Implementation of hierarchical F-channels for high-performance distributed computing

Summary High performance distributed computing systems require high performance communication systems.F-channels andHierarchical F-channels address this need by permitting a high level of concurrency like non-FIFO channels while retaining the simplicity of FIFO channels critical to the design and proof of many distributed algorithms. In this paper, we present counter-based implementations for F-channels and Hierarchical F-channels using message augmentation-appending control information to a message. These implementations guarantee that no messages are unnecessarily delayed at the receiving end. Keith Shafer received the B.A. degree in computer science and mathematics in 1986 from Mount Vernon Nazarene College, Mount Vernon, Ohio, USA, and the M.S. and Ph.D. degrees in computer science from The Ohio State University, Columbus, Ohio, USA, in 1988 and 1992, respectively. He is currently a Senior Research Scientist at OCLC Online Computer Library Center Inco, Dublin, OH, USA. His research interests include tools for comparing logical channels and methods for automatically constructing corpus grammars from tagged documents as an aid for database preparation and document conversion. Dr. Shafer is a member of the IEEE Computer Society. Mohan Ahuja received the M.A. degree in 1983 and the Ph.D. degree in 1985, both in computer science, from the University of Texas at Austin. He is currently with Department of Computer Science and Engineering, Univ. of California, San Diego. His recent research contributions include Global Flushing, message receipt in Receive-Phases, Incremental Publication of a Partial Order, Design of Highways (a high-performance distributed programming system) and — in collaboration with others — Passive-space and Time View, Performance evaluation of F-Channels, and Units of Computation in Fault-Tolerant Distributed Systems. His current research interests are in high-performance distributed communication and computing architectures, building high-performance systems, distributed operating systems, distributed algorithms, fault tolerance, and performance evaluation.Parts of this paper appeared in two conference papers, (1) Distributed Modeling and Implementation of High Performance Communication Architectures, in proceedings of the Thirteenth IEEE International Conference on Distributed Computing Systems, papes 56–65, 1993 and (2) Process-Channel_agem-Process model of asynchronous distributed communication, in proceedings of the Twelfth IEEE International Conference on Distributed Computing Systems, pages 4–11, 1992     

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.     

分布式计算中新兴古典经济学的资源分配方法

为了提高分布式计算环境下资源分配的效率,提出一种基于新兴古典经济学的资源分配方法,其重点关注如何提高整个系统的性能,使得客户得到的整体效用最大。通过将资源分配问题转化成专业化分工问题,应用超边际分析求出分配方案的最优解,从而进行分配策略制定和分配结构的动态调整。仿真试验证明,该方法能够有效地对分布式计算环境下的资源进行分配。     

通信网络随机模拟的快速方法研究

引入现代控制科学离散事件动态系统摄动分析思想，提出通信网络随机模拟的快速并行算法。在一台个人计算机上根据被模拟网络在一组参数下的仿真样本轨迹，同时构造一簇不同参数集合的网络系统样本轨迹。     

ScalaTrace: Scalable compression and replay of communication traces for high-performance computing

Characterizing the communication behavior of large-scale applications is a difficult and costly task due to code/system complexity and long execution times. While many tools to study this behavior have been developed, these approaches either aggregate information in a lossy way through high-level statistics or produce huge trace files that are hard to handle.     

A high-performance distributed algorithm for mining association rules

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speed-up with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm. As a result of this tighter bound and by utilizing the combined memory of several computers, the algorithm generates far fewer candidates than comparable sequential algorithms—the same order of magnitude as the optimum.     

An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems

This paper investigates the problem of allocating parallel application tasks to processors in heterogeneous distributed computing systems with the goal of maximizing the system reliability. The problem of finding an optimal task allocation for more than three processors is known to be NP-hard in the strong sense. To deal with this challenging problem, we propose a simple and effective iterative greedy algorithm to find the best possible solution within a reasonable amount of computation time. The algorithm first uses a constructive heuristic to obtain an initial assignment and iteratively improves it in a greedy way. We study the performance of the proposed algorithm over a wide range of parameters including problem size, the ratio of average communication time to average computation time, and task interaction density. The viability and effectiveness of our algorithm is demonstrated by comparing it with recently proposed task allocation algorithms for maximizing system reliability available in the literature.     

File allocation in a distributed database

A File allocation problem is studied in this paper. The problem formulated is to minimize transmission cost where the constraints are the number of files and the number of copies. The transmission cost also reflects concurrency control cost which is essential in a distributed database system. It is demonstrated that an optimal solution can be found by transforming this problem to a network flow problem.     

Remote database access in the distributed computing environment

This paper describes the design, implementation and testing of a set of software modules that are used for remote database access in a distributed computing environment. The goal of our research and development is to implement a client server model using Structured Query Language (SQL) functions in the Open Software Foundation's (OSF) distributed computing environment (DCE). This design is compared with another which simply uses the sockets application programming interface (API), running over the transmission control protocol/internet protocol (TCP/IP), and is implemented in subroutines that act similar to the remote procedure call (RPC) generated stub code. The prototypes for the remote SQL access project are implemented using an IBM RISC System/6000 (the client) running the AIX operating system and an IBM AS/400 (the server) running the OS/400 operating system. We selected the AS/400 for its database abilities, and the RISC System/6000 since the DCE software is available for it.     

A self-adaptable query allocation framework for distributed information systems

In large-scale distributed information systems, where participants are autonomous and have special interests for some queries, query allocation is a challenge. Much work in this context has focused on distributing queries among providers in a way that maximizes overall performance (typically throughput and response time). However, preserving the participants’ interests is also important. In this paper, we make the following contributions. First, we provide a model to define the participants’ perception of the system regarding their interests and propose measures to evaluate the quality of query allocation methods. Then, we propose a framework for query allocation called Satisfaction-based Query Load Balancing (SQLB, for short), which dynamically trades consumers’ interests for providers’ interests based on their satisfaction. Finally, we compare SQLB, through experimentation, with two important baseline query allocation methods, namely Capacity based and Mariposa-like. The results demonstrate that SQLB yields high efficiency while satisfying the participants’ interests and significantly outperforms the baseline methods. Work partially funded by ARA “Massive Data” of the French ministry of research (Respire project) and the European Strep Grid4All project.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

A macroeconomic model for resource allocation in large-scale distributed systems

In this paper we discuss an economic model for resource sharing in large-scale distributed systems. The model captures traditional concepts such as consumer satisfaction and provider revenues and enables us to analyze the effect of different pricing strategies upon measures of performance important for the consumers and the providers. We show that given a particular set of model parameters the satisfaction reaches an optimum; this value represents the perfect balance between the utility and the price paid for resources. Our results confirm that brokers play a very important role and can influence positively the market. We also show that consumer satisfaction does not track the consumer utility; these two important performance measures for consumers behave differently under different pricing strategies. Pricing strategies also affect the revenues obtained by providers, as well as, the ability to satisfy a larger population of users.     

Automatic data allocation to minimize communication on SIMD machines

Straightforward compilation of array operations onto massively parallel SIMD machines results in a significant amount of interprocessor data motion. Careful allocation of data across the processors eliminates much of this interprocessor data motion. Researchers are working on extending programming languages to include user directives for specifying good data allocation. Our focus is to automate the data allocation through compiler techniques to achieve portability without sacrificing efficiency. These techniques can be used to fully automate the data allocation process or can be integrated with alignment directives.We present here a complete compiler algorithm for the automatic layout of data to minimize interprocessor data motion. Arrays are aligned by mapping them onto the processors based on their usage. Arrays may be mapped differently in different sections of the program, eliminating much of the interprocessor data motion resulting from a static mapping of arrays. We describe an integrated technique for determining the alignment of arrays locally within regions of the program and minimizing communication globally among these regions. This technique starts with the alignments specified by the directives, if any, and determines the alignment for the remaining arrays.The algorithms proposed in this paper were used in the SIMD compilers at Compass, Inc. Preliminary results from the initial implementation of the data optimization techniques described here suggest a significant decrease of the interprocessor data motion. More analysis is required to better understand the range of expected gains and the conditions under which those gains are achieved.The research described in this paper was supported in part by the Defense Advanced Research Projects Agency under contracts N00014-87K-0825, F19628-92-C-0045, and N00014-91-J-1698 and in part by a National Science Foundation Presidential Young Investigator Award, grant MIP-8657531, with matching funds from General Electric Corporation, IBM Corporation, and AT&T.     

Cost-oriented task allocation and hardware redundancy policies in heterogeneous distributed computing systems considering software reliability

Task allocation policy and hardware redundancy policy for distributed computing system (DCS) are of great importance as they affect many system characteristics such as system cost, system reliability and performance. In recent years, abundant research has been carried out on the optimal task allocation and/or hardware redundancy problem, most of which took a reliability-oriented approach, i.e., the optimization criterion was system reliability maximization. Nevertheless, besides system reliability, other system characteristics such as system cost may be of great concern to management. In this paper, we take a cost-oriented approach to the optimal task allocation and hardware redundancy problem for DCS, which addresses both system cost and system reliability issues. A system cost model which could reflect the impact of system unreliability on system cost is developed, and by minimizing the total system cost, a satisfactory level of system reliability could be reached simultaneously. In the reliability modeling and analysis of DCS, we take both hardware reliability and software reliability into account. Two numerical examples are given to illustrate the formulation and solution procedures, in which genetic algorithm is used. Results show that based on the developed system cost model, appropriate decision-makings on task allocation and hardware redundancy policies for DCS could be made, and the result obtained seems to be a fairly good trade-off between system cost and system reliability.     

An efficient approach for distributed dynamic channel allocation with queues for real-time and non-real-time traffic in cellular networks

We are witnessing these days a rapid growth of mobile users. Therefore, frequency spectrum must be efficiently utilized, as available frequency spectrum is limited. This paper proposes a channel allocation scheme with efficient bandwidth reservation, which initially reserves some channels for handoff calls, and later reserves the channels dynamically, based on the user mobility. The direction of user mobility may not be straight always, but the user may also go left, right or backwards. Thus, QoS can be improved, if the channel reservation is made based upon the user mobility and the location of the user. We devise here a new algorithm that deals with multiple traffic systems by modifying the existing DDCA algorithm [Krishna, P.V., Iyengar, N.Ch.S.N., 2008. Optimal channel allocation algorithm with efficient channel reservation for cellular networks. International Journal of Communication Networks and Distributed Systems 1 (1), 33-51]. This algorithm reserves more channels for hot cells, less number of channels for cold cells and an average number of channels for the medium cells. Furthermore, we maintain queues for all types of calls. We model the system by a three-dimensional Markov Chain and compute the QoS parameters in terms of the blocking probability of originating calls and the dropping probability of handoff calls. The results indicate that the proposed channel allocation scheme exhibits better performance by considering the above mentioned user mobility, type of cells, and maintaining of the queues for various traffic sources. In addition, it can be observed that our approach reduces the dropping probability by using reservation factor.     

A near-optimal database allocation for reducing the average waiting time in the grid computing environment

In a grid computing environment, a great many users may access the same database simultaneously. To reduce the average waiting time for all users, a grid designer usually replicates the frequently accessed database among nodes based on the load balance heuristic. On the other hand, users may raise identical queries regarding an issue of interest, e.g., stock information, on a database and each of the queries will be directed to any node having a replica of that database. That is, the same answer will be determined by multiple nodes. Consequently, there exist two shortcomings of poor data sharing and duplicate calculations if the database is not replicated and allocated adequately. In this paper, we aim to minimize average waiting time and try to overcome the two shortcomings by performing database allocation over multiple nodes without any replication. The main idea behind the proposed method is to map the original problem to the Euclidean space Rⁿ and to solve the mapped problem in Rⁿ by a gradient-based optimization technique. The theoretical analyses ensure that the proposed method can converge linearly and achieve near-optimal results.     

An efficient non-contiguous processor allocation strategy for 2D mesh connected multicomputers

In non-contiguous allocation, a job request can be split into smaller parts that are allocated possibly non-adjacent free sub-meshes rather than always waiting until a single sub-mesh of the requested size and shape is available. Lifting the contiguity condition is expected to reduce processor fragmentation and increase system utilization. However, the distances traversed by messages can be long, and as a result the communication overhead, especially contention, is increased. The extra communication overhead depends on how the allocation request is partitioned and assigned to free sub-meshes. In this paper, a new non-contiguous processor allocation strategy, referred to as Greedy-Available-Busy-List, is suggested for the 2D mesh network. Request partitioning in our suggested strategy is based on the sub-meshes available for allocation. To evaluate the performance improvement achieved by our strategy and compare it against well-known existing non-contiguous and contiguous strategies, we conduct extensive simulation runs under the assumption of wormhole routing and three communication patterns, notably one-to-all, all-to-all and random. The results show that the new strategy can reduce the communication overhead and substantially improve performance in terms of job turnaround time and system utilization.     

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.     

多用户分布式MIMO-OFDM系统的多维资源分配算法

针对多用户分布式MIMO-OFDM系统中的资源分配问题,结合分布式架构特点,提出了一种基于分级优化的天线、子载波与功率联合分配算法.该算法将三维的资源联合分配问题分级转换为两次二维资源联合分配问题,即先引入端口并行处理机制,完成天线与子载波的分配,形成"用户-子信道对",进而采用注水功率分配的方式,完成功率在"用户-子...     

A security framework in G-Hadoop for big data computing across distributed Cloud data centres

MapReduce is regarded as an adequate programming model for large-scale data-intensive applications. The Hadoop framework is a well-known MapReduce implementation that runs the MapReduce tasks on a cluster system. G-Hadoop is an extension of the Hadoop MapReduce framework with the functionality of allowing the MapReduce tasks to run on multiple clusters. However, G-Hadoop simply reuses the user authentication and job submission mechanism of Hadoop, which is designed for a single cluster. This work proposes a new security model for G-Hadoop. The security model is based on several security solutions such as public key cryptography and the SSL protocol, and is dedicatedly designed for distributed environments. This security framework simplifies the users authentication and job submission process of the current G-Hadoop implementation with a single-sign-on approach. In addition, the designed security framework provides a number of different security mechanisms to protect the G-Hadoop system from traditional attacks.