Hai Jin

Huazhong University of Science and Technology | hust

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users’ geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have...

In the multi-access edge computing (MEC) environment, app vendors' data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors bu...

Edge computing is highly recommended to support Mobile Crowdsensing (MCS) applications for sensing data processing. In this paper, we consider the MCS applications supported by the mobile phones of bus passengers, who transfer between different bus stations equipped with edge servers. The edge servers deployed with the corresponding MCS services ca...

Blockchain has recently emerged as a research trend, with potential applications in a broad range of industries and context. One particular successful Blockchain technology is smart contract, which is widely used in commercial settings (e.g., high value financial transactions). This, however, has security implications due to the potential to financ...

In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-access...

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constr...

The huge amount of data enforces great pressure on the processing efficiency of database systems. By leveraging the in-situ computing ability of emerging nonvolatile memory, processing-in-memory (PIM) technology shows great potential in accelerating database operations against traditional architectures without data movement overheads. In this artic...

Plenty of research efforts have been devoted to FPGA-based acceleration, due to its low latency and high energy efficiency. However, using the original low-level hardware description languages like Verilog to program FPGAs requires generally good knowledge of hardware design details and hand-on experiences. Fortunately, the FPGA community intends t...

RDF is a standard model for data interchange on the web and is widely adopted for graph data management. With the explosive growth of RDF data, how to process RDF data incrementally and maximize the parallelism of RDF systems has become a challenging problem. The existing RDF data management researches mainly focus on parallel query, and rarely pay...

In distributed deep learning training, the synchronization of gradients usually brings huge network communication overhead. Although many methods have been proposed to solve the problem, limited effectiveness has been obtained, since these methods do not fully consider the differences of diverse layers. We propose a novel hybrid layer-based optimiz...

Non-Volatile Main Memories (NVMMs) have recently emerged as promising technologies for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, low cost, and energy efficiency, at the expense of high write latency, high write power consumption and limited write endurance. NVMM...

Edge computing, as an emerging and prospective computing paradigm, allows a service provider to serve its users by allocating them to nearby edge servers delivering services with low latency. From the service provider's perspective, a cost-effective service user allocation aims to allocate maximum service users to minimum edge servers. Such an allo...

The performance of distributed stream processing engines is significantly compromised when processing stream data with skewed distribution. Current stream partitioning schemes are not able to meet the rigorous requirements of distributed stream processing. We show that network cost is an essential factor for partitioning data, and this factor shoul...

A major design to improve scalability and performance of blockchain is sharding, which maintains a distributed ledger by running classical Byzantine Fault Tolerance (BFT) protocols through several relatively small committees. However, there are several drawbacks with the existing sharding protocols. First, the sharding mechanism which ensures that...

In a variety of applications, the data items of multiple participants are collected and analyzed, and meanwhile the participants’ privacy needs to be protected. This paper studies an over-threshold data aggregation problem, i.e., over-threshold set-union. In our model, we assume there are n participants, an untrusted data aggregator and a proxy, an...

Test-based automated program repair (APR) has attracted huge attention from both industry and academia. Despite the significant progress made in recent studies, the overfitting problem (i.e., the generated patch is plausible but overfitting) is still a major and long-standing challenge. Therefore, plenty of techniques have been proposed to assess t...

Real-world stream data with skewed distributions raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a "one size fits all" design, which leverages either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leadin...

Many iterative graph processing systems have recently been developed to analyze graphs. Although they are effective from different aspects, there is an important issue that has not been addressed yet. A real-world graph follows the power-law property, in which a small number of vertices have high degrees (i.e., are connected to most other vertices...

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory (DRAM) and non-volatile memories (NVMs), to improve the system performance and energy efficiency. However, page migration introduces some side effects, such as more translation lookaside buffer (TLB) misses, breaking memory contiguity, and extra me...

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users' retrieval of app data. However, an edge server normally owns limited resources due to...

Edge computing (EC) can overcome several limitations of cloud computing. In the EC environment, a service provider can deploy its application instances on edge servers to serve users with low latency. Given a limited budget K for deploying applications in a particular geographical area, some approaches have been proposed to achieves various optimiz...

In this paper, we propose a two-stage high-performance graph coloring algorithm, called Feluca, aiming to address the above challenges. Feluca combines the recursion-based method with the sequential spread-based method. In the first stage, Feluca uses a recursive routine to color a majority of vertices in the graph. Then, it switches to the sequent...

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world graphs. Programmability and pipeline parallelism of FPGAs make it potential to process different stages of graph iterations. Nevertheless, considering the limited on-chip resources and streamline pipeline computation, the efficien...

Graph processing is widely used in modern society, such as social networks, bioinformatics, and information networks. It is observed that the dataflow architecture has been demonstrated to effectively resolve the challenges of low instruction-level parallelism and branch mispredictions in the existing general-purpose architecture for graph applicat...

Edge computing, as an extension of cloud computing, distributes computing and storage resources from centralized cloud to distributed edge servers, to power a variety of applications demanding low latency, e.g., IoT services, virtual reality, real-time navigation, etc. From an app vendor's perspective, app data needs to be transferred from the clou...

IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by the device vendor, e.g., Philips bulbs are managed under Philips Hue cloud. Today's mainstream IoT clouds also support device access delega...

Edge computing has emerged as a new computing paradigm that allows computation and storage resources in the cloud to be distributed to edge servers. Those edge servers are deployed at base stations to provide nearby users with high-quality services. Thus, data caching is extremely important in ensuring low latency for service delivery in the edge c...

There is a growing gap between data explosion speed and the improvement of graph processing systems on conventional architectures. The main reason lies in the large overhead of random access and data movement, as well as the unbalanced and unordered communication cost. The emerging metal-oxide resistive random access memory (ReRAM) has great potent...

Key Points: Question: Can a machine-learning-based algorithm identify important predictors for COVID-19 patients at high risk of becoming critically ill to provide early warnings to clinicians for the possible intervention? Findings: After analyzed 2,654 hospitalized COVID-19 patients, our explainable ensemble learning model showed good discrim...

Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation...

Graph processing is one of the important research topics in the big-data era. To build a general framework for graph processing by using a DRAM-based FPGA board with deep memory hierarchy, one of the reasonable methods is to partition a given big graph into multiple small subgraphs, represent the graph with a two-dimensional grid, and then process...

This paper proposes a dynamic entity-based NER approach under unconstrained tagging schemes. To eliminate the constraints in new schemes, we reorganize widely used tagging schemes and propose two novel unconstrained tagging schemes: one in which tags are assigned for words, chunks and entities and one where tags are unconstrained between themselves...

User-generated trajectories (e.g. during traveling) can be leveraged to offer value-added services (e.g. smart city policy formulation), but there are also privacy implications. For example, information about the routes or destinations obtained from such published trajectories can be used to profile and identify users. Meanwhile, the existing traje...

FPGA-based graph processing accelerators are nowadays equipped with multiple pipelines for hardware acceleration of graph computations. However, their multi-pipeline efficiency can suffer greatly from the considerable overheads caused by the read/write conflicts in their on-chip BRAM from different pipelines, leading to significant performance degr...

Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we prop...

Network Function Virtualization (NFV), as an emerging solution to virtualizing network services traditionally running on proprietary, dedicated devices, can effectively reduce the cost of big data processing service providers and improve service quality by running a service chain of ordered Virtual Network Functions (VNFs) on commodity hardware. On...

Internet of Things (IoT) has become a critical infrastructure in the smart city services. Unlike traditional network nodes, most of the current IoT devices are constrained with limited capabilities. Moreover, frequent changes in network status (e.g., nodes turns into sleep mode to save battery) make it even more difficult to set up a stable, secure...

As a recent innovation, network functions virtualization (NFV)—with its core concept of replacing hardware middleboxes with software network functions (NFs) implemented in commodity servers—promises cost savings and flexibility benefits. However, transitioning NFs from special-purpose hardware to commodity servers has turned out to be more challeng...

Graphics Processing Units (GPUs) have evolved as powerful co-processors for the CNN training. Many new features have been introduced into GPUs such as concurrent kernel execution and hyper-Q technology. It is challenging to orchestrate concurrency for CNN (convolutional neural networks) training on GPUs since it may introduce much synchronization o...

Superpages have long been proposed to enlarge the coverage of translation lookaside buffer (TLB). They are extremely beneficial for reducing address translation overhead in big memory systems, such as hybrid memory systems that composed of DRAM and non-volatile memories (NVMs). However, superpages conflict with fine-grained memory migration, one of...

As the cloud systems gain in popularity, they suffer from cyber attacks. One of the most notorious cyber attacks is Distributed Denial of Service (DDoS) attack, which aims to drain the system resources so that the system becomes unresponsive to the genuine users. DDoS attack and defense essentially revolve around resource competition. Many efforts...

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control pl...

Currently, HPC storage systems still use hard disk drive (HDD) as their dominant storage device. Solid state drive (SSD) is widely deployed as the buffer to HDDs. Burst buffer has also been proposed to manage the SSD buffering of bursty write requests. Although burst buffer can improve I/O performance in many cases, we find that it has some limitat...

Discovering vulnerabilities in smart contracts, particularly those that can be exploited, is challenging. Existing research efforts tend to focus on pre-tests or are not capable of dynamically protecting the deployed contracts without impacting on the availability of the contracts. Thus in this paper, we propose and implement a high-availability an...

Reachability queries ask whether a vertex can reach another vertex on large directed graphs. It is one of the most fundamental graph operators and has attracted researchers in both academics and industry to study it. The main technical challenge is to support fast reachability queries by efficient managing the three main costs: the index constructi...

Hosting virtualized network functions (VNF) has been regarded as an effective way to realize network function virtualization (NFV). Considering the cost diversity in cloud computing, from the perspective of service providers, it is significant to orchestrate the VNFs and schedule the traffic flows for network utility maximization (NUM) as it implie...

With the rapid development of IoT and smart homes, smart meters have received extensive attention. The third-party applications, such as smart home controlling, dynamic demand-response, power monitoring, etc., can provide services to users based on consumption data of household electricity collected from smart meters. With the emergence of non-intr...

Current DRAM-based memory systems face the scalability challenges in terms of memory density, energy consumption, and monetary cost. Hybrid memory architectures composed of emerging Non-Volatile Memory (NVM) and DRAM is a promising approach to large-capacity and energy-efficient main memory. However, hybrid memory systems pose a new challenge to on...

As social networks are integrated into the Vehicular ad hoc networks (VANETs), the emerging Vehicular social networks (VSNs) have gained massive interests. However, the security and privacy of data generated by various applications in VSNs is a great challenge, which blocks the further development of VSNs. The emerging Blockchain technology seems t...

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep lear...

Automatically detecting software vulnerabilities is an important problem that has attracted much attention. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and locating precision that would warrant their adoption for real-world use. In this paper, we present Vulnerability Deep Learning-based Loc...

A residual 3D U-Net enables multi-scaling with the concatenation of feature maps from different scales. The connectors between different sub-networks assists in the concatenation of feature maps. A multi-path architecture enables the fusion of feature maps from different scales. In this paper, the combination of two different architectures is propo...

Homomorphic Encryption (HE) allows processing cipher-text data, but it is a challenge to enable complex methods such as multimedia decompression in the HE domain. In this paper, we propose a novel scheme to enable FLAC (Free Lossless Audio Codec) decompression in the HE domain. FLAC applies linear prediction to predict the current sample and Golomb...

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge 〈u; v〉 is a superi...

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcr...

Pneumoconiosis is one of the most serious occupational diseases in China, which seriously endangers the health of most workers in dust environments. The diagnosis of pneumoconiosis is very complex and cumbersome, which relies mostly on doctor’s medical knowledge and clinical reading experiences of X-ray chest film. Traditional image processing appr...

Cloud computing is an arising paradigm to run and hosts a number of applications and services. These computing services are accommodated by a set of virtual machines. These virtual machines are an abstraction of real servers or physical machines. A physical machine can hosts a number of virtual machines, depending on its capacity. Virtual machine p...

Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We...

Magnetic Resonance Imaging (MRI) is dominant modality for infant brain analysis. Segmenting the whole infant MRI brain into number of tissues such as Cerebrospinal fluid (CSF), White matter (WM), and Gray Matter (GM) are highly desirable in the clinical environment. However, traditional methods tend to be degrading due to low contrast between GM an...

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or tempor...

With the rapidly growing demand of graph processing in the real world, a large number of iterative graph processing jobs run concurrently on the same underlying graph. However, the storage engines of existing graph processing frameworks are mainly designed for running an individual job. Our studies show that they are inefficient when running concur...

It has long been an area of interest to identify important vertices in social networks. Closeness centrality is one of the most popular measures of centrality of vertices. Generally speaking, it measures how a node is close to all other nodes on average. However, closeness centrality measures the centrality from a global view. Consequently, in real...

Although GPUs have emerged as the mainstream for the acceleration of convolutional neural network (CNN) training processes, they usually have limited physical memory, meaning that it is hard to train large-scale CNN models. Many methods for memory optimization have been proposed to decrease the memory consumption of CNNs and to mitigate the increas...

Nowadays, web servers often face the threat of distributed denial of service attacks and their intrusion prevention systems cannot detect those attacks effectively. Many existing intrusion prevention systems detect attacks by the state of per-flow and current processing speed cannot fulfill the requirements of real-time detection due to the high sp...

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is...

How to reduce the costly cross-rack data transferring is challenging in improving the performance of MapReduce platforms. Previous schemes mainly exploit the data locality in the Map phase to reduce the cross-rack communications. However, the Map locality based schemes may lead to highly skewed distribution of Map tasks across racks in the platform...

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep lear...

DDoS attacks are rampant in cloud environments and continually evolve to more sophisticated and intelligent modalities, such as low-rate DDoS attacks. At the same time, the cloud environment is also developing in constant. Container technology and microservice architecture together constitute the container-based cloud environment. Comparing with tr...

Automatic accurate segmentation of medical images has significant role in computer-aided diagnosis and disease treatment. The segmentation of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues plays an important role in infant brain structure for studying early brain development. However, this task is very challenging due to...

Buffer overflow (BoF) is one of the most dangerous security vulnerabilities. A BoF can be induced by functions, such as the memcpy family, or loops with pointer or array operations. Static detection of BoF is a well-defined method and often performed before system deployment. However, most of previous static techniques either detect the BoFs induce...