Service-oriented systems are widely-employed in e-business, e-government, finance, management systems, and so on. Service fault tolerance is one of the most important techniques for building highly reliable service-or...
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Service-oriented systems are widely-employed in e-business, e-government, finance, management systems, and so on. Service fault tolerance is one of the most important techniques for building highly reliable service-oriented systems. In this paper, we provide an overview of various service fault tolerance techniques,including sections on fault tolerance strategy design, fault tolerance strategy selection, and Byzantine fault tolerance. In the first section, we introduce the design of static and dynamic fault tolerance strategies, as well as the major problems when designing fault tolerance strategies. After that, based on various fault tolerance strategies, in the second section, we identify significant components from a complex service-oriented system, and investigate algorithms for optimal fault tolerance strategy selection. Finally, in the third section, we discuss a special type of service fault tolerance techniques, i.e., the Byzantine fault tolerance.
The big data era is characterized by the emergence of live data with high volume and fast arrival rate, it poses a new challenge to stream processing applications: how to process the unbounded live data in real time w...
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ISBN:
(纸本)9781509053827
The big data era is characterized by the emergence of live data with high volume and fast arrival rate, it poses a new challenge to stream processing applications: how to process the unbounded live data in real time with high throughput. The sliding window technique is widely used to handle the unbounded live data by storing the most recent history of streams. However, existing centralized solutions cannot satisfy the requirements for high processing capacity and low latency due to the single-node bottleneck. Moreover, existing studies on distributed windows primarily focus on specific operators, while a general framework for processing various window-based operators is wanted. In this paper, we firstly classify the window-based operators to two categories: data-independent operators and data-dependent operators. Then, we propose GDSW, a general framework for distributed count-based sliding window, which can handle both of data-independent and data-dependent operators. Besides, in order to balance system load, we further propose a dynamic load balance algorithm called DAD based on buffer usage. Our framework is implemented on Apache Storm 0.10.0. Extensive evaluation shows that GDSW can achieve sub-second latency, and 10X improvement in throughput compared with centralized processing, when processing rapid data rate or big size window.
The traditional identifier locator split network has many issues such as inflexibility, hard to innovate and difficult to deploy. SDN (Software Defined Network) provides a new direction for designing flexible identifi...
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ISBN:
(纸本)9781467386456
The traditional identifier locator split network has many issues such as inflexibility, hard to innovate and difficult to deploy. SDN (Software Defined Network) provides a new direction for designing flexible identifier locator split network. The recent identifier locator split network based on SDN use the OpenFlow switch directly via rewritting the address, which lacks the scalability and utilizes locator address ineffectively. An OpenFlow switch named IDOpenFlow is proposed to support the communication based on identifier. IDOpenFlow switch provides the communication mechanism via encapsulating the packets, which has good scalability and utilizing locator address effectively. IDOpenFlow switch encapsulates and decapsulates packets according flow entries which are installed by SDN controller. Moreover, the prototype system shows that IDOpenFlow effectively supports the communication for both the fixed node and the mobile node. With respect to the issues of software forwarding performance, a high-performance IDOpenFlow switch based on Intel DPDK (which is named A-IDOpenFlow) is proposed. The results of Ixia test tool show that: 1) for packets more than 128 bytes, A-IDOpenFlow switch supports the communication based on identifier at rate of 10Gbit/s; 2) for small packet of 64 bytes, the rate of A-IDOpenFlow is 7.25 times faster than the rate of IDOpenFlow.
Anomaly detection over multi-dimensional data stream has attracted considerable attention recently in various fields, such as network, finance and aerospace. In many cases, anomalies are composed of a sequence of mult...
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ISBN:
(纸本)9781509053827
Anomaly detection over multi-dimensional data stream has attracted considerable attention recently in various fields, such as network, finance and aerospace. In many cases, anomalies are composed of a sequence of multi-dimensional data, and it's necessary to detect this type of anomalies accurately and efficiently over data stream. Existing online methods of anomaly detection merely focus on the single-dimensional sequence. What's more, current studies about multi-dimensional sequence are mainly concentrated on static database. However, the anomaly detection for multi-dimensional sequence over data stream is much more difficult, due to the complexity of multidimensional sequence processing, the dynamic nature of data stream and the unbalance between normal and abnormal data. Facing these challenges, we propose an anomaly detection method for multi-dimensional sequence over data stream based on cost sensitive support vector machine (C-SVM) called ADMS. First, to improve the accuracy and efficiency, the ADMS transforms multi-dimensional sequences into feature vectors in a lossless way and prunes worthless features of these vectors. And then, the ADMS can detect abnormal sequences over dynamically imbalanced data stream by lively testing these vectors based on C-SVM. Experiments show that the false negative rate (FNR) of the ADMS is lower than 5%, the false positive rate (FPR) is lower than 7%, and the throughput is improved 42% by pruning worthless features. In addition, the AMDS performs well when there are concept drifts over the data stream.
Fault resilience has became a major issue for HPC systems, particularly, in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. MPI-level fault tolerant constru...
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Convolution operation is the most important and time consuming step in a convolution neural network *** this work,we analyze the computing complexity of direct convolution and fast-Fourier-transform-based(FFT-based) *...
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ISBN:
(纸本)9781510835368
Convolution operation is the most important and time consuming step in a convolution neural network *** this work,we analyze the computing complexity of direct convolution and fast-Fourier-transform-based(FFT-based) *** creatively propose CS-unit,which is equivalent to a combination of a convolutional layer and a pooling layer but more *** computing complexity of and some other similar operation is demonstrated,revealing an advantage on computation of ***,practical experiments are also performed and the result shows that CS-unit holds a real superiority on run time.
Powering is an important operation in many computation intensive workloads. This paper investigates the performance of different styles to calculate the powering operations from the application level. A series of smal...
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ISBN:
(纸本)9781509045181
Powering is an important operation in many computation intensive workloads. This paper investigates the performance of different styles to calculate the powering operations from the application level. A series of small benchmark codes that calculate the powering operations in different ways are designed. Their performance is evaluated on Intel Xeon CPU under Intel compilation environments. The results show that the number of floating-point operations and the related runtime are sensitive to the value of the exponent Y and how it is used. When Y is an immediate integer number whose value is known at compile time, the cost of powering is much less than the situation when Y is an integer variable whose value is known at runtime. When Y is defined as a real variable, the cost of powering is always high, be it equals to an integer number or not. Based on the investigations, performance optimizations are applied to a kernel subroutine from a real-world supersonic combustion simulation code, which intensively involves powering operations. The result shows that the performance of that subroutine is improved for 13.25 times on the Intel Xeon E5-2692 CPU.
In this research, we apply the Green's theory for converting the partial differential equation to the boundary integral equation for geometric transformation. Green's theory is designed specifically for integr...
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Binary Exchange Algorithm (BEA) always introduces excessive shuffle operations when mapping FFTs on vector SIMD DSPs. This can greatly restrict the overall performance. We propose a novel mod (2P-1) shuffle function a...
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ISBN:
(纸本)9781467390408
Binary Exchange Algorithm (BEA) always introduces excessive shuffle operations when mapping FFTs on vector SIMD DSPs. This can greatly restrict the overall performance. We propose a novel mod (2P-1) shuffle function and Mod-BEA algorithm (MBEA), which can halve the shuffle operation count and unify the shuffle mode. Such unified shuffle mode inspires us to propose a set of novel mod (2P-1) shuffle memory-access instructions, which can totally eliminate the shuffle operations. Experimental results show that the combination of MBEA and the proposed instructions can bring 17.2%-31.4% performance improvements at reasonable hardware cost, and compress the code size by about 30%.
Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those co...
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Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those code fragments are designed a priori, self-adaptive software cannot handle situations adequately when the contextual changes go beyond those that are originally anticipated. In this case, the original builtin adaptivity should be tuned. For example, new code should be added to provide the capacity to sense the unexpected environment or to replace outdated adaptation decision logic. The technical challenges in this process, especially that of tuning software adaptivity at runtime, cannot be understated. In this paper,we propose an architecture-centric application framework for self-adaptive software named Auxo. Similar to existing work, our framework supports the development and running of self-adaptive software. Furthermore,our framework supports the tuning of software adaptivity without requiring the running self-adaptive software to be terminated. In short, the architecture style that we are introducing can encapsulate not only general functional logic but also the concerns in the self-adaptation loop(such as sensing, decision, and execution)as architecture elements. As a result, a third party, potentially the operator or an augmented software entity equipped with explicit domain knowledge, is able to dynamically and flexibly adjust the self-adaptation concerns through modifying the runtime software architecture. To truly exercise, validate, and evaluate our approach,we describe a self-adaptive application that was deployed on the framework, and conducted several experiments involving self-adaptation and the online tuning of software adaptivity.
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