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Symmetric Key Based Secure Resource Sharing 1

引用

5th international symposium on Security in computing and Communications, SSCC 2017

作者： Bezawada, Bruhadeshwar Kothapalli, Kishore Raman, Dugyala Li, Rui Mahindra Ecole Centrale Hyderabad India International Institute of Information Technology Hyderabad India Vardhaman College of Engineering Hyderabad India College of Computer Science and Networking Security Dongguan University of Technology of Science and Technology Dongguan China

ISBN: (数字)9789811068980

ISBN: (纸本)9789811068973

We focus on the problem of symmetric key distribution for securing shared resources among large groups of users in distributed applications like cloud storage, shared databases, and collaborative editing, among others. In such applications, resources such as data, are sensitive in nature and it is necessary that only authorized users are allowed access without the presence of on-line monitoring system. the de-facto approach is to encrypt a shared resource and deploy a key distribution mechanism, which enables only authorized users to generate the respective decryption key for the resource. the key distribution approach has two major challenges: first, the applications are dynamic i.e., users might join and leave arbitrarily, and second, for a large number of users, it is required that the cryptographic technique be scalable and efficient. In this work, we describe an approach that overcomes these challenges by using two key techniques: first, flattening the access structure and applying efficient symmetric key distribution techniques. By flattening the access structure, we reduce the problem to that of key distribution of a resource among all the users sharing that resource. We consider this smaller flattened access structure and devise a unified key distribution technique that is sufficient for key distribution across all such structures. Our key distribution techniques have an important feature of a public secret and a private secret, which allows the group controller to publish updates to the keying material using the public secret and therefore, does not necessitate the users to be in constant communication with the group controller. Using this model we describe two efficient key distribution techniques that scale logarithmically with the group size and also handle group additions and removals. Furthermore, a user can be off-line for any amount of time and need not be aware of the dynamics of the system, which is important as it overcomes the problems posed by l

关键词： Cloud storage

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A parallel Programming Course Based on an Execution Time-Energy Consumption Optimization Problem 30

A Parallel Programming Course Based on an Execution Time-Ene...

引用

30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Cuenca, Javier Gimenez, Domingo Univ Murcia Dept Engn & Technol Comp E-30071 Murcia Spain Univ Murcia Dept Comp & Syst E-30071 Murcia Spain

ISBN: (纸本)9781509036820

this paper presents an experience of Problem-based learning in a parallel Programming course. the course includes the basics of parallel Programming, from methodological and technological aspects to the analysis and design of parallel algorithms. the students work with an optimization problem in the field of parallel computing. the execution time and the energy consumption of a simplified master-slave scheme in a simplified heterogeneous system are optimized, so treating it as a bi-objective optimization problem, which is addressed with sequential, shared-memory, message-passing and hybrid parallel programming. In this way, the students follow the various parts of the syllabus of the course by working with a problem in which topics studied in previous courses are combined (green computing, computational systems architecture, optimization, heuristics), and this contributes to a deeper understanding of these topics and motivates the introduction of new concepts.

关键词： problem-based learning parallel programming bi-objective optimization execution time energy consumption

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A Novel Performance Evaluation and Optimization Model for Big Data System 15

A Novel Performance Evaluation and Optimization Model for Bi...

引用

15th international symposium on parallel and distributed computing (ISPDC)

作者： Xu, Jungang Wang, Guolu Liu, Shengyuan Liu, Renfeng Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China

ISBN: (纸本)9781509041527

In recent years, the development of Internet enables the rapid growth of global data volume, the arrival of the era of big data has brought great challenges to the traditional computing. Big Data systems, such as hadoop, spark, are becoming important platforms to handle big data, but due to design flaws of big data application itself, and unreasonable distributed framework configuration, the performance of the applications in big data system is difficult to achieve peak speed of computer theory, so how to locate performance bottleneck of big data system and analyze the bottleneck causes is worthy of research. In this paper, a 5-layer performance evaluation model for big data system is proposed, which is a reliable basis for performance analysis, and at the same time, a performance optimization model for big data system is also proposed, which can assist performance bottleneck location and bottleneck analysis, and further optimize performance. Based on these two performance models, an event-based performance tool to profile performance data is implemented. Experimental results show that these two performance models are effective for performance evaluation and optimization of big data system, which can improve average running time of big data system by 19%.

关键词： MapReduce parallel Program Performance Model Bottleneck Performance analysis

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Testing fine-grained parallelism for the ADMM on a factor-graph 30

Testing fine-grained parallelism for the ADMM on a factor-gr...

引用

30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Hao, Ning Oghbaee, AmirReza Rostami, Mohammad Derbinsky, Nate Bento, Jose

ISBN: (纸本)9781509036820

there is an ongoing effort to develop tools that apply distributed computational resources to tackle large problems or reduce the time to solve them. In this context, the Alternating Direction Method of Multipliers (ADMM) arises as a method that can exploit distributed resources like the dual ascent method and has the robustness and improved convergence of the augmented Lagrangian method. Traditional approaches to accelerate the ADMM using multiple cores are problem-specific and often require multi-core programming. By contrast, we propose a problem-independent scheme of accelerating the ADMM that does not require the user to write any parallel code. We show that this scheme, an interpretation of the ADMM as a message-passing algorithm on a factor-graph, can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control. Specifically, we obtain 10-18x speedup using a GPU, and 5-9x using multiple CPU cores, over a serial, optimized C-version of the ADMM, which is similar to the typical speedup reported for existing GPU-accelerated libraries, including cuFFT (19x), cuBLAS (17x), and cuRAND (8x).

关键词： ADMM distributed Optimization Message-passing algorithm GPU computing Shared-memory multi-core computing

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Service Load Balancing in Fog-based 5G Radio Access Networks 28

Service Load Balancing in Fog-based 5G Radio Access Networks

引用

28th Annual IEEE international symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Jijin, Jofina Seet, Boon-Chong Chong, Peter Han Joo Jarrah, Hazim Auckland Univ Technol Dept Elect & Elect Engn Auckland 1010 New Zealand

ISBN: (纸本)9781538635315

Fog-based radio access networks (F-RAN) are poised to play a significant role in future 5th generation (5G) cellular networks by harnessing the distributed resources of collaborative edge devices to deliver localized RAN services to the end-users. through ingestion and processing of end-user tasks close to their sources, F-RAN has potential to meet the stringent latency and bandwidth requirements of 5G services and applications. Due to the multitude of edge devices with different resource capacities that can be selected by the fog access point (FAP) as service nodes to process an end-user task, an important issue is deciding which service node(s) should be assigned to process what tasks from end-users in the F-RAN. the tasks here refer to user processing tasks that would have normally performed by the FAP or remotely by the cloud. In this paper, we introduce the concept of virtual FAPs (v-FAPs) formed by a number of local devices such as WiFi access points, femtocell base-stations and the more resource-rich end-user devices under the coverage and management of the FAP. this paper focuses on addressing the task assignment problem by proposing a service load balancing algorithm for the v-FAPs. We model the user tasks as a task graph and the service nodes as an edgeless service graph. We then formulate an optimization problem to find the optimal assignment of tasks with the objective of balancing loads at the service nodes.

关键词： fog computing radio access network task assignment load balancing 5G

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New Biology Inspired Anonymous distributed Algorithms to compute Dominating and Total Dominating Sets in Network Graphs 30

New Biology Inspired Anonymous Distributed Algorithms to com...

引用

30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Luo, Feng Srimani, Pradip K. Clemson Univ Sch Comp Clemson SC 29634 USA

ISBN: (纸本)9781509036820

the selection mechanism of sensory organ precursor (SOP) from pre-neural cells of fruity fly has recently been used to develop a new computing paradigm for probabilistic (randomized) distributed algorithms for graph theoretic primitives like maximal independent set (MIS) in networks [1]. the new paradigm is significantly different from the settings of traditional distributed algorithms [2], [3], [4], [5]: nodes are anonymous, the nodes do not need any knowledge of its neighbors, they receive binary message(s) from the neighbor(s), there is no need to decipher the message, only receipt or non-receipt of message(s) at any step of the algorithm. In this paper, we develop two new simple distributed algorithms to compute the minimal dominated set (MDS) and minimal total dominating set (MTDS) in a graph using this new paradigm. We show that the two algorithms for computing the MDS and MTDS respectively converge with probability (1 - 1/n(2)) using O(D log n) expected number of steps, where n is the number of nodes in the system and D is the maximum degree of a node. Preliminary experimental simulation results show that the algorithms converge in substantially less number of steps to converge than estimated by the analysis for a large number of graph families.

关键词： Biology inspired distributed algorithms Minimal dominating set Minimal total dominating set Synchronous run time scheduler

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ZCCloud: Exploring Wasted Green Power for High-Performance computing 30

ZCCloud: Exploring Wasted Green Power for High-Performance C...

引用

30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Yang, Fan Chien, Andrew A. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Math & Comp Sci Argonne IL 60439 USA

ISBN: (纸本)9781509021406

In supercomputer centers, available power, cooling, or carbon footprint often limits supercomputer performance. We propose a new approach to continue scaling that avoids many of these limits, augmenting a traditional system with another that employs only "wasted" renewable power, stranded power. this excess power cannot be economically distributed through grid, and is only intermittently available. We call this approach Zero-carbon Cloud (ZCCloud). We explore the potential benefits of unreliable resources with production DOE HPC workloads using a simple periodic model, and identify job types that benefit most (capability jobs and on-time jobs). the benefits scale with duty factor and resource quantity. Next, to create realistic models of "stranded power" we study 28 months of Mid-continent Independent System Operator (MISO) power market history (1,259 generators, 77 million 5-minute intervals). We find that opportunity varies, but the best single wind site can provide 80% duty factor, and 20MW average stranded power. Combining sites further improves duty factor. With resource volatility models from the MISO study, we simulate production DOE HPC workloads and find that stranded power HPC, ZCCloud, can provide significant benefit, decreasing average job-wait time by 50%.

关键词： High-Performance computing Cloud Power Grid Batch Scheduling Power limits

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Relaxing the Data Access Bottleneck of Geographic Big-data Analytics Applications using distributed Quad trees 5

Relaxing the Data Access Bottleneck of Geographic Big-data A...

引用

5th international Conference on Multimedia computing and Systems (ICMCS)

作者： Nyirenda, Mayumbo Arimura, Hiroki Ito, Kimihito Hokkaido Univ IST Sapporo Hokkaido Japan Hokkaido Univ CZC Sapporo Hokkaido Japan

ISBN: (纸本)9781509051465

Data access of a massive collection of geographic spatial data is one of the serious bottlenecks in large-scale datacentric applications in the big data era such as data assimilation and urban data analytic systems. In this paper, we consider the issue of implementation of distributed spatial indices, specifically quad trees, on a distributed computing system in the shared-nothing memory approach. We discuss static and dynamic partitioning and allocation strategies for data and queries across distributed nodes. Using scale-down parallel data load and search experiments with a small distributed processor system as proof-of-concept, we show that the proposed approach with a collection of small indices of distributed shared-nothing memory is more efficient than the conventional approach with a single processor with a large external index. We also observed that the proposed tree-based partitioning and assignment strategy using sampling reduces query time than other conventional partitioning strategies used in databases. We also discuss how to allocate a collection of small tree indices among distributed processors. these results suggest that the use of parallelized access to databases with spatial indexing functions can enhance the throughput of large-scale data-centric applications.

关键词： Spatial index structure spatial queries shared-nothing distributed computers data assimilation adaptive partition strategy highperformance

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Analysis of shared memory in distributed and non distributed environment 5

Analysis of shared memory in Distributed and non Distributed...

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5th international Conference on Eco-Friendly computing and Communication Systems, ICECCS 2016

作者： Dangi, Dharmendra Bhandari, Sachin Bhagat, Amit Mathematics and Computer Application MANIT Bhopal India Computer Science Engineering SVCE Indore India

ISBN: (纸本)9781509043590

Shared memory, one of the most popular models for programming parallel platforms, is becoming ubiquitous both in low-end workstations and high-end servers. With the advent of low-latency networking hardware, clusters of workstations strive to offer the same processing power as high-end servers for a fraction of the cost. In such environments, shared memory has been limited to page-based systems that control access to shared memory using the memory' s page protection to implement shared memory coherence protocols. Unfortunately, In distributed and parallel computing system, efficient memory sharing, that is free from false-sharing and fragmentation issue and consistent, of data intensive applications is one of the most essential and difficult issues without degrading the system efficiency. therefore proposed analysis is to simulating the behavior of the existing distributed shared memory programming model to find out some way to improve the efficiency. In distributed shared memory environment, a process may require access to remotely available data. So the aim is to propose a distributed shared memory programming model which will maximize the utilization of shared memory, at the same time, a high degree of parallelism and efficiency. © 2016 IEEE.

关键词： Efficiency

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parallel Algorithm Design for Audio Feature Extraction

Parallel Algorithm Design for Audio Feature Extraction

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2017 5th international Conference on Machinery,Materials and computing Technology(ICMMCT 2017)

作者： Xiaocheng Luo Jingfei Jiang Junjie Zhu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

关键词： Feature Extraction Sound Recognition parallel Algorithm

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