A fundamental problem that confronts peer-to-peer applications is to efficiently locate the node that stores a particular data item. This paper presents Chord, a distributed lookup protocol that addresses this problem. Chord provides support for just one operation: given a key, it maps the key onto a node. Data location can be easily implemented on top of Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. Chord adapts efficiently as nodes join and leave the system, and can answer queries even if the system is continuously changing. Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.

Chord: A scalable peer-to-peer lookup service for internet applications

This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer ap- plications. Pastry performs application-level routing and object location in a po- tentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry's scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.

/pdf/pastry-scalable-decentralized-object-location-and-routing-1u2equbbbp.pdf

Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems

Hash tables - which map "keys" onto "values" - are an essential building block in modern software systems. We believe a similar functionality would be equally valuable to large distributed systems. In this paper, we introduce the concept of a Content-Addressable Network (CAN) as a distributed infrastructure that provides hash table-like functionality on Internet-like scales. The CAN is scalable, fault-tolerant and completely self-organizing, and we demonstrate its scalability, robustness and low-latency properties through simulation.

https://www.cs.sjtu.edu.cn/~gchen/course/acn/Reading/CAN.pdf

A scalable content-addressable network

A review of the geologic history of the Himalayan-Tibetan orogen suggests that at least 1400 km of north-south shortening has been absorbed by the orogen since the onset of the Indo-Asian collision at about 70 Ma. Significant crustal shortening, which leads to eventual construction of the Cenozoic Tibetan plateau, began more or less synchronously in the Eocene (50–40 Ma) in the Tethyan Himalaya in the south, and in the Kunlun Shan and the Qilian Shan some 1000–1400 km in the north. The Paleozoic and Mesozoic tectonic histories in the Himalayan-Tibetan orogen exerted a strong control over the Cenozoic strain history and strain distribution. The presence of widespread Triassic flysch complex in the Songpan-Ganzi-Hoh Xil and the Qiangtang terranes can be spatially correlated with Cenozoic volcanism and thrusting in central Tibet. The marked difference in seismic properties of the crust and the upper mantle between southern and central Tibet is a manifestation of both Mesozoic and Cenozoic tectonics. The form...

Geologic Evolution of the Himalayan-Tibetan Orogen

Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.

/pdf/dynamo-amazon-s-highly-available-key-value-store-2ozfk43ou4.pdf

Dynamo: amazon's highly available key-value store

In general, the dataset of volunteer recommendation systems shows the sparsity, while a volunteer recommendation system required performing the function of recommending voluntary activities interesting to a specific volunteer. To our knowledge, there exists no such kind of recommendation systems. To begin with, this paper firstly presents an analysis of a dataset collected from a real volunteering application website and discovered two features: the locations between the volunteers and the voluntary activities are in close proximity, and the resulting graph which describes the participation relationship between volunteers and voluntary activities is a kind of bipartite, showing many small communities inside it. We call the first discovery 'geographically closely participating', and the second discovery 'participating together'. Based on these findings, a rating matrix, featuring a matching method for the recommendation algorithm has been constructed. Secondly, we propose a weighted Personal Rank algorithm to implement the required functions of a volunteer recommendation system by employing the registration information of volunteers and voluntary activities. This includes the volunteers' preferences, activities and location etc. The comparison of proposed method with the rating matrix-based collaborative filter algorithm and the Personal Rank algorithms shows that our proposed method outperforms them.

Common Features Based Volunteer and Voluntary Activity Recommendation Algorithm

Knowledge element relation extraction is to find predefined relations between pairs of knowledge elements from text documents. As a novel form for organization and management of knowledge resources, knowledge element relation can be utilized to establish knowledge navigation system, knowledge retrieval system and collaborative knowledge construction system. In this paper, we employ conditional random fields (CRFs) to extract relations between knowledge elements from natural language documents by treating the relation extraction task as a sequence labeling problem. We first introduce three rules to generate candidate relation instances, and then incorporate various features including terms, semantic type, distance and context information to represent candidate relation instances. Experimental evaluation shows that our method achieves better performance than previous work. It also indicates that CRFs outperform other probabilistic models i.e. hidden Markov model and maximum entropy, and show effective in knowledge element relation extraction.

Knowledge element relation extraction using conditional random fields

Serverless computing is a new cloud service model that reduces both cloud providers' and consumers' costs through extremely agile development, operation, and charging mechanisms and has been widely applied since its emergence. Nevertheless, some characteristics of serverless computing, such as fragmented application boundaries, have raised new security challenges. Considerable literature work has been committed to addressing these challenges. Commercial and open-source serverless platforms implement many security measures to enhance serverless environments. This paper presents the first survey of serverless security that considers both literature work and industrial security measures. We summarize the primary security challenges, analyze corresponding solutions from the literature and industry, and identify potential research opportunities. Then, we conduct a gap analysis of the academic and industrial solutions as well as commercial and open-source serverless platforms' security capabilities, and finally, we present a complete picture of current serverless security research.

Securing Serverless Computing: Challenges, Solutions, and Opportunities.

With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models.

/pdf/an-improved-hybrid-transfer-learning-based-deep-learning-1h43udih.pdf

An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction

Internet fault diagnosis is important to end users, overlay network service providers (like Akamai), and Internet service providers (ISPs). For example, with Internet fault diagnosis tools, users can choose more reliable ISPs. However, The modern Internet is heterogeneous and largely unregulated, which renders the Internet diagnosis an increasingly challenging problem. Though several router-based Internet diagnosis tools have been proposed [1, 2], these tools generally need special support from routers. For example, Tulip [1] requires the routers to support continuous IP-ID for generated ICMP packets. Also these ICMP-based tools are subject to ICMP rate limiting and are sensitive to crosstraffic. In contrast, many recently-developed tools for Internet Tomography use signal processing and statistical approaches to infer link level properties [3, 4, 5, 6] or shared congestion [7] based on end-to-end measurements of IP routing paths. The advantage of statistical inference is their fine diagnosis granularity, i.e., they infer the property of each virtual link (a consecutive subpath of an IP path with no branches [5]). However, the accuracy of the predicted link properties is subject to uncertainty in the model assumptions. Since true multicast does not exist in the Internet, many mulitcast-based tomography approaches [3, 4] have to use unicast for approximation, such as [8] and [9]. Thus the accuracy of the probe measurements heavily depends on the cross traffic in the network, and there is no guarantee of their accuracy.

Yan Chen

Papers

Common Features Based Volunteer and Voluntary Activity Recommendation Algorithm

Knowledge element relation extraction using conditional random fields

Securing Serverless Computing: Challenges, Solutions, and Opportunities.

An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction

Towards deterministic network diagnosis