Xia Peisu Young Scholars Forum 2021 is intended to promote the communication between young scholars world wide and exchange novel research ideas and methods in the frontier of computer science. The forum is named after Prof. Xia Peisu, an academician of the Chinese Academy of Sciences, who made a great contribution to the development of computational technologies and participated in building the first computer in China. Prof. Xia Peisu has also founded the first English computer journal of China, i.e., Journal of Computer Science and Technology (JCST), in 1986.

The main topic of Xia Peisu Young Scholars Forum 2021 is “Network, Data and AI”, which aims to discuss the opportunities and challenges in developing the big data, artificial intelligence and next-generation network technologies. With the development of information technologies, we are entering an age of intelligence where network, data and AI have become indispensable elements which are intertwined closely and have huge impact on the human society. The intensive discussions on the three topics in this forum help promote the cross-disciplinary research between these fields. After the forum, we invited the participants to submit their work to JCST. After two rounds of peer-review, eight papers were selected for the Special Section of Xia Peisu Young Scholars Forum 2021.

In this special section, there are three papers on the joint optimization of applications and networking. Traditionally, applications treat networks as a black box, which mainly provides information transport services. Meanwhile, networking optimizations focus on “lower latency and higher throughput” for decades. However, networking's real and only mission should be making applications better, and networking schemes should be jointly optimized with applications. Being aware of occasional packet corruptions' detrimental effects to RDMA-enabled applications, Gao et al.'s work strives to shrink the flow completion time by orders of magnitude. By understanding application semantics from passing-through packets, Dong et al.'s work could improve data-parallel jobs' performance by over 10%. Wang et al.'s work intelligently places energy harvesting nodes that can significantly prolong the lifetime of a sensor service.

Another two papers are related to distributive networking, which has attracted much attention in recent years. Shi et al.'s work focuses on the performance assessment of decentralized clouds and proposes a robust assessment solution RODE. Experiments show that RODE can accurately monitor the performance of cloud providers. Meanwhile, considering distributive platforms, iterative algorithms are promising to analyze large scale data. Yu et al.'s work designs an efficient execution manager Aiter-R, which can be integrated into existing delta-based iterative processing to achieve maximum efficiency, with the proposed group-based iterative execution approach. Experimental results show that Aiter-R outperforms state-of-the-art solutions.

Besides, there are three papers related to the topics of big data and artificial intelligence, including information retrieval, recommendation and text summarization. Wu et al.'s work focuses on the document ranking in information retrieval and investigates how users' information gain accumulates both within a document and across a query session. The proposed model PCGM, which incorporates the document-level and query-level passage cumulative gain, outperforms multiple advanced ranking baselines and the predicted results are highly consistent with users' preferences. Jiang et al.'s work aims to improve the friend recommendation with fine-grained evolving interests and proposes an LPRF-F framework which explores the learning interest tags and time features to predict the user interests. Extensive experiments validate the effectiveness of this work as well as the effects of social influence and cross-domain interest. Jiang et al.'s another paper strives to better evaluate the performance of abstractive summarization models by considering ngram-based semantic information. Empirical results demonstrate the proposed evaluation metrics are well correlated with human judgements.

Five papers of this special section are published in Vol.37, No.4, 2022, and the other three, i.e., the papers of Shi et al. and Jiang et al., will be included in Vol.37, No.5, 2022.

We hope that readers will enjoy this special section. We are grateful to all the paper authors and reviewers for their valuable contributions.

Remote direct memory access (RDMA) has become one of the state-of-the-art high-performance network technologies in datacenters. The reliable transport of RDMA is designed based on a lossless underlying network and cannot endure a high packet loss rate. However, except for switch buffer overflow, there is another kind of packet loss in the RDMA network, i.e., packet corruption, which has not been discussed in depth. The packet corruption incurs long application tail latency by causing timeout retransmissions. The challenges to solving packet corruption in the RDMA network include: 1) packet corruption is inevitable with any remedial mechanisms and 2) RDMA hardware is not programmable. This paper proposes some designs which can guarantee the expected tail latency of applications with the existence of packet corruption. The key idea is controlling the occurring probabilities of timeout events caused by packet corruption through transforming timeout retransmissions into out-of-order retransmissions. We build a probabilistic model to estimate the occurrence probabilities and real effects of the corruption patterns. We implement these two mechanisms with the help of programmable switches and the zero-byte message RDMA feature. We build an ns-3 simulation and implement optimization mechanisms on our testbed. The simulation and testbed experiments show that the optimizations can decrease the flow completion time by several orders of magnitudes with less than 3% bandwidth cost at different packet corruption rates.

Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time (JCT) is a major metric for assessing their effectiveness. How to reduce the JCT for these systems through reasonable scheduling has become a hot issue in both industry and academia. Data skew is a common phenomenon that can compromise the performance of such distributed computing systems. This paper proposes SMART, which can effectively reduce the JCT through handling the data skew during the reducing phase. SMART predicts the size of reduce tasks based on part of the completed map tasks and then enforces largest-first scheduling in the reducing phase according to the predicted reduce task size. SMART makes minimal modifications to the original Hadoop with only 20 additional lines of code and is readily deployable. The robustness and the effectiveness of SMART have been evaluated with a real-world cluster against a large number of datasets. Experiments show that SMART reduces JCT by up to 6.47%, 9.26%, and 13.66% for Terasort, WordCount and InvertedIndex respectively with the Purdue MapReduce benchmarks suite (PUMA) dataset.

Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a wireless sensor network with energy harvesting nodes is very costly. A compromise is upgrading existing networks with energy harvesting technologies. In this paper, we focus on prolonging the lifetime of WSNs with the help of energy harvesting relays (EHRs). EHRs are responsible for forwarding data for sensor nodes, allowing them to become terminals and thus extending their lifetime. We aim to deploy a minimum number of relays covering the whole network. As EHRs have several special properties such as the energy harvesting and depletion rate, it brings great research challenges to seek an optimal deployment strategy. To this end, we propose an approximation algorithm named Effective Relay Deployment Algorithm, which can be divided into two phases: disk covering and connector insertion using the partitioning technique and the Steinerization technique, respectively. Based on probabilistic analysis, we further optimize the performance ratio of our algorithm to (5 + 6/K) where K is an integer denoting the side length of a cell after partitioning. Our extensive simulation results show that our algorithm can reduce the number of EHRs to be deployed by up to 45% compared with previous work and thus validate the efficiency and effectiveness of our solution.

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations; while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

Document ranking is one of the most studied but challenging problems in information retrieval (IR). More and more studies have begun to address this problem from fine-grained document modeling. However, most of them focus on context-independent passage-level relevance signals and ignore the context information. In this paper, we investigate how information gain accumulates with passages and propose the context-aware Passage Cumulative Gain (PCG). The fine-grained PCG avoids the need to split documents into independent passages. We investigate PCG patterns at the document level (DPCG) and the query level (QPCG). Based on the patterns, we propose a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) and show that PCGM can effectively predict PCG sequences. Finally, we apply PCGM to the document ranking task using two approaches. The first one is leveraging DPCG sequences to estimate the gain of an individual document. Experimental results on two public ad hoc retrieval datasets show that PCGM outperforms most existing ranking models. The second one considers the cross-document effects and leverages QPCG sequences to estimate the marginal relevance. Experimental results show that predicted results are highly consistent with users' preferences. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.

Software-defined networking (SDN) decouples the data and control planes. However, attackers can lead catastrophic results to the whole network using manipulated flooding packets, called the data-to-control-plane saturation attacks. The existing methods, using centralized mitigation policies and ignoring the buffered attack flows, involve extra network entities and make benign traffic suffer from long network recovery delays. For these purposes, we propose LFSDM, a saturation attack detection and mitigation system, which solves these challenges by leveraging three new techniques: 1) using linear discriminant analysis (LDA) and extracting a novel feature called control channel occupation rate (CCOR) to detect the attacks, 2) adopting the distributed mitigation agents to reduce the number of involved network entities and, 3) cleaning up the buffered attack flows to enable fast recovery. Experiments show that our system can detect the attacks timely and accurately. More importantly, compared with the previous work, we save 81% of the network recovery delay under attacks ranging from 1,000 to 4,000 packets per second (PPS) on average, and 87% of the network recovery delay under higher attack rates with PPS ranging from 5,000 to 30,000.

The volume of information that needs to be processed in big data clusters increases rapidly nowadays. It is critical to execute the data analysis in a time-efficient manner. However, simply adding more computation resources may not speed up the data analysis significantly. The data analysis jobs usually consist of multiple stages which are organized as a directed acyclic graph (DAG). The precedence relationships between stages cause scheduling challenges. General DAG scheduling is a well-known NP-hard problem. Moreover, we observe that in some parallel computing frameworks such as Spark, the execution of a stage in DAG contains multiple phases that use different resources. We notice that carefully arranging the execution of those resources in pipeline can reduce their idle time and improve the average resource utilization. Therefore, we propose a resource pipeline scheme with the objective of minimizing the job makespan. For perfectly parallel stages, we propose a contention-free scheduler with detailed theoretical analysis. Moreover, we extend the contention-free scheduler for three-phase stages, considering the computation phase of some stages can be partitioned. Additionally, we are aware that job stages in real-world applications are usually not perfectly parallel. We need to frequently adjust the parallelism levels during the DAG execution. Considering reinforcement learning (RL) techniques can adjust the scheduling policy on the fly, we investigate a scheduler based on RL for online arrival jobs. The RL-based scheduler can adjust the resource contention adaptively. We evaluate both contention-free and RL-based schedulers on a Spark cluster. In the evaluation, a real-world cluster trace dataset is used to simulate different DAG styles. Evaluation results show that our pipelined scheme can significantly improve CPU and network utilization.

Sustaining an operational wireless sensor network (WSN) is challenging due to the persistent need of the battery-powered sensors to be charged from time to time. The procedure of exploiting mobile chargers (MCs) that traverse to the fixed sensors of the network and wirelessly transfer energy in an efficient matter has been considered widely as a promising way to tackle this challenge. An optimization problem, called the mobile charger coverage problem, arises naturally to keep all of the sensors alive with an objective of determining both the minimum number of MCs required meeting the sensor recharge frequency and the schedule of these MCs. It is shown that this optimization problem becomes NP-hard in high-dimensional spaces. Moreover, the special case of the homogeneous recharge frequency of the sensors has already been proven to have a tractable algorithm if we consider whether the 1-dimensional space is a line or a ring. In this work, we seek to find a delicate border between the tractable and the intractable problem space. Specifically, we study the special case of heterogeneous sensors that take frequencies of 1's and 2's (lifetime of 1 and 0.5 time units) on a line, conjecture the special case's NP-hardness, propose a novel brute-force optimal algorithm, and present a linear-time greedy algorithm that gives a 1.5-approximation solution for the problem. Afterwards, we introduce the energy optimization problem of the MCs with the minimized number and solve it optimally. Comprehensive simulation is conducted to verify the efficiency of using our proposed algorithms that minimize the number of MCs.

Scalability has long been a major challenge of cryptocurrency systems, which is mainly caused by the delay in reaching consensus when processing transactions on-chain. As an effective mitigation approach, the payment channel networks (PCNs) enable private channels among blockchain nodes to process transactions off-chain, relieving long-time waiting for the online transaction confirmation. The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing, scheduling, and initial deposits, as well as preventing the system from security and privacy attacks. However, the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected. To fill this gap, we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks. Our study is progressive, as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN. Our analysis is complementary, since we utilize not only the classic game theory with the complete rationality assumption, but also the evolutionary game theory considering the limited rationality of players to portray the evolution of PCN. The results of numerous simulation experiments verify the effectiveness of our analysis.

360° video has been becoming one of the major media in recent years, providing immersive experience for viewers with more interactions compared with traditional videos. Most of today's implementations rely on bulky Head-Mounted Displays (HMDs) or require touch screen operations for interactive display, which are not only expensive but also inconvenient for viewers. In this paper, we demonstrate that interactive 360° video streaming can be done with hints from gaze movement detected by the front camera of today's mobile devices (e.g., a smartphone). We design a lightweight real-time gaze point tracking method for this purpose. We integrate it with streaming module and apply a dynamic margin adaption algorithm to minimize the overall energy consumption for battery-constrained mobile devices. Our experiments on state-of-the-art smartphones show the feasibility of our solution and its energy efficiency toward cost-effective real-time 360° video streaming.

Mobile Edge Computing (MEC) has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process, while ignoring the impact of offloading among edge nodes. In addition, centralized task offloading strategies result in enormous computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users' offloading strategies and propose the Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) to substantially accelerate the convergence. More practically, we extend the users' offloading strategies to take into account idle devices and cloud services, which considers the potential computing resources at the edge. Furthermore, we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency, and find the potential function to prove the existence of Nash equilibrium (NE). Additionally, we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets, and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.

The dataflow architecture, which is characterized by a lack of a redundant unified control logic, has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency, especially of applications used in high-performance computing (HPC). Importantly, the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous manner. Therefore, a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different contexts. Possible solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection, or a handshake mechanism in which the data is only sent after acknowledgments are received. However, these mechanisms are characterized by both inefficient data transfer and increased area cost. Good performance of the dataflow architecture depends on the efficiency of data transfer. In order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost, we propose a Look-Ahead Acknowledgment (LAA) mechanism. LAA accelerates the execution flow by speculatively acknowledging ahead without penalties. Our simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%, with a reduction in the average execution time by 17.4% and an increase in the average power efficiency of dataflow processors by 22.4%. Crucially, our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%. In conclusion, the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.