With the advent in WAN of delay sensitive technical applications, such as remote control, signal propagation delay becomes a major problem. The extent of the problem is analysed by lower bounding the delay in relation...
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ISBN:
(纸本)1932415262
With the advent in WAN of delay sensitive technical applications, such as remote control, signal propagation delay becomes a major problem. The extent of the problem is analysed by lower bounding the delay in relation to different transmission media. Requirements for reliability and bandwidth are considered. The consequences of this bounding for communication network planning and provisioning are discussed in relation to mitigating the effects of signal propagation delay as a barrier to the penetration of control applications into WAN.
Cloud computing has transformed the means of computing in recent years with several benefits over traditional systems, like scalability and high availability. However, there are still some opportunities, especially in...
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ISBN:
(纸本)9781728116440
Cloud computing has transformed the means of computing in recent years with several benefits over traditional systems, like scalability and high availability. However, there are still some opportunities, especially in the area of resource provisioning and scaling [13]. Since workload may fluctuate a lot in certain environments, over-provisioning is a common practice to avoid abrupt Quality of Service (QoS) drops that may result in Service Level Agreement (SLA) violations, but at the price of an increase in provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a cloud can be improved [13]. Knowing demand in advance allows the previous allocation of sufficient resources to maintain QoS and avoid SLA violations [1]. This paper presents the advantages and disadvantages of three workload prediction techniques when applied in the context of cloud computing. Our preliminary results compare ARIMA, MLP, and GRU under different cloud configurations to help administrators choose the more appropriate and efficient predictive model for their specific problem.
Pervasive Grid Computing Platforms include centralized computing nodes (e.g. parallel servers) as well as decentralized and mobile devices. Pervasive Grid applications include data-and computing-intensive components w...
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ISBN:
(纸本)9780769539393
Pervasive Grid Computing Platforms include centralized computing nodes (e.g. parallel servers) as well as decentralized and mobile devices. Pervasive Grid applications include data-and computing-intensive components which can be mapped also onto decentralized and mobile nodes. The effective and practical success of this mapping resides also in deriving proper configurations of applications which consider the limited memory capabilities of those resources. In this paper we target this issue by showing how we can study and configure the memory requirements of an Emergency Management application. We present our solutions by using the ASSISTANT programming model for Pervasive Grid applications.
Large-scale workflows are becoming increasingly important in both the scientific research and business domains. Science and commerce have both experienced an explosion in the sheer amount of data that must be analyzed...
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ISBN:
(纸本)9781932415605
Large-scale workflows are becoming increasingly important in both the scientific research and business domains. Science and commerce have both experienced an explosion in the sheer amount of data that must be analyzed. An important tool for analyzing these huge data sets is a compute "cluster" of hundreds or thousands of machines. However, debugging and tuning clusters requires specialized tools. Current cluster performance tools are more oriented towards tightly coupled parallelapplications. We describe how the NetLogger Toolkit methodology is more appropriate for this class of cluster computing, and describe our new automatic workflow anomaly detection component. We also describe how this methodology is being used in the Nearby Supernova Factory (SNfactory) project at Lawrence Berkeley National Laboratory.
Intrusion prevention system is the most important and popular tool in information security. It has been widely used to identify potential threats and respond to them swiftly. However, the existing IPS based on regular...
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ISBN:
(纸本)9781538637906
Intrusion prevention system is the most important and popular tool in information security. It has been widely used to identify potential threats and respond to them swiftly. However, the existing IPS based on regular expression matching is time consuming and do not support large data well, since it scans and processes input linearly. To enhance conventional approach's parallelism and improve its efficiency for processing problems with large-scale data, this paper raised a speculative parallel intrusion prevention system based on Apache Spark. In the proposed system, the input packets are speculatively divided into several chunks, and then distributed into Apache Spark for parallelprocessing. After processing, the results are collected and evaluated to eliminate incorrect speculations. Experiments show that for large-scale data, by comparing with the conventional approach, the proposed system could markedly shorten the execution time for intrusion prevention. It can be proved that by adopting our novel system, the efficiency of intrusion prevention can be significantly enhanced.
With current trends toward deeper pipelined, superscalar processors and the high frequency of branch instructions, accurate branch prediction becomes increasingly important. This paper introduces an innovative hybrid ...
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ISBN:
(纸本)1892512459
With current trends toward deeper pipelined, superscalar processors and the high frequency of branch instructions, accurate branch prediction becomes increasingly important. This paper introduces an innovative hybrid branch prediction method that applies a four-class dynamic classifier. It employs various prediction methods for branch instructions of different classes. The simulation results indicate that this new method provides an improvement of accuracy over the published branch prediction methods. In addition, the Four-class Dynamic Branch Predictor performs specifically well when hardware resources are limited.
A heterogeneous distributed database system (HDDBS) is designed to provide universal access to distributed data across multiple autonomous, heterogeneous local database systems (LDBSs). In this paper, we propose a glo...
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ISBN:
(纸本)1892512459
A heterogeneous distributed database system (HDDBS) is designed to provide universal access to distributed data across multiple autonomous, heterogeneous local database systems (LDBSs). In this paper, we propose a global concurrency control (GCC) algorithm in HDDBS. The proposed GCC algorithm adopts the idea of message ordering in group communication. A necessary condition of GCC algorithm is that global transactions are serialized in the same order at all LDBSs that they execute. If all operations of a transaction are bundled in a single message and the message arrives at LDBSs in the same order using message ordering property, each LDBS can perform subtransactions in the same order. As a result, message ordering allows to determine easily the relative serialization order of global transactions, and then it can support a local autonomy without any information about the serialization order of transactions executing locally. And we propose a distributed database simulation model to evaluate the performance of the proposed algorithm under a wide variety of system configurations.
processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We are developing a compiler that processes data intensive applications written in a dialect ...
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ISBN:
(纸本)0769513638
processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We are developing a compiler that processes data intensive applications written in a dialect of Java and compiles them for efficient execution on distributed memory parallel machines. In this paper, we focus on the problem of generating correct and efficient communication for data intensive applications. We present static analysis techniques for 1) extracting a global reduction function from a data parallel loop, and 2) determining if a subscript function is monotonic. We also present a runtime technique for reducing the volume of communication during the global reduction phase. We have experimented with two data intensive applications to evaluate the efficacy of our techniques. Our results show that 1) our techniques for extracting global reduction functions and establishing monotonicity of subscript functions can successfully handle these applications, 2) significant reduction in communication volume and execution times is achieved through our runtime analysis technique, 3) runtime communication analysis is critical for achieving speedups on parallel configurations.
Current increases in the logic density of silicon circuits have made feasible the integration of multiple parallel compression engines (along with their own dedicated memory) onto a single chip. This paper presents a ...
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ISBN:
(纸本)1892512416
Current increases in the logic density of silicon circuits have made feasible the integration of multiple parallel compression engines (along with their own dedicated memory) onto a single chip. This paper presents a range of alternative routing strategies for data compression, which are investigated in order to understand tradeoffs between compression, latency and throughput. The performance of the compressors is tested using a variety of realistic datasets. For parallel compression it is clear that careful consideration must be given to the routing of the input and output stream.
In this paper, we present a quasi-Newton parallel algorithm to solve the Inverse Additive Symmetric Eigenvalue Problem. Our approach differs from other state-of-the-art algorithms that use Newton type iteration, in th...
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ISBN:
(纸本)1892512416
In this paper, we present a quasi-Newton parallel algorithm to solve the Inverse Additive Symmetric Eigenvalue Problem. Our approach differs from other state-of-the-art algorithms that use Newton type iteration, in that we carry out the computation of the Jacobian matrix once in the first iteration, thus giving better performance than Newton algorithms even in the sequential case. In addition, our algorithm has been parallelized by using public domain software like ScaLAPACK and MPI, thus guaranteeing portability. Good performances have been obtained on a cluster of PCs connected through a Fast Ethernet network.
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