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Building dynamic integrity protection for multiple independent authorities in virtualization-based infrastructure

Building dynamic integrity protection for multiple independe...

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International Workshop on grid computing

作者： Ge Cheng Hai Jin Deqing Zou Xinwen Zhang Min Li Chen Yu Guofu Xiang Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Samsung Information Systems America Inc. San Jose CA USA

In grid and cloud computing infrastructures, the integrity of a computing platform is a critical security requirement in order to provide secure and honest computing environments to service providers and resource consumers. However, due to the fact that software components running on a single platform are usually provided and maintained by different authorities which are potentially untrusted to each other, the problem to monitor and protect runtime system integrity become very challenging and has not been well addressed yet. In this paper, we present a virtualization based dynamic integrity protection method which ensures that only appropriate authorities can control over their components without interfering with other component providers or authorities. In our solution, integrity requirements defined by the authorities of upper components (e.g., service middleware and applications) are respected by preventing the underlying components (e.g., operating system) from exposing their sensitive data, which can be caused by update of the underlying components or other malicious actions. We implement our solution on Xen-based platform, and our evaluation results show that the solution is effective for integrity protection with acceptable performance overhead.

关键词： Protection Cloud computing grid computing Application software Hardware Portable computers Middleware Physics computing Operating systems Computer science

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X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics

X-Stream: A Flexible, Adaptive Video Transformer for Privacy...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Dou Feng Lin Wang Shutong Chen Lingching Tung Fangming Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Paderborn University Germany National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Guangxi University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Peng Cheng Laboratory China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

关键词： Privacy Transforms Streaming media Transformers Multitasking Real-time systems Task analysis

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grid Security Interoperation between CGSP and VEGA

Grid Security Interoperation between CGSP and VEGA

引用

International Conference on Future Generation Communication and Networking (FGCN)

作者： Deqing Zou Hai Jin Xueguang Chen Zongfen Han Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769530482;0769530486

grid computing emerges as an effective technology to couple geographically distributed resources and solve large-scale computational problems in wide area networks. There are several popular grid platforms, such as UNICORE and Globus Toolkit. Many famous grid projects are constructed with these platforms, each of which manages different distributed resources. In order to aggregate resources managed by different grid platforms, it is necessary to implement the interoperation among these grid platforms. Security interoperation is one of key issues for the interoperation. CGSP (Chinagrid Support Platform) and VEGA are two popular grid platforms in China. To achieve security interoperation between these two platforms, four mapping policies, including user-user mapping, user-agora mapping, role-user mapping, role-agora mapping, are proposed in this paper. The security interoperation process is proposed based on SAML specification and the mapping policies. We also analyze the flexibility of mapping policies and the security of the interoperation process.

关键词： Security Resource management grid computing Authentication Large-scale systems Computer networks Distributed computing Wide area networks Web services Middleware

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Event-Driven Scheduling for P2P VoD

Event-Driven Scheduling for P2P VoD

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Chinagrid Annual Conference (Chinagrid)

作者： Bin Cheng Hai Jin Xiaofei Liao Bo Li Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

Scheduling is essential in peer-to-peer (P2P) video-on-demand (VoD) applications to obtain desirable user experience and high sharing efficiency. Existing systems and proposals largely utilize scheduling mechanisms that are periodical in nature. We refer these as time-driven scheduling. In this paper we argue that the time-driven scheduling algorithm is not efficient for P2P VoD due to its inadequate resource utilization and inability in quickly adapting to the potential dynamics in such systems. We propose an event-driven scheduling algorithm for P2P VoD systems, in which the scheduler is triggered by events such as peer churn or/and random seeks. We evaluate our design through extensive simulations, and the experimental results show that event-driven scheduling outperforms time-driven scheduling in terms of both user experience and system scalability.

关键词： Scheduling algorithm Dynamic scheduling Processor scheduling Peer to peer computing Large-scale systems Bandwidth Computer science Resource management Discrete event simulation Scalability

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The Content Pollution in Peer-to-Peer Live Streaming Systems: Analysis and Implications

The Content Pollution in Peer-to-Peer Live Streaming Systems...

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International Conference on Parallel Processing (ICPP)

作者： Sirui Yang Hai Jin Bo Li Xiaofei Liao Hong Yao Xuping Tu Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781424432134

There has been significant progress in the development and deployment of peer-to-peer (P2P) live video streaming systems. However, there has been little study on the security aspect in such systems. Our prior experiences in Anysee exhibit that existing systems are largely vulnerable to intermediate attacks, in which the content pollution is a common attack that can significantly reduce the content availability, and consequently impair the playback quality. This paper carries out a formal analysis of content pollution and discusses its implications in P2P live video streaming systems. Specifically, we establish a probabilistic model to capture the progress of content pollution. We verify the model using a real implementation based on Anysee system; we evaluate the content pollution effect through extensive simulations. We demonstrate that (1) the number of polluted peers can grow exponentially, similar to random scanning worms. This is vital that with 1% polluters, the overall system can be compromised within minutes; (2) the effective bandwidth utilization can be sharply decreased due to the transmission of polluted packets; (3) Augmenting the number of polluters does not imply a faster progress of content pollution, in which the most influential factors are the peer degree and access bandwidth. We further examine several techniques and demonstrate that a hash-based signature scheme can be effective against the content pollution, in particular when being used during the initial phase.

关键词： Pollution Peer to peer computing Bandwidth Streaming media Availability Grippers Topology

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LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud

LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Shadi Ibrahim Hai Jin Lu Lu Song Wu Bingsheng He Li Qi Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Engineering Nanyang Technological University Singapore Information and Technology Department China Development Bank Beijing China

This paper investigates the problem of Partitioning Skew in MapReduce-based system. Our studies with Hadoop, a widely used MapReduce implementation, demonstrate that the presence of partitioning skew causes a huge amount of data transfer during the shuffle phase and leads to significant unfairness on the reduce input among different data nodes. As a result, the applications experience performance degradation due to the long data transfer during the shuffle phase along with the computation skew, particularly in reduce phase. We develop a novel algorithm named LEEN for locality-aware and fairness-aware key partitioning in MapReduce. LEEN embraces an asynchronous map and reduce scheme. All buffered intermediate keys are partitioned according to their frequencies and the fairness of the expected data distribution after the shuffle phase. We have integrated LEEN into Hadoop-0.18.0. Our experiments demonstrate that LEEN can efficiently achieve higher locality and reduce the amount of shuffled data. More importantly, LEEN guarantees fair distribution of the reduce inputs. As a result, LEEN achieves a performance improvement of up to 40% on different workloads.

关键词： Partitioning algorithms Distributed databases Cloud computing Performance evaluation Time factors Degradation Kernel

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MeG2: In-Memory Acceleration for Genome Graphs Analysis

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG 2 , a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG 2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG 2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG 2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG 2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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Data Hiding in Virtual Machine Disk Images

Data Hiding in Virtual Machine Disk Images

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International Conference on Computer and Information Technology (CIT)

作者： Yu Su Xiaofei Liao Hai Jin Tim Bell Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Software Engineering College of Engineering University of Canterbury Christchurch New Zealand

Data hiding in computer system is an interesting and important research issue, which brings benefits for secret communication and watermarking. The development of virtual machine brings new potential for data hiding. In this paper we explore the potential for data hiding in virtual machine disk images, and especially hiding schemes that can be used with copy-on-write images. Besides being considered as a way for valid uses such as secret communication and watermarking, these schemes can be a warning against malicious intentions as well. Furthermore, it lays the foundation for a more thorough analysis of the whole virtual machine system for data hiding.

关键词： File systems Detectors Writing Virtual machining Linux Fats Virtual environment

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Ripple: Toward a Substrate Middleware for Peer-to-Peer Applications

Ripple: Toward a Substrate Middleware for Peer-to-Peer Appli...

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International Conference on Innovative computing, Information and Control (ICICIC)

作者： Hong Yao Linchen Yu Hai Jin Sirui Yang Qi Huang Wenjian Jiang Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Geoscience Wuhan China France Telecom Research and Development Center Beijing China

Peers in multiple P2P applications execute overlapped operations with different implementation, like neighbour management, resource publish/lookup, delay investigation, which leads to poor interoperability and unnecessary network load. Peers contribute their resources only when they stay in same application. We propose a P2P middleware, called Ripple, to accomplish common functions in a layered architecture. Ripple manages all peers running various applications in a uniform substrate overlay network. The substrate overlay is designed with a hybrid topology. This hybrid topology does not aim at only a particular type of applications, but also enables the flexibility for various applications. Our work is an attempt to address common needs of P2P applications, which may simplify the development and improve the performance of P2P networks, as well as mitigate the sampling stress to physical networks.

关键词： Peer to peer computing Delay Tin Topology Indexes Substrates Network topology

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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