Stackelberg Routing in Arbitrary Networks

We study the degradation in network performance caused by the selfish behavior of noncooperative network users. We consider a directed network in which each edge possesses a latency function describing the common latency incurred by all traffic on the edge as a function of the edge congestion. Given a rate of traffic between each pair of nodes in the network, we aspire toward an assignment of traffic to paths in which the sum of all travel times (the total latency) is minimized; however, in many settings network users are free to route their traffic in a selfish manner, without regard to the total latency. We therefore assume that each network user routes its traffic on the minimum-latency path available to it, given the network congestion caused by the other users. In general such a selfishly motivated assignment of traffic to paths (a Nash equilibrium) will not minimize the total latency; hence, selfish behavior carries the cost of decreased network performance. We quantify this degradation in network performance via the price of anarchy, defined as the worst possible ratio between the total latency of a Nash equilibrium and of a minimum-latency routing of the traffic.In this paper, we show that the underlying network topology plays no role in the determination of the price of anarchy. Specifically, we show that under weak hypotheses on the class of allowable edge latency functions, the worst-case ratio between the total latency of a Nash equilibrium and of a minimum-latency routing for any multicommodity flow network is achieved by a single-commodity instance on a set of parallel links. In the special case where the class of allowable latency functions includes all of the constant functions, we prove that a network with only two parallel links suffices to achieve the worst-possible ratio. Informally, these results imply that the inefficiency inherent in a flow at Nash equilibrium stems from the inability of selfish users to discern which of two competing routes is superior and not from the topological complexity arising from the diverse intersections of many paths belonging to different commoditie.Our proof techniques also give powerful methods for computing the price of anarchy with respect to an arbitrary class of latency functions. We apply these methods to function classes that have been well studied in the literature (such as degree-bounded polynomials and functions of the form $\ell(x) = (u—x)—1} that are used to model edges with capacity u), thereby achieving the first tight analyses of the price of anarchy for significant classes of latency functions outside the class of linear functions.

Stackelberg thresholds in network routing games or the value of altruism

We investigate the efficiency of some Stackelberg strategies in congestion games with affine latency functions. A Stackelberg strategy is an algorithm that chooses a subset of players and assigns them a prescribed strategy with the purpose of mitigating the detrimental effect that the selfish behavior of the remaining uncoordinated players may cause to the overall performance of the system. The efficiency of a Stackelberg strategy is measured in terms of the price of anarchy of the pure Nash equilibria they induce. Three Stackelberg strategies, namely Largest Latency First, Cover and Scale, were already considered in the literature and non-tight upper and lower bounds on their price of anarchy were given. We reconsider these strategies and provide the exact bound on the price of anarchy of both Largest Latency First and Cover and a better upper bound on the price of anarchy of Scale.

We investigate the efficiency of some Stackelberg strategies in congestion games with affine latency functions. A Stackelberg strategy is an algorithm that chooses a subset of players and assigns them a prescribed strategy with the purpose of mitigating the detrimental effect that the selfish behavior of the remaining uncoordinated players may cause to the overall performance of the system. The efficiency of a Stackelberg strategy is measured in terms of the price of anarchy of the pure Nash equilibria they induce. Three Stackelberg strategies, namely Largest Latency First, Cover and Scale, were already considered in the literature and non-tight upper and lower bounds on their price of anarchy were given. We reconsider these strategies and provide the exact bound on the price of anarchy of both Largest Latency First and Cover and a better upper bound on the price of anarchy of Scale.

In this paper, we propose a distributed algorithm for scheduling divisible loads originating from single site on arbitrary networks. We first propose a mathematical model and formulate the scheduling problem as an optimization problem with an objective to minimize the processing time of the loads. A number of theoretical results on the solution of the optimization problem are derived. On the basis of these results, we propose our algorithm using the concept of virtual routing. The proposed algorithm has three attractive features - distributed working style, simple structure, in terms of implementation ease, and offers generalized approach for handling divisible load scheduling for any network topology. This is the first time in the divisible load scheduling literature that a distributed strategy is attempted.

Closed-Loop Strategy for Stackelberg Game Problem with Incomplete Information Structures

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm.

Energy conservation and network performance have become two of the most important issues in data center as the scale of cloud services continues growing. Recent researches usually consider these two issues separately. Energy conservation mainly deals with hosts, which reduces total energy consumption by consolidating virtual machine(VM)s to fewer hosts, and network performance mainly deals with network scalability and energy efficiency, which improves data center network(DCN) scalability by applying new network topologies or routing schemes and improves DCN energy efficiency by consolidating trafile. In this paper, we jointly consider these two issues and define Combined VM Consolidation (CVC) problem. We prove that CVC is NP-complete and is inapproximable by a factor of 3/2 e unless P = NP. Next, we propose NICE: Network-aware VM Consolidation scheme for Energy Conservation in Data CEnter to solve CVC. Instead of taking the unrealistic hypothesis that migration cost is negligible, a common assumption in most literatures, we precisely analyze VM migration cost according to real-trace experiments in a 6-server testbed via VMware. Massive simulations validate the efficiency of NICE, In all, to the best of our knowledge, we arc the first work to combine VM consolidation with network optimization and migration cost.

4 - Recursive Enumerability

Sensitivity of a general class of shape functionals to topological changes

We look at the scenario of having to route a continuous rate of traffic from a source node to a sink node in a network, where the objective is to maximize throughput. This is of interest, e.g., for providers of streaming content in communication networks. The overall path latency, which was relevant in other non-cooperative network routing games such as the classic Wardrop model, is of lesser concern here.To that end, we define bottleneck games with splittable traffic where the throughput on a path is inversely proportional to the maximum latency of an edge on that very path—the bottleneck latency. Therefore, we define a Wardrop equilibrium as a traffic distribution where this bottleneck latency is at minimum on all used paths. As a measure for the overall system well-being—called social cost—we take the weighted sum of the bottleneck latencies of all paths.Our main findings are as follows: First, we prove social cost of Wardrop equilibria on series parallel graphs to be unique. Even more, for any graph whose subgraph induced by all simple start-destination paths is not series parallel, there exist games having equilibria with different social cost. For the price of stability, we give an independence result with regard to the network topology. Finally, our main result is giving a new exact price of stability for Wardrop/bottleneck games on parallel links with M/M/1 latency functions. This result is at the same time the exact price of stability for bottleneck games on general graphs.KeywordsNash EquilibriumSocial CostSink NodeLatency FunctionMaximum LatencyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Our starting point are several general classes of real functions defined on the unit square satisfying some basic properties such as a boundary condition or several types of monotonicity and continuity. Applying to these functions some parameterized transformations and other constructions such as the transpose and flipping (which describe different aspects of symmetry) and truncation, we ask for conditions yielding (again) a bivariate copula. Some of these transformations are involutive (on one or more classes of functions), others are not even injective, and occasionally they induce additional properties, yielding, e.g., a (quasi-)copula. For several typical scenarios we identify the (not necessarily convex) sets of parameters leading to a copula and conditions imposing a minimal set of parameters.

In this paper, we introduce notable Jensen–Mercer inequality for a general class of convex functions, namely uniformly convex functions. We explore some interesting properties of such a class of functions along with some examples. As a result, we establish Hermite–Jensen–Mercer inequalities pertaining uniformly convex functions by considering the class of fractional integral operators. Moreover, we establish Mercer–Ostrowski inequalities for conformable integral operator via differentiable uniformly convex functions. Finally, we apply our inequalities to get estimations for normal probability distributions (Gaussian distributions).

We consider a learning model in which each element of a class of recursive functions is to be identified in the limit by a computable strategy. Given gradually growing initial segments of the graph of a function, the learner is supposed to generate a sequence of hypotheses converging to a correct hypothesis. The term correct means that the hypothesis is an index of the function to be learned in a given numbering. Restriction of the basic definition of learning in the limit yields several inference criteria, which have already been compared with respect to their learning power.The scope of uniform learning is to synthesize appropriate identification strategies for infinitely many classes of recursive functions by a uniform method, i.e. a kind of meta-learning is considered. In this concept we can also compare the learning power of several inference criteria. If we fix a single numbering to be used as a hypothesis space for all classes of recursive functions, we obtain results similar to the non-uniform case. This hierarchy of inference criteria changes, if we admit different hypothesis spaces for different classes of functions. Interestingly, in uniform identification most of the inference criteria can be separated by collections of finite classes of recursive functions.

In this work we study weighted network congestion games with player-specific latency functions where selfish players wish to route their traffic through a shared network. We consider both the case of splittable and unsplittable traffic. Our main findings are as follows. For routing games on parallel links with linear latency functions, we introduce two new potential functions for unsplittable and for splittable traffic, respectively. We use these functions to derive results on the convergence to pure Nash equilibria and the computation of equilibria. For several generalizations of these routing games, we show that such potential functions do not exist. We prove tight upper and lower bounds on the price of anarchy for games with polynomial latency functions. All our results on the price of anarchy translate to general congestion games.