abstract In this paper we propose a distributed algorithm for solving separable convex optimization problems on graphs arising for example from estimation and control in networks. We derive a primal-dual decomposition algorithm for solving this type of separable optimization problems in a distributed fashion given restrictions on the communication topology. The proposed algorithm is based on consensus principles in combination with local subgradient updates for the primal-dual variables. The main novelty of our algorithm consists in the way the weights in the consensus process are updated using arguments from optimization theory, while in most of the existing distributed algorithms based on consensus principles the weights have to be tuned.
Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably ef...
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Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably efficient, and (3) able to dynamically adapt to the changes to the emergency situation. Four variations of the resource allocation problem, namely maximum rate, minimum clearance time, maximum social welfare, and proportional fair resource allocation, are modeled and compared. It is shown that the optimal answer to each problem can be found efficiently. Additionally, a distributed and dynamic algorithm based on the Lagrangian dual approach, called PFD 2 A, is developed to find the proportional fair allocation of resources. Numerical results for a sample scenario are presented.
The composition of Cloud Services to satisfy customer requirements is a complex task, owing to the huge number of services that are currently available. The advent of Big Data and Internet of Things (IoT), which rely ...
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The composition of Cloud Services to satisfy customer requirements is a complex task, owing to the huge number of services that are currently available. The advent of Big Data and Internet of Things (IoT), which rely on Cloud resources for better performances and scalability, is pushing researchers to find new solutions to the Cloud Services composition problem. In this paper a semantic- based representation of Application Patterns and Cloud Services is presented, with an example of its use in a typical distributed application, which shows how the proposed approach can be successfully employed for the discovery and composition of Cloud Services..
The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed ...
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The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed to be very efficient on problems where components have a sparse interaction. This work explores a step further in this direction. The idea is to extend the celebrated turbo algorithms, extremely successful to decode large-scale sparse error correcting codes. The paper proposes an adaptation of this technique to the setting of cost-optimal factored planning, and illustrates its behavior on large randomly generated systems.
In dynamic distributed systems, the topology of networks changes over time which makes difficult the design and much harder the proof of distributed algorithms. These unavoidable changes of the topology make the elect...
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In dynamic distributed systems, the topology of networks changes over time which makes difficult the design and much harder the proof of distributed algorithms. These unavoidable changes of the topology make the election and the maintenance of the elected leader a complex task. The maintaining problem is not considered in a static context. To encode distributed algorithms, we adopt the local computation model. distributed algorithms are formally presented by transitions systems. Based on both, the mobile agent paradigm and the local computation model, we present in this paper a distributed algorithm that elects a leader in a tree. A set of topological events that may affect the structure of the tree: we focus on the appearance and the disappearance of places as well as the communication channels. Our goal is to maintain always a tree with a single leader or a forest of trees where each one has his own leader.
We take a new approach to investigate synchronization in networks of coupled oscillators. We show that the coupled oscillator system when restricted to a proper region is a distributed partial primal-dual gradient alg...
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We take a new approach to investigate synchronization in networks of coupled oscillators. We show that the coupled oscillator system when restricted to a proper region is a distributed partial primal-dual gradient algorithm for solving a well-defined convex optimization problem and its dual. We characterize conditions for synchronization solution of the KKT system of the optimization problem, based on which we derive conditions for synchronization equilibrium of the coupled oscillator network. This new approach reduces the hard problem of synchronization of coupled oscillators to a simple problem of verifying synchronization solution of a system of linear equations, and leads to a complete characterization of synchronization condition for the coupled oscillator network in an interesting and practically important region. Our synchronization condition is stated elegantly as the existence of solution for a system of linear equations, of which one best existing synchronization condition is a special sufficient condition case. In addition, we formulate a non-convex optimization problem with the force balance constraint for which the afore convex optimization problem is relaxation, and show that the coupled oscillator system is also a distributed algorithm for solving this non-convex problem. This has interesting implication on exact convex relaxation, and confirms the insight that a physical system usually solves a convex problem even though it may have a non-convex representation.
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