Distributed computational grids depend on TCP to ensure reliable end-to-end communication between nodes across the wide-area network (WAN). Unfortunately, TCP performance can be abysmal even when buffers on the end hosts are manually optimized. Recent studies blame the self-similar nature of aggregate network traffic for TCP's poor performance because such traffic is not readily amenable to statistical multiplexing in the Internet, and hence computational grids. In this paper we identify a source of self-similarity previously ignored, a source that is readily controllable--TCP. Via an experimental study, we examine the effects of the TCP stack on network traffic using different implementations of TCP. We show that even when aggregate application traffic ought to smooth out as more applications' traffic are multiplexed, TCP induces burstiness into the aggregate traffic loud, thus adversely impacting network performance. Furthermore, our results indicate that TCP performance will worsen as WAN speeds continue to increase.

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The failure of tcp in high-performance computational grids

In this paper, we study the embedding of cycles in twisted cubes. It has been proven in the literature that, for any integer l, 4 ≤ l ≤ 2 n , a cycle of length l can be embedded with dilation 1 in an n-dimensional twisted cube, n ≥ 3. We obtain a stronger result of embedding of cycles with edge-pancyclic. We prove that, for any integer l, 4 ≤ l ≤ 2 n , and a given edge (x, y) in an n-dimensional twisted cube, n ≥ 3, a cycle C of length l can be embedded with dilation 1 in the n-dimensional twisted cube such that (x, y) is in C in the twisted cube. Based on the proof of the edge-pancyclicity of twisted cubes, we further provide an O (l log l+ n 2 + nl) algorithm to find a cycle C of length l that contains (u, v) in T Q n for any (u, v) ∈ E(TQ n ) and any integer l with 4 ≤ l ≤ 2 n .

Embedding of Cycles in Twisted Cubes with Edge-Pancyclic

This paper derives the conditional node connectivity of the k-ary n-cube interconnection network under the condition of forbidden faulty sets (i.e. assuming that each non-faulty processor has at least one non-faulty neighbor). It is shown that under this condition and for k≥4 and n≥2, the k-ary n-cube, whose connectivity is 2n, can tolerate up to 4n-3 faulty nodes without becoming disconnected. The conditional node connectivity in this case is therefore 4n-2. For k=3 and n≥2 the established conditional node connectivity is 4n-3. The result for the remaining smaller values of k and n are also obtained.

The Conditional Node Connectivity of the k-ary n-Cube.

Security and Blockchain Convergence with Internet of Multimedia Things: Current Trends, Research Challenges and Future Directions.

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user's data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. What's more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis in Crowdsensing.

Privacy preserving classification on local differential privacy in data centers

The 3-ary n-cube, denoted as $$ {Q}_n^3 $$
 , is an important interconnection network topology proposed for parallel computers, owing to its many desirable properties such as regular and symmetrical structure, and strong scalability, among others. In this paper, we first obtain an exact formula for the minimum wirelength to embed $$ {Q}_n^3 $$
 into grids. We then propose a load balancing algorithm for embedding $$ {Q}_n^3 $$
 into a square grid with minimum dilation and congestion. Finally, we derive an O(N2) algorithm for embedding $$ {Q}_n^3 $$
 into a gird with balanced communication, where N is the number of nodes in $$ {Q}_n^3 $$
 . Simulation experiments are performed to verify the total wirelength and evaluate the network cost of our proposed embedding algorithm.

Optimally Embedding 3-Ary n -Cubes into Grids

Graph embedding is an important technology in simulating parallel structures and designing VLSI layout Hypercube is one of the most significant interconnection networks in parallel computing systems The exchanged hypercube is an important variant of the hypercube, which is obtained by systematically deleting edges from a hypercube It not only retains several valuable and desirable properties of the hypercube, but also has lower hardware cost In this paper, we first give an exact formula of minimum wirelength of exchanged hypercube layout into a grid Furthermore, we propose an O(N) algorithm for embedding exchanged hypercube into a gird with load 1, expansion 1 and minimum wirelength and derive a linear layout of exchanged hypercube with minimum number of tracks and efficient layout areas Finally, we present simulation experiments of our embedding algorithm on network overhead and total wirelength, which conduce to estimate the total wirelength and chip area

An efficient algorithm for embedding exchanged hypercubes into grids

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm

Weibei Fan

Papers

Privacy preserving classification on local differential privacy in data centers

Optimally Embedding 3-Ary n -Cubes into Grids

An efficient algorithm for embedding exchanged hypercubes into grids

Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks

Structure Fault-Tolerance of the Generalized Hypercube