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Sponge:An Oversubscription Strategy Supporting Performance Interference Management in Cloud

China Communications 2015年第11期12卷 196-209页

作者： LIU Ying Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Resource oversubscription optimizes the utilization of the computing resources. Many well-known virtual machine monitors(VMMs)such as Xen and KVM,adopt this approach to help maximize the yield of the cloud datacenters That is,with proper resource oversubscription strategies,more virtual machines(VMs) can be supported by limited resources. However performance interference among VMs hosting in the same physical machines(PMs) exists in cloud environment,and probably aggravated by resource oversubscription strategies,which aims to put more VMs into the same PM. In this paper,we present a resource oversubscription strategy called Sponge targeting cloud platforms Sponge mitigates the issue of performance interference among the oversubscribed co-hosting VMs. Sponge also provides a VM association strategy for each PM to handle with its besteffort. We performed our evaluation on a virtua datacenter simulated by Xen. Our evaluation results show that Sponge improves the resources utilization and manages to make each VM mee its performance requirement even hosting with other VMs in the same PM.

关键词： resources oversubscription performance interference cloud computing resource management

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A Novel Metadata Extraction Method for Surveillance Video

A Novel Metadata Extraction Method for Surveillance Video

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International Conference on computing, Networking and Communications

作者： Ran Zheng Long Chen Hai Jin Lei Zhu Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781467385800

Surveillance videos are increasing massively with monitoring cameras widely deployed in cities. Metadata of moving objects in videos can reduce video storage and be utilized in many video analysis applications. The whole metadata extraction is too time-consuming to be fulfilled quickly. Therefore, it is urgent to parallelize and accelerate the extraction process. However, iterative execution of traditional metadata extraction methods makes the parallelization quite challenging. In this paper, we propose a novel Parallel Metadata Extraction (PME) method for surveillance video. A novel video segmentation algorithm is designed to segment whole video into independent video segments. The metadata of each video segment is extracted independently and simultaneously on computer nodes, which will be integrated later to guarantee the completeness of the metadata. Performance evaluations demonstrate that PME can accelerate extraction process greatly almost without metadata loss.

关键词： Surveillance Videos Video Segmentation Metadata Extraction Iterative Execution MapReduce

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Robust Scheme to Protect Authentication Code of Message/Image Documents in Cloud computing

Robust Scheme to Protect Authentication Code of Message/Imag...

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International Conference on computing, Networking and Communications

作者： Zaid Ameen Abduljabbar Hai Jin Ali A. Yassin Zaid Alaa Hussien Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology University of Basrah

ISBN: (纸本)9781467385800

A number of image/message document authentication and integrity schemes have been conducted to recognize any modification in the exchange of documents between two entities (sender and receiver) within a cloud environment. Existing solutions are based on combining key-based hash function with traditional factors (steganography, smart-card, timestamp). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent common forms of attack such as replay, forgery, stolen verifier, brute force, and insider attacks. In this paper, we propose a scheme to ensure message/image document integrity for each user's login by providing one-time biometric message/image authentication code called MACLESS, which is a summation of combining the key-based hash function (MAC-SHA-1) of a message/image document and the one-time bio-key. Thereafter, MACLESS is hidden in a cover image based steganography anonymity. The proposed scheme has several important security attributes, such as phase key agreement, users' one-time bio-key, and one-time authentication code is valid only for one user's login session. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.

关键词： Cloud computing MACLESS One-Time Bio-key One-Time Message/Image Document Authentication Code

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vProbe: Scheduling Virtual Machines on NUMA Systems

vProbe: Scheduling Virtual Machines on NUMA Systems

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IEEE International Conference on cluster computing

作者： Song Wu Huahua Sun Like Zhou Qingtian Gan Hai Jin Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509036547

With the development of multi-core platforms andcloud computing, Non-Uniform Memory Access (NUMA) architecturehas been dominant in cloud data centers in recentyears. However, NUMA architecture is not well supported invirtualized environments. Because of the semantic gap introducedby the virtualization layer, hypervisors know little aboutthe characteristics of applications running in virtual machines (VMs). More importantly, in order to guarantee hypervisors' applicability, load balance strategies of virtual CPU (VCPU) schedulers do not consider the memory access characteristicsof applications running in VMs, which probably introducessignificant shared resource contention and unnecessary remotememory accesses. In this paper, we propose a NUMA-aware VCPU schedulerbased on Xen, named vProbe, to improve the performanceof memory-intensive applications while maintaining the transparencyof the virtualization layer in NUMA-based servers. It collects performance monitoring units (PMU) data for eachVCPU and analyzes their memory access characteristics. Then, according to the memory access characteristics of each VCPU, it periodically reassigns all memory-intensive VCPUs to eachNUMA node evenly while preferentially allocating them to theirlocal nodes, which aims to alleviate shared resource contentionand reduce unnecessary remote memory accesses. Moreover, when a physical CPU (PCPU) becomes idle, it preferentiallysteals a VCPU from the run queues of PCPUs in the local nodeto this PCPU, which helps to maintain balanced last-level cache (LLC) contention and reduce extra remote memory accesses. Our evaluation shows that vProbe can significantly improve theperformance of memory-intensive applications (e.g., up to 45.2% performance improvement compared with the Credit scheduler) while introducing negligible overheads.

关键词： Servers Phasor measurement units Virtualization Virtual machine monitors Resource management Bandwidth Cloud computing

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Conch: A Cyclic MapReduce Model for Iterative Applications

Conch: A Cyclic MapReduce Model for Iterative Applications

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Ran Zheng Genmao Yu Hai Jin Xuanhua Shi Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

MapReduce programming model is a popular model to simplify but speed up data parallel applications. However, it is not efficient for iterative applications because of its repeated data transmission with HDFS (Hadoop Distributed File System). Conch, a cyclic MapReduce model, is designed for efficient processing of iterative applications. In order to minimize network overhead, shared data is cached locally and a "map-shuffle" phase is presented with a combined transmission mechanism. Meanwhile, a prediction scheduler for iterative applications is brought out to achieve better data locality in terms of runtime information. The experiments show that Conch can support iterative applications transparently and efficiently. Compared with Hadoop and HaLoop in single-job environment, Conch can achieve 13%-17% improvements on K-Means and fuzzy C-Means. Especially in multi-job environment, 63.6% and 28.6% improvements can be obtained compared with Hadoop and HaLoop.

关键词： Delays Iterative methods Computational modeling Prediction algorithms Heart beat Data transfer Data models

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Cost-Effective Resource Allocation in C-RAN with Mobile Cloud

Cost-Effective Resource Allocation in C-RAN with Mobile Clou...

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Ad-Hoc and Sensor Networks Symposium

作者： Kezhi Wang Kun Yang Xinhou Wang Chathura Sarathchandra Magurawalage School of Computer Sciences and Electrical Engineering University of Essex Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781479966653

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms non-optimal solutions.

关键词： C-RAN Cost-Effective Resource Allocation MCC

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Communication Cost Efficient Virtualized Network Function Placement for Big Data Processing

Communication Cost Efficient Virtualized Network Function Pl...

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IEEE Conference on computer Communications Workshops

作者： Lin Gu Sheng Tao Deze Zeng Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan Hubei China

ISBN: (纸本)9781467399562

The data explosion in the emerging big data era imposes a big burden on the network infrastructure. This vision has urged the evolution of computer networks. By softwarizing traditional dedicated hardware based functions to virtualized network function (VNF) that can run on standard commodity servers, network function virtualization (NFV) technology promises increased networking efficiency, flexibility and scalability. From the perspective of network service providers, with the consideration of big data traffic volume, one primary concern is on the communication cost, which is highly influenced by the VNF placement strategy. In this paper, we are motivated to investigate the issue on communication cost efficient VNF placement problem for big data processing, with joint consideration of network flow balancing and the predetermined network service semantics. We formulate this problem into a mixed-integer linear programming (MILP) form and then propose a low-complexity relaxation-based heuristic algorithm accordingly. The high efficiency of our proposal is validated by extensive simulation studies.

关键词： Noise measurement Big data Distributed databases Algorithm design and analysis Servers Heuristic algorithms Semantics

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DTI changes of thalamic subregions in genetic frontotemporal dementia: findings from the GENFI cohort

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Alzheimer's & Dementia 2023年第S24期19卷

作者： Sonja Soskic Henry F. J. Tregidgo Emily G. Todd Arabella Bouzigues David M Cash Lucy L. Russell David L Thomas Ian B Malone John C. van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Carmela Tartaglia James B. Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher R. Butler Simon Ducharme Alexander Gerhard Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Daniel C Alexander Juan Eugenio Iglesias Jonathan D. Rohrer Martina Bocchetta the GENetic Frontotemporal dementia Initiative (GENFI) Centre for Medical Image Computing University College London London United Kingdom SonjaSoskic Centre for Medical Image Computing University College London London United Kingdom. Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London United Kingdom UCL Centre for Medical Image Computing London United Kingdom Neuroradiological Academic Unit Department of Brain Repair and Rehabilitation UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Neurology and Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan Italy Neurology Department Hospital Clinic Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire Département des Sciences Neurologiques CHU de Québec and Faculté de Médecine Université Laval Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Division Translational Genomics of Neurodegenerative Diseases Hertie Institute for Clinical Brain Research (HIH) University of Tübingen Tübingen Germany German Center for Neurodegenerative Diseases (DZNE) Tübingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Stockholm Sweden Unit for Hereditary Dementia Theme Inflammation and Aging Karolinska University Hospital-Solna Stockholm Sweden Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada Toronto Western Hospital Tanz Centre for Research in Neurodegenerative Disease Toronto ON Canada Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and Brain Sci

Background Atrophy of thalamic subregions has been observed across the spectrum of frontotemporal dementia (FTD). To gain better insight into underlying tissue changes, we investigated how thalamic subregional fractional anisotropy (FA) and mean diffusivity (MD) derived from diffusion tensor imaging (DTI) are altered in genetic FTD. Method We used our newly developed thalamus segmentation tool, which jointly combines structural and diffusion input MRI data, to segment thalami and extract thalamic subregional FA and MD values for 163 genetic mutation carriers and 126 non-carriers with suitable 3T MRI data from the GENetic FTD Initiative (GENFI). Mutation carriers were divided according to their genetic diagnosis and CDR®+NACC FTLD global scores into presymptomatic/prodromal (≤0.5: 41 C9orf72 , 59 GRN , 34 MAPT ) and symptomatic (≥1: 8 C9orf72 , 11 GRN , 10 MAPT ) groups. Mean FA and MD values for thalamic subregions were obtained using diffusion tensors interpolated in the log domain and weighted by segmentation posterior probabilities. Thalamic subregional FA and MD values for presymptomatic and symptomatic mutation carriers within each genetic group were compared with non-carriers using analysis of covariance with bootstrapping, where age, scanner type, and sex were covariates. We corrected for multiple comparisons and calculated percentage changes in adjusted FA and MD mean values for mutation carriers relative to non-carriers. Result The only significant change at the presymptomatic stage was found for C9orf72 expansion carriers, who showed FA reduction in the intralaminar subregion (5%) (Figure 1, Table 1). In symptomatic C9orf72 expansion carriers, FA was reduced in the laterodorsal (21%), lateral posterior (13%), anteroventral (13%) and intralaminar (11%) subregions. Symptomatic MAPT mutation carriers also showed FA reduction in the laterodorsal (15%) and anteroventral (11%) subregions. No significant FA reductions were found for GRN mutation carriers and no s

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Secure biometric image retrieval in IoT-cloud

Secure biometric image retrieval in IoT-cloud

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IEEE International Conference on Signal Processing, Communications and computing (ICSPCC)

作者： Zaid Ameen Abduljabbar Hai Jin Ayad Ibrahim Zaid Alaa Hussien Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Basrah Basrah Iraq SouthernTechnical University Basrah Iraq

ISBN: (纸本)9781509027095

Within the IoT-cloud, security has a very significant role to play. One of the best means to safeguard confidentially, security and privacy of a biometric image is through encryption. However, looking through encrypted data is a difficult process. A number of different techniques for searching encrypted data have been devised, but certain security solutions may not be used for smart devices within an IoT-cloud, and this is due to the fact that such solutions are not lightweight. In this paper, we present a lightweight scheme that provides the privacy-preserving biometric image search, which is a special case of content-based image retrieval (CBIR). A fusion of homomorphic encryption, cosine similarity and garbled circuit-based approaches are adopted in our scheme to achieve the best performance while simultaneously ensuring the privacy of the biometric image, and protection of any data access patterns and the user's input query. We conduct several empirical analyses on real image collections to demonstrate the performance and security of our work.

关键词： Encryption Biomedical imaging Servers Protocols Smart devices

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Improving the memory efficiency of in-memory mapreduce based HPC systems 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Pei, Cheng Shi, Xuanhua Jin, Hai Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

In-memory cluster computing systems based MapReduce, such as Spark, have made a great impact in addressing all kinds of big data problems. Given the overuse of memory speed, which stems from avoiding the latency caused by disk I/O operations, some process designs may cause resource inefficiency in traditional high performance computing (HPC) systems. Hash-based shuffle, particularly large-scale shuffle, can significantly affect job performance through excessive file operations and unreasonable use of memory. Some intermediate data unnecessarily overflow to the disk when memory usage is unevenly distributed or when memory runs out. Thus, in this study, Write Handle Reusing is proposed to fully utilize memory in shuffle file writing and reading. Load Balancing Optimizer is introduced to ensure the even distribution of data processing across all worker nodes, and Memory-Aware Task Scheduler that coordinates concurrency level and memory usage is also developed to prevent memory spilling. Experimental results on representative workloads demonstrate that the proposed approaches can decrease the overall job execution time and improve memory efficiency. © Springer International Publishing Switzerland 2015.

关键词： MapReduce

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