Search results for "Distributed"
showing 10 items of 1260 documents
Bayesian Game Model: Demand Side Management for Residential Consumers with Electric Vehicles
2019
This paper proposes the game theory enabled approach for the integration of electric vehicles for demand side management (DSM). Demand side management is very complex with conventional approaches. In order to the efficient mechanism of a game theory enabled approach may resolve the complexity. With the increased penetration level of electric vehicles it will be difficult to control grid-to-vehicle integration. The Bayesian game theory provides the solution of such problems in an organized manner. In the presence of distributed energy resources, Electric vehicles will play an important role to stabilize the grid integration. Electric Vehicles consume power during off-peak load period and inj…
Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems
2014
The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This article presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations…
Modeling and Simulation of Network-on-Chip Systems with DEVS and DEUS
2013
Networks on-chip (NoCs) provide enhanced performance, scalability, modularity, and design productivity as compared with previous communication architectures for VLSI systems on-chip (SoCs), such as buses and dedicated signal wires. Since the NoC design space is very large and high dimensional, evaluation methodologies rely heavily on analytical modeling and simulation. Unfortunately, there is no standard modeling framework. In this paper we illustrate how to design and evaluate NoCs by integrating the Discrete Event System Specification (DEVS) modeling framework and the simulation environment called DEUS. The advantage of such an approach is that both DEVS and DEUS support modularity—the fo…
"Table 19" of "Measurement of event shape and inclusive distributions at s**(1/2) = 130-GeV and 136-GeV."
1997
2-jet rate for the Durham Algorithm.
"Table 27" of "Tuning and test of fragmentation models based on identified particles and precision event shape data."
1996
Differential 2-jet rate for the Durham Algorithm. Corrected to final state particles. YCUT is the jet finding cutt-off parameter.
Hierarchies of probabilistic and team FIN-learning
2001
AbstractA FIN-learning machine M receives successive values of the function f it is learning and at some moment outputs a conjecture which should be a correct index of f. FIN learning has two extensions: (1) If M flips fair coins and learns a function with certain probability p, we have FIN〈p〉-learning. (2) When n machines simultaneously try to learn the same function f and at least k of these machines output correct indices of f, we have learning by a [k,n]FIN team. Sometimes a team or a probabilistic learner can simulate another one, if their probabilities p1,p2 (or team success ratios k1/n1,k2/n2) are close enough (Daley et al., in: Valiant, Waranth (Eds.), Proc. 5th Annual Workshop on C…
Balls into non-uniform bins
2014
Balls-into-bins games for uniform bins are widely used to model randomized load balancing strategies. Recently, balls-into-bins games have been analysed under the assumption that the selection probabilities for bins are not uniformly distributed. These new models are motivated by properties of many peer-to-peer (P2P) networks, which are not able to perfectly balance the load over the bins. While previous evaluations try to find strategies for uniform bins under non-uniform bin selection probabilities, this paper investigates heterogeneous bins, where the "capacities" of the bins might differ significantly. We show that heterogeneous environments can even help to distribute the load more eve…
Randomized renaming in shared memory systems.
2021
Abstract Renaming is a task in distributed computing where n processes are assigned new names from a name space of size m . The problem is called tight if m = n , and loose if m > n . In recent years renaming came to the fore again and new algorithms were developed. For tight renaming in asynchronous shared memory systems, Alistarh et al. describe a construction based on the AKS network that assigns all names within O ( log n ) steps per process. They also show that, depending on the size of the name space, loose renaming can be done considerably faster. For m = ( 1 + ϵ ) ⋅ n and constant ϵ , they achieve a step complexity of O ( log log n ) . In this paper we consider tight as well as loos…
Frequency Assignment and Multicoloring Powers of Square and Triangular Meshes
2005
The static frequency assignment problem on cellular networks can be abstracted as a multicoloring problem on a weighted graph, where each vertex of the graph is a base station in the network, and the weight associated with each vertex represents the number of calls to be served at the vertex. The edges of the graph model interference constraints for frequencies assigned to neighboring stations. In this paper, we first propose an algorithm to multicolor any weighted planar graph with at most $\frac{11}{4}W$ colors, where W denotes the weighted clique number. Next, we present a polynomial time approximation algorithm which garantees at most 2W colors for multicoloring a power square mesh. Fur…
Online Scheduling of Task Graphs on Heterogeneous Platforms
2020
Modern computing platforms commonly include accelerators. We target the problem of scheduling applications modeled as task graphs on hybrid platforms made of two types of resources, such as CPUs and GPUs. We consider that task graphs are uncovered dynamically, and that the scheduler has information only on the available tasks, i.e., tasks whose predecessors have all been completed. Each task can be processed by either a CPU or a GPU, and the corresponding processing times are known. Our study extends a previous $4\sqrt{m/k}$ 4 m / k -competitive online algorithm by Amaris et al. [1] , where $m$ m is the number of CPUs and $k$ k the number of GPUs ( $m\geq k$ m ≥ k ). We prove that no online…