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Secure group communication schemes for dynamic heterogeneous distributed computing

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 74卷 313-324页

作者： Castiglione, Arcangelo D'Arco, Paolo De Santis, Alfredo Russo, Rosario Univ Salerno Dept Comp Sci Via Giovanni Paolo 2 132 I-84084 Fisciano SA Italy

Nowadays Sensor Networks and Ad Hoc Networks are widely used communication facilities, mainly because of their many application settings. Again, though above types of network are paradigms of communication widespread and well-established in the state of the art, however, they turn out to be among the most important concepts underlying the modern and increasingly expanding User-Centric Networks, which can be used to build dynamic middleware services for heterogeneous distributed computing. In this way, can be addressed the strong dynamic behavior of user communities and of resource collections they use. In this paper we focus our attention on key predistribution for secure communications in those types of networks. In particular, we first analyze some schemes proposed in the literature for enabling a group of two or more nodes to compute a common key, which can be used later on to encrypt or authenticate exchanged messages. The schemes we have chosen are well representative of different design strategies proposed in the state of the art. Moreover, in order to find out under which conditions and in which settings a scheme is more suitable than others, we provide an evaluation and a performance comparison of those schemes. Furthermore, we look at the problem of identifying optimal values for the parameters of such schemes, with respect to a certain desirable security degree and reasonable security assumptions. Finally, we extend one of those schemes, showing both analytically and through experiments, the improvement the new scheme provides in terms of security compared to the basic one. (C) 2015 Elsevier B.V. All rights reserved.

关键词： heterogeneous distributed computing Secure group communication Key predistribution Sensor Networks Ad Hoc Networks Performance analysis

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Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2015年 51卷 61-71页

作者： Vasile, Mihaela-Andreea Pop, Florin Tutueanu, Radu-Ioan Cristea, Valentin Kolodziej, Joanna Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania Cracow Univ Technol Inst Comp Sci PL-30084 Krakow Poland

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for heterogeneous distributed computing, especially for modern High-Performance computing (HPC) systems in which applications are modeled with various requirements (both 10 and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Resource provisioning Task scheduling Clustering heterogeneous distributed computing Multimedia content-driven applications

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Saksham: Resource Aware Block Rearrangement Algorithm for Load Balancing in Hadoop

Saksham: Resource Aware Block Rearrangement Algorithm for Lo...

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International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Shah, Ankit Padole, Mamta Shankersinh Vaghela Bapu Inst Technol Gandhinagar 382650 India Maharaja Sayajirao Univ Baroda Vadodara 390002 India

Big Data Analytics demands huge processing power. distributed computing seems to be a viable solution with the usage of commodity computers. But, processing big data optimally, on distributed systems poses the number of challenges, such as seamlessly integrating different resources of distributed systems. Hadoop provides this ease and hence is one of the most commonly used tools for the processing of big data. But, it is observed that the performance of Hadoop is compromised, when it comes to the heterogeneous environment. This paper proposes Saksham: The block rearrangement algorithm which optimizes the processing time by efficient file system management in the heterogeneous environment. Our proposed scheme successfully optimizes the big data processing over the homogeneous and heterogeneous environment. To achieve better performance for big data processing, we target two important aspects of heterogeneous distributed computing: file system management and process management. First, file system management basically controls the block placement and allows us to rearrange the blocks on specified nodes, considering the node's storage and processing capability. Second, we use the concept of node labeling and scheduling to achieve better process management. The results prove that the proposed approach has optimized job execution time along with reduced latency and data-skew substantially. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Hadoop load balancing Custom block placement Hadoop performance improvement Big data processing heterogeneous distributed computing

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Saksham: Resource Aware Block Rearrangement Algorithm for Load Balancing in Hadoop

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Procedia Computer Science 2020年 167卷 47-56页

作者： Ankit Shah Mamta Padole Shankersinh Vaghela Bapu Institute of Technology Gandhinagar-382650 India The Maharaja Sayajirao University of Baroda Vadodara-390002 India

Big Data Analytics demands huge processing power. distributed computing seems to be a viable solution with the usage of commodity computers. But, processing big data optimally, on distributed systems poses the number of challenges, such as seamlessly integrating different resources of distributed systems. Hadoop provides this ease and hence is one of the most commonly used tools for the processing of big data. But, it is observed that the performance of Hadoop is compromised, when it comes to the heterogeneous environment. This paper proposes Saksham: The block rearrangement algorithm which optimizes the processing time by efficient file system management in the heterogeneous environment. Our proposed scheme successfully optimizes the big data processing over the homogeneous and heterogeneous environment. To achieve better performance for big data processing, we target two important aspects of heterogeneous distributed computing: file system management and process management. First, file system management basically controls the block placement and allows us to rearrange the blocks on specified nodes, considering the node’s storage and processing capability. Second, we use the concept of node labeling and scheduling to achieve better process management. The results prove that the proposed approach has optimized job execution time along with reduced latency and data-skew substantially.

关键词： Hadoop load balancing Custom block placement Hadoop performance improvement Big data processing heterogeneous distributed computing

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Lower bound of network dismantling problem

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CHAOS 2018年第6期28卷 063128-063128页

作者： Sun, Jiachen Liu, Rong Fan, Zhengping Xie, Jiarong Ma, Xiao Hu, Yanqing Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

The network dismantling problem is one of the most fundamental problems in network science. It aims to identify the minimum number of nodes, such that after their removal the network is broken into many disconnected pieces with a sub-extensive size. However, the identification of the minimum removed nodes belongs to the class of nondeterministic polynomial problems. Although many heuristic algorithms have been proposed to identify the removed nodes, the smallest dismantling set remains unknown. Therefore, the determination of a good lower bound of dismantling sets is of great significance to evaluating the performances of heuristic algorithms. The minimum number of deleted nodes to dismantle a network is strictly no smaller than that to dismantle its any subnetwork in nature. Any lower bound of a subnetwork is indeed a lower bound of the original network. Utilizing the heterogeneous degree distribution and 2-core properties, we find that with previous removal of some appropriate nodes, the lower bound obtained on the basis of the subnetwork is counterintuitively significantly better than the one obtained directly on the original network, especially for the real-world networks. Published by AIP Publishing.

关键词： HEURISTIC algorithms POLYNOMIALS HEURISTIC programming heterogeneous distributed computing SET theory

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Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system

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SUSTAINABLE computing-INFORMATICS & SYSTEMS 2015年 5卷 14-30页

作者： Khemka, Bhavesh Friese, Ryan Pasricha, Sudeep Maciejewski, Anthony A. Siegel, Howard Jay Koenig, Gregory A. Powers, Sarah Hilton, Marcia Rambharos, Rajendra Poole, Steve Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Oak Ridge Natl Lab One Bethel Valley RdPOB 2008MS-6164 Oak Ridge TN 37831 USA US Dept Def Washington DC 20001 USA

The need for greater performance in high performance computing systems combined with rising costs of electricity to power these systems motivates the need for energy-efficient resource management. Driven by the requirements of the Extreme Scale Systems Center at Oak Ridge National Laboratory, we address the problem of scheduling dynamically-arriving tasks to machines in an oversubscribed and energy constrained heterogeneous distributed computing environment. Our goal is to maximize total "utility" earned by the system, where the utility of a task is defined by a monotonically-decreasing function that represents the value of completing that task at different times. To address this problem, we design four energy-aware resource allocation heuristics and compare their performance to heuristics from the literature. For our given energy-constrained environment, we also design an energy filtering technique that helps some heuristics regulate their energy consumption by allowing tasks to only consume up to an estimated fair-share of energy. Extensive sensitivity analyses of the heuristics in environments with different levels of heterogeneity show that heuristics with the ability to balance both energy consumption and utility exhibit the best performance because they save energy for use by future tasks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： High performance computing system Energy-constrained computing heterogeneous distributed computing Energy-aware resource management

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Energy-Aware Resource Management for computing Systems 7

Energy-Aware Resource Management for Computing Systems

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7th International Conference on Contemporary computing (IC3)

作者： Siegel, Howard Jay Khemka, Bhavesh Friese, Ryan Pasricha, Sudeep Maciejewski, Anthony A. Koenig, Gregory A. Powers, Sarah Hilton, Marcia Rambharos, Rajendra Okonski, Gene Poole, Steve Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA US Dept Def Washington DC 20001 USA

ISBN: (纸本)9781479951734

This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [1], [2]. We address the problem of assigning dynamically-arriving tasks to machines in a heterogeneous computing environment. These machines execute a workload composed of different tasks, where the tasks have diverse computational requirements. Each task has a utility function associated with it that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of our resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying an energy constraint. We describe example energy-aware resource management methods to accomplish this goal, and compare their performance. We also study the biobjective problem of maximizing system utility and minimizing the system energy consumption. This analysis technique allows system administrators to investigate the trade-offs between these conflicting goals.

关键词： high performance computing system heterogeneous distributed computing energy-aware computing resource management bi-objective optimization

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Task assignment for minimizing application completion time using honeybee mating optimization

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Frontiers of Computer Science 2013年第3期7卷 404-415页

作者： Qinma KANG Hong HE School of Information Engineering Shandong University Weihai 264209 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization （HBMO） algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.

关键词： heterogeneous distributed computing task assignment task interaction graph honeybee mating optimization meta-heuristics

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HONEYBEE MATING OPTIMIZATION ALGORITHM FOR TASK ASSIGNMENT IN heterogeneous computing SYSTEMS

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INTELLIGENT AUTOMATION AND SOFT computing 2013年第1期19卷 69-84页

作者： Kang, Qinma He, Hong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100864 Peoples R China Shandong Univ Weiha Sch Informat Engn Weihai 264209 Peoples R China

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for solving the problem with the objective of minimizing the total execution and communication costs. We discuss the adaptation and implementation of the HBMO search strategy to the task assignment problem. Through simulations over a wide range of parameters, we demonstrate the performance of our method by comparing it with three existing task assignment techniques from the literature.

关键词： Meta-heuristics Task assignment heterogeneous distributed computing Task interaction graph Honeybee mating optimization

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Dynamic task flow scheduling for heterogeneous distributed computing: Algorithm and strategy

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2007年第4期E90D卷 736-744页

作者： Sun, Wei Zhang, Yuanyuan Inoguchi, Yasushi JAIST Sch Informat Sci Nomi 9231292 Japan Fujitsu Labs Ltd Peta Scale Comp Res Ctr Kawasaki Kanagawa 2118588 Japan JAIST Ctr Informat Sci Nomi 9231292 Japan

heterogeneous distributed computing environments are well suited to meet the fast increasing computational demands. Task scheduling is very important for a heterogeneous distributed system to satisfy the large computational demands of applications. The performance of a scheduler in a heterogeneous distributed system normally has something to do with the dynamic task flow, that is, the scheduler always suffers from the heterogeneity of task sizes and the variety of task arrivals. From the long-term viewpoint it is necessary and possible to improve the performance of the scheduler serving the dynamic task flow. In this paper we propose a task scheduling method including a scheduling strategy which adapts to the dynamic task flow and a genetic algorithm which can achieve the short completion time of a batch of tasks. The strategy and the genetic algorithm work with each other to enhance the scheduler's efficiency and performance. We simulated a task flow with enough tasks, the scheduler with our strategy and algorithm, and the schedulers with other strategies and algorithms. We also simulated a complex scenario including the variant arrival rate of tasks and the heterogeneous computational nodes. The simulation results show that our scheduler achieves much better scheduling results than the others, in terms of the average waiting time, the average response time, and the finish time of all tasks.

关键词： heterogeneous distributed computing task scheduling task flow genetic algorithm scheduling strategy

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