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arXiv 2021年

作者： Pasdar, Amirmohammad Dong, Zhongli Lee, Young Choon Aglive Lab 8 Hadenfeld Ave Macquarie ParkNSW2113 Australia Department of Computing Macquarie University SydneyNSW2109 Australia Center for Distributed and High Performance Computing School of Computer Science The University of Sydney NSW2006 Australia

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides;consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, blockchain oracles are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end. © 2021, CC BY.

关键词： Smart contract

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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Spark-Tuner: An Elastic Auto-Tuner for Apache Spark Streaming

Spark-Tuner: An Elastic Auto-Tuner for Apache Spark Streamin...

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IEEE International Conference on Cloud computing, CLOUD

作者： M.Reza HoseinyFarahabady Javid Taheri Albert Y. Zomaya Zahir Tari The University of Sydney School of Computer Science Center for Distributed and High Performance Computing Karlstad University Sweden RMIT University School of Science Australia

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Spark has emerged as one of the most widely and successfully used data analytical engine for large-scale enterprise, mainly due to its unique characteristics that facilitate computations to be scaled out in a distributed environment. This paper deals with the performance degradation due to resource contention among collocated analytical applications with different priority and dissimilar intrinsic characteristics in a shared Spark platform. We propose an auto-tuning strategy of computing resources in a distributed Spark platform for handling scenarios in which submitted analytical applications have different quality of service (QoS) requirements (e.g., latency constraints), while the interference among computing resources is considered as a key performance-limiting parameter. We compared Spark-Tuner to two widely used resource allocation heuristics in a large scale Spark cluster through extensive experimental settings across several traffic patterns with uncertain rate and application types. Experimental results show that with Spark-Tuner, the Spark engine can decrease the p-99 latency of high priority applications by 43% during the high-rate traffic periods, while maintaining the same level of CPU throughput across a cluster.

关键词： Cloud computing Tuners Quality of service Traffic control Sparks Resource management Engines

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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of Computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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Disk throughput controller for cloud data-centers 20

Disk throughput controller for cloud data-centers

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20th International Conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Hoseinyfarahabady, M. Reza Tari, Zahir Zomaya, Albert Y. University of Sydney Center for Distributed and High Performance Computing School of Computer Science NSW Australia RMIT University School of Science MelbourneVIC Australia

ISBN: (纸本)9781728126166

With the increasing popularity of virtual machine monitoring (VMM) technologies, performance variability among collocated virtual machines (VMs) can easily become a severe scalability issue. Particularly, it becomes a necessary for administrative team to control the performance degradation level in a shared environment when multiple I/O-intensive applications simultaneously request their I/O operations [1]. Nevertheless, adding several logical layers between the running applications and the physical storage system, as seen in contemporary virtualized storage devices, makes it considerably difficult to build a low overhead controlling mechanism for such systems (while each VM may running a separate operating system instance) [2]. In this paper, we propose a strategy based on control theory for managing the performance of several I/O requests, such as mean response times and read/write throughput in a consolidated environment where multiple virtual services can share access to a storage system. This scheme uses an approach for measuring the characterization of read/write performance attributes of each virtual services and also takes into account the run-time quality of service enforcement levels requested by them. This is formulated as an optimization problem where a reward function is defined to reduce the overall QoS violation incidents among all consolidated virtual services. performance evaluation is carried out by comparing the proposed solution with the default embedded Linux controller across a range of emulated application workloads in scenarios with multiple consolidated virtual containers. The results confirm that the proposed solution can reduce the overall QoS violation incident rates in scenarios in which the platform operates at a significant traffic load comparing to the default policy in LXC engine. © 2019 IEEE.

关键词： Controllers

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A Dynamic Resource Controller for Resolving Quality of Service Issues in Modern Streaming Processing Engines

A Dynamic Resource Controller for Resolving Quality of Servi...

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IEEE International Symposium on Network computing and Applications

作者： M. Reza HoseinyFarahabady Javid Taheri Albert Y. Zomaya Zahir Tari The University of Sydney School of Computer Science Center for Distributed and High Performance Computing NSW Australia Karlstad University Sweden RMIT University School of Science Victoria Australia

ISBN: (数字)9781728183268

ISBN: (纸本)9781728183275

Devising an elastic resource allocation controller of data analytical applications in virtualized data-center has received a great attention recently, mainly due to the fact that even a slight performance improvement can translate to huge monetary savings in practical large-scale execution. Apache Flink is among modern streamed data processing run-times that can provide both low latency and high throughput computation in to execute processing pipelines over high-volume and high-velocity data-items under tight latency constraints. However, a yet to be answered challenge in a large-scale platform with tens of worker nodes is how to resolve the run-time violation in the quality of service (QoS) level in a multi-tenant data streaming platforms, particularly when the amount of workload generated by different users fluctuates. Studies showed that a static resource allocation algorithm (round-robin), which is used by default in Apache Flink, suffer from lack of responsiveness to sudden traffic surges happening unpredictably during the run-time. In this paper, we address the problem of resource management in a Flink platform for ensuring different QoS enforcement levels in a platform with shared computing resources. The proposed solution applies theoretical principals borrowed from close-loop control theory to design a CPU and memory adjustment mechanism with the primary goal to fulfill the different QoS levels requested by submitted applications while the resource interference is considered as the critical performance-limiting factor. The performance evaluation is carried out by comparing the proposed resource allocation mechanism with two static heuristics (round robin and class-based weighted fair queuing) in a 80-core cluster under multiple traffic patterns resembling sudden changes in the incoming workloads of low-priory streaming applications. The experimental results confirm the stability of the proposed controller to regulate the underlying platform resources to smo

关键词： Quality of service Data processing Resource management Process control Time factors Degradation Surges

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Real-time stream data processing at scale 20

Real-time stream data processing at scale

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20th International Conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Hoseinyfarahabady, M. Reza Jannesari, Ali Bao, Wei Tari, Zahir Zomaya, Albert Y. University of Sydney Center for Distributed and High Performance Computing School of Computer Science NSW Australia Department of Computer Science Iowa State University IA United States RMIT University School of Science MelbourneVIC Australia

ISBN: (纸本)9781728126166

A typical scenario in a stream data-flow processing engine is that users submit continues queries in order to receive the computational result once a new stream of data arrives. The focus of the paper is to design a dynamic CPU cap controller for stream data-flow applications with real-time constraints, in which the result of computations must be available within a short time period, specified by the user, once a recent update in the input data occurs. It is common that the stream data-flow processing engine is deployed over a cluster of dedicated or virtualized server nodes, e.g., Cloud or Edge platform, to achieve a faster data processing. However, the attributes of incoming stream data-flow might fluctuate in an irregular way. To effectively cope with such unpredictable conditions, the underlying resource manager needs to be equipped with a dynamic resource provisioning mechanism to ensure the real-time requirements of different applications. The proposed solution uses control theory principals to achieve a good utilization of computing resources and a reduced average response time. The proposed algorithm dynamically adjusts the required quality of service (QoS) in an environment when multiple stream & data-flow processing applications concurrently run with unknown and volatile workloads. Our study confirms that such a unpredictable demand can negatively degrade the system performance, mainly due to adverse interference in the utilization of shared resources. Unlike prior research studies which assumes a static or zero correlation among the performance variability among consolidated applications, we presume the prevalence of shared-resource interference among collocated applications as a key performance-limiting parameter and confront it in scenarios where several applications have different QoS requirements with unpredictable workload demands. We design a low-overhead controller to achieve two natural optimization objectives of minimizing QoS violation amount an

关键词： Quality of service

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Q-Flink: A QoS-Aware Controller for Apache Flink

Q-Flink: A QoS-Aware Controller for Apache Flink

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： M. Reza HoseinyFarahabady Ali Jannesari Javid Taheri Wei Bao Albert Y. Zomaya Zahir Tari School of Computer Science Center for Distributed and High Performance Computing The University of Sydney Department of Computer Science Iowa State University IA USA Department of Mathematics and Computer Science Karlstad University Sweden School of Science RMIT University Australia

ISBN: (数字)9781728160955

Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing). Flink allows application developers to design and execute queries over continuous raw-inputs to analyze a large amount of streaming data in a parallel and distributed fashion. To increase the throughput of computing resources in stream processing platforms, a service provider might be tempted to use a consolidation strategy to pack as many processing applications as possible on the working nodes, with the hope of increasing the total revenue by improving the overall resource utilization. However, there is a hidden trap for achieving such a higher throughput solely by relying on an interference-oblivious consolidation strategy. In practice, collocated applications in a shared platform can fiercely compete with each others for obtaining the capacity of shared resources (e.g., cache and memory bandwidth) which in turn can lead to a severe performance degradation for all consolidated *** paper addresses the shared resource contention problem associated with the auto-resource controlling mechanism of Apache Flink engine running across a distributed cluster. A controlling strategy is proposed to handle scenarios in which stream processing applications may have different quality of service (QoS) requirements while the resource interference is considered as the key performance-limiting parameter. The performance evaluation is carried out by comparing the proposed controller with the default Flink resource allocation strategy in a testbed cluster with total 32 Intel Xeon cores under different workload traffic with up to 4000 streaming applications chosen from various benchmarking tools. Experimental results demons

关键词： Quality of service Resource management Time factors Engines Data processing Interference Process control

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Untrusted Hardware Causes Double-Fetch Problems in the I/O Memory

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Journal of Computer Science & Technology 2018年第3期33卷 587-602页

作者： Kai Lu Peng-Fei Wang Gen Li Xu Zhou College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High-Performance Computing National University of Defense Technology Changsha 410073 China

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices （aka. the hardware double fetch）. Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O （input and output） memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

关键词： hardware double fetch double-fetch bug I/O memory peripheral device double-fetch vulnerability

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Disk Throughput Controller for Cloud Data-centers

Disk Throughput Controller for Cloud Data-Centers

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IEEE International Conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： M. Reza Hoseinyfarahabady Zahir Tari Albert Y. Zomaya Center for Distributed & High Performance Computing School of Computer Science The University of Sydney New South Wales Australia School of Science RMIT University Melbourne VIC Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

关键词： Quality of service Virtual machine monitors performance evaluation Containers Interference Linux Resource management

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