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GlobLease: A Globally Consistent and Elastic Storage System using Leases 20

GlobLease: A Globally Consistent and Elastic Storage System ...

引用

20th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Liu, Ying Li, Xiaxi Vlassov, Vladimir KTH Royal Inst Technol Dept Software & Comp Syst Stockholm Sweden

ISBN: (纸本)9781479976157

Nowadays, more and more IT companies are expanding their businesses and services to a global scale, serving users in several countries. Globally distributed storage systems are employed to reduce data access latency for clients all over the world. We present GlobLease, an elastic, globally-distributed and consistent key-value store. It is organised as multiple distributed hash tables (DHTs) storing replicated data and namespace. Across DHTs, data lookups and accesses are processed with respect to the locality of DHT deployments. We explore the use of leases in GlobLease to maintain data consistency across DHTs. the leases enable GlobLease to provide fast and consistent read access in a global scale with reduced global communications. the write accesses are optimized by migrating the master copy to the locations, where most of the writes take place. the elasticity of GlobLease is provided in a fine-grained manner in order to precisely and efficiently handle spiky and skewed read workloads. In our evaluation, GlobLease has demonstrated its optimized global performance, in comparison with Cassandra, with read and write latency less than 10 ms in most of the cases. Furthermore, our evaluation shows that GlobLease is able to bring down the request latency under an instant 4.5 times workload increase with skewed key distribution (a zipfian distribution with an exponent factor of 4) in less than 20 seconds.

关键词： distributed Storage Elasticity Geo-replication

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ConSnap: Taking Continuous Snapshots for Running State Protection of Virtual Machines 20

ConSnap: Taking Continuous Snapshots for Running State Prote...

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20th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Li, Jianxin Zheng, Jingsheng Cui, Lei Yang, Renyu Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China

ISBN: (纸本)9781479976157

the reliability of data and services hosted in a virtual machine (VM) is a top concern in cloud computing environment. Continuous snapshots reduces the data loss in case of failures, and thus is prevailing for providing protection for long-running systems. However, existing methods suffer from long VM downtime, long snapshot interval and significant performance overhead. In this paper, we present ConSnap, a system designed to enable taking fine-grained continuous snapshots of virtual machines without compromising VM performance. First, ConSnap adopts the COW (copy-on-write) manner to save the memory pages in a lazy way, and thus decrease the snapshot interval to dozens of milliseconds. Second, we only save the incremental memory pages on the basis of the last snapshot in each epoch to reduce the snapshot duration, and thus mitigate VM performance loss. third, we propose a multi-granularity space reclamation strategy, which merges the unused snapshot files to achieve storage space saving, as well as fast recovery. We have implemented ConSnap on QEMU/KVM and conducted several experiments to verify its effectiveness. Compared with the stop-and-copy based incremental snapshots, ConSnap reduces the performance loss by 71.1% similar to 10.2% under Compilation workload, and 14.5% similar to 4.7% for the Ftp workload, when the interval varies from 1s to 60s.

关键词： cloud computing virtual machine continuous snapshots copy-on-write space reclamation

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Fine-grained parallel Implementation of Edge-directed Image Interpolation on GPU 20

Fine-grained Parallel Implementation of Edge-directed Image ...

引用

20th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Li, Wenze Wu, Jiaji Shi, Jiao Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781479976157

Edge-directed interpolation is widely used to enhance visual performance of remote sensing image. Compared with traditional bi-cubic interpolation and bilinear interpolation, a great number of matrix operations will appear as it is getting better visual performance. CUDA (Compute Unified Device Architecture) offers tremendous performance in many high-performance computing areas. Edge-directed interpolation can be mapped to this architecture (CUDA) readily. However, parallel schemes based on CUDA are generally decomposed into coarse-grained tasks, which is suitable for thread blocks. In this paper, a parallel approach of fine-grained edge-directed interpolation is proposed. Based on CUDA, the process of parallel interpolation for one missing pixel is assigned to 4*4 threads for the reason that majority of matrix operations are related to 4*4 matrix. this task division strategy minimizes resource pressure of thread-blocks. Our calculating scheme is expressed in terms of increasing parallelism that is efficiently implemented on the GPU. By employing one NVIDIA GTX480 GPU and one NVIDIA GTX590 GPU in the case with asynchronous I/O transfer, our GPU optimization efforts on fine-grained edge-directed interpolation scheme finally achieve a speedup of 69.8x with respect to its CPU counterpart C code running on one CPU core of Intel core(TM) i7-920.

关键词： fine-grained CUDA(Compute Unified Device Architectur) GPU edge-directed interpolation

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A Spark-based Task Allocation Solution for Machine Learning in the Edge-Cloud Continuum 20

A Spark-based Task Allocation Solution for Machine Learning ...

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20th Annual international conference on distributed computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Belcastro, Loris Marozzo, Fabrizio Presta, Aleandro Talia, Domenico Univ Calabria DIMES Arcavacata Di Rende Italy

ISBN: (纸本)9798350369458;9798350369441

the widespread utilization of Internet of things (IoT) devices has resulted in an exponential increase in data at the Internet's edges. this trend, combined with the rapid growth of machine learning (ML) applications, necessitates the execution of learning tasks across the entire spectrum of computing resources - from the device, to the edge, to the cloud. this paper investigates the execution of machine learning algorithms within the edge-cloud continuum, focusing on their implications from a distributed computing perspective. We explore the integration of traditional ML algorithms, leveraging edge computing benefits such as low-latency processing and privacy preservation, along with cloud computing capabilities offering virtually limitless computational and storage resources. Our analysis offers insights into optimizing the execution of machine learning applications by decomposing them into smaller components and distributing these across processing nodes in edge-cloud architectures. By utilizing the Apache Spark framework, we define an efficient task allocation solution for distributing ML tasks across edge and cloud layers. Experiments on a clustering application in an edgecloud setup confirm the effectiveness of our solution compared to highly centralized alternatives, in which cloud resources are extensively used for handling large volumes of data from IoT devices.

关键词： Machine learning Internet of things edge computing cloud computing edge-cloud continuum Apache Spark

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Combination Feature for Image Retrieval in the distributed Datacenter 20

Combination Feature for Image Retrieval in the Distributed D...

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20th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Yang, Di Liao, Jianxin Qi, Qi Wang, Jingyu Sun, Haifeng Jiang, Shan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781479976157

Since the emergence of cloud datacenters provides an enormous amount of resources easily accessible to people, it is challenging to provide an efficient search framework in such a distributed environment. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. these methods are insufficient to meet requirements of content based image retrieval (CBIR) and more powerful search frameworks are needed. In this paper, we present LCFIR, an effective image retrieval framework for fast content location in the distributed situation. It adopts the peer-to-peer paradigm and combines color and edge features. the basic idea is to construct multiple replicas of an image's index through exploiting the property of Locality Sensitive Hashing (LSH). thus, the indexes of similar images are probabilistically gathered into the same node without the knowledge of any global information. the empirical results show that the system is able to yield high accuracy with load balancing, and only contacts a few number of the participating nodes.

关键词： Cloud computing Content based image retrieval Peer-to-peer Locality sensitive hashing Combination feature

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FENet: An SDN-Based Scheme for Virtual Network Management 20

FENet: An SDN-Based Scheme for Virtual Network Management

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20th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Liu, Kun Wo, Tianyu Cui, Lei Shi, Bin Xu, Jie Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Univ Leeds Sch Comp Leeds W Yorkshire England

ISBN: (纸本)9781479976157

Virtual networking is vital to efficient resource management in Clouds, and it is in fact one of the main services provided by many Cloud computing platforms. Virtual network management needs to meet specific requirements, including tenant isolation and adaption to virtual machines' lifecycle. Most of the existing schemes for virtual network management are based on the use of overlay networks in order to achieve a desirable degree of flexibility. However, these schemes suffer from a common limit, i.e. relatively high performance penalty due to a complicated forwarding process. We address this performance concern by developing a new management scheme, FENet, which makes use of Software-Defined Networks (SDN) to create virtual networks and manage them via the SDN controller programs. We present the design of an SDN controller, with the definition of flow entry rules based on the OpenFlow protocol and the specification of a routing algorithm. the results from our experimental evaluation show that our SDN-based prototype can control virtual network interconnections and tenant isolation appropriately. FENet achieves about 30% better network performance than the management scheme based on OpenVPN and lower latency in comparison with the traditional bridging scheme.

关键词： Cloud computing OpenFlow SDN tenant isolation virtual networks

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A parallel classification method for genomic and proteomic problems

A parallel classification method for genomic and proteomic p...

引用

20th international conference on Advanced Information Networking and Applications

作者： Guarracino, M. R. Cifarelli, C. Seref, O. Pardalos, P. M. High Performance Computing and Networking Institute National Research Council Italy Department of Statistic Probability and Applied Statistics University of Rome La Sapienza Italy Center for Applied Optimization University of Florida Gainesville FL 32611-6595 United States

ISBN: (纸本)0769524664

Classification is one of the most widely used method in data mining with numerous applications in biomedicine. the scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study, a parallel implementation of an efficient algorithm that is based on regularized general eigenvalue classification is introduced. the proposed implementation is tested on a very large scale genomic data base and preliminary results regarding efficiency are presented.

关键词： Proteins

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A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog computing 20

A Deep Q-Learning Framework for Enhanced QoE and Energy Opti...

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20th Annual international conference on distributed computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Sumona, Sabiha Tasnim Hasan, Syed Sabbir Tamzid, Abu Yousuf Roy, Palash Razzaque, Md Abdur Mahmud, Redowan Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka Bangladesh Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia

ISBN: (纸本)9798350369458;9798350369441

Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. this paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks.

关键词： Fog computing Task offloading energy consumption Deep Q-Learning Quality-of-Experience

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Quality of Experience Aware Task Offloading in Digital Twinning Vehicular Edge computing 20

Quality of Experience Aware Task Offloading in Digital Twinn...

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20th Annual international conference on distributed computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Jihad, Mostakim Rodshi, Mashraba Tasnim Al Fahad, Abdullah Roy, Palash Razzaque, Md Abdur Hassan, Mohammad Mehedi Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia

ISBN: (纸本)9798350369458;9798350369441

Adopting Digital Twin (DT) technology in vehicular edge computing (VEC) enables efficient capture of real-time state information of applications, thereby addressing complex task scheduling problems. Existing literature studies considered only minimizing service latency for task offloading;however, there is room for exploring strategies to enhance user Quality of Experience (QoE) in timeliness and reliability domains. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), namely QuETOD, which minimizes service latency by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. the developed QuETOD framework clusters the vehicles based on the demand-supply theory of economics by considering computing resources and utilizing the multi-weighted subjective logic for getting the proper reputation update of the vehicles. the experimental results of the developed QuETOD system depict significant performance improvement in terms of QoE and reliability compared to the state-of-the-art works as high as 15% and 25%, respectively.

关键词： Vehicular Edge computing Task Offloading Digital Twin Quality-of-Experience

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Proceedings - 20th international conference on High Performance computing and Communications, 16th international conference on Smart City and 4th international conference on Data Science and systems, HPCC/SmartCity/DSS 2018

Proceedings - 20th International Conference on High Performa...

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20th international conference on High Performance computing and Communications, 16th IEEE international conference on Smart City and 4th IEEE international conference on Data Science and systems, HPCC/SmartCity/DSS 2018

ISBN: (纸本)9781538666142

the proceedings contain 248 papers. the topics discussed include: parallel computing implementation for real-time image dehazing based on dark channel;improved parallel algorithms for sequential minimal optimization of classification problems;heterogeneous assignment of functional units with Gaussian execution time on a tree;high performance and low latency vision system with hardware accelerator;merge-based parallel sparse matrix-sparse vector multiplication with a vector architecture;a learning-based adjustment model with genetic algorithm of function point estimation;high-performance implementation of matrix-free Runge-Kutta discontinuous Galerkin method for Euler equations;a step towards hadoop dynamic scaling;and towards building a distributed data management architecture to integrate multi-sources remote sensing big data.

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