Improvement In Execution Time Research Articles

Integrated Geo Cloud Solution for Seismic Data Processing

Oil industries generate an enormous volume of digitized data (e.g., seismic data) as a part of their seismic study and move it to the cloud for downstream applications. Moving massive data into the cloud can pose many challenges, especially to Commercial-off-the-shelf geoscience applications as they require very high compute and disk throughput. This paper proposes a digital transformation framework for efficient seismic data processing and storage comprising of: (a) Novel Data storage options, (b) Cloud-based HPC framework for efficient seismic data processing, and (c) MD5 hash calculation using the MapReduce pattern with Hadoop clusters. Azure cloud platform is used to validate the proposed framework and compare it with the existing process. Experimental results show a significant improvement in execution time, throughput, efficiency, and cost. The proposed framework can be used in any domain which deals with extensive data requiring high compute and throughput.

Fast semi-supervised evidential clustering

Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semi-supervised clusterings, Semi-supervised Evidential Clustering (SECM) deals with the problem of uncertain/imprecise labels and creates a credal partition. In this work, a new heuristic algorithm, called SECM-h, is presented. The proposed algorithm relaxes the constraints of SECM in such a way that the optimization problem is solved using the Lagrangian method. Experimental results show that the proposed algorithm largely improves execution time while accuracy is maintained.

Parallel Quadri-valent Quantum-Inspired Gravitational Search Algorithm on a heterogeneous platform for wireless sensor networks

Sensor nodes in a wireless sensor Network are assigned for different operational modes to perfume application-specific objectives. The decision to assign operational modes to nodes is a challenging problem in the presence of multiple criteria including energy-efficient, maintaining network connectivity, and fulfilling application goals. Several metaheuristic methods are introduced in the literature to address this NP-hard problem, however, these methods require further improvements in execution-time and finding the optimum solution. In this research, we propose an improved version of a metaheuristic method called Quadri-valent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) to solve Quadri-valent problems by applying a Not Q-Gate and paralleling QQIGSA method on the graphics processing unit. The proposed method employs a heterogeneous platform and justifies its parameters. The experimental results show that the performance enhancement from 1.8 to 2.25 compared to the previous parallel implementations. Moreover, we achieve the speedup of 8 by using the proposed heterogeneous paralleling technique.

Towards enabling I/O awareness in task-based programming models

Storage systems have not kept the same technology improvement rate as computing systems. As applications produce more and more data, I/O becomes the limiting factor for increasing application performance. I/O congestion caused by concurrent access to storage devices is one of the main obstacles that cause I/O performance degradation and, consequently, total performance degradation.Although task-based programming models made it possible to achieve higher levels of parallelism by enabling the execution of tasks in large-scale distributed platforms, this parallelism only benefited the compute workload of the application. Previous efforts addressing I/O performance bottlenecks either focused on optimizing fine-grained I/O access patterns using I/O libraries or avoiding system-wide I/O congestion by minimizing interference between multiple applications.In this paper, we propose enabling I/O Awareness in task-based programming models for improving the total performance of applications. An I/O aware programming model is able to create more parallelism and mitigate the causes of I/O performance degradation. On the one hand, more parallelism can be created by supporting special tasks for executing I/O workloads, called I/O tasks, that can overlap with the execution of compute tasks. On the other hand, I/O congestion can be mitigated by constraining I/O tasks scheduling. We propose two approaches for specifying such constraints: explicitly set by the users or automatically inferred and tuned during application’s execution to optimize the execution of variable I/O workloads on a certain storage infrastructure.We implement our proposal using PyCOMPSs: a Task-based programming model for parallelizing Python applications. Our experiments on the MareNostrum 4 Supercomputer demonstrate that using I/O aware PyCOMPSs can achieve significant performance improvement in the total execution time of applications with different I/O workloads. This performance improvement can reach up to 43% of total application performance as compared to the I/O non-aware version of PyCOMPSs.

Accelerated execution via eager-release of dependencies in task-based workflows

Task-based programming models offer a flexible way to express the unstructured parallelism patterns of nowadays complex applications. This expressive capability is required to achieve maximum possible performance for applications that are executed in distributed execution platforms. In current task-based workflows, tasks are launched for execution when their data dependencies are satisfied. However, even though the data dependencies of a certain task might have been already produced, the execution of this task will be delayed until its predecessor tasks completely finish their execution. As a consequence of this approach of releasing dependencies, the amount of parallelism inherent in applications is limited and performance improvement opportunities are wasted. To mitigate this limitation, we propose an eager approach for releasing data dependencies. Following this approach, the execution of tasks will not be delayed until their predecessor tasks completely finish their execution, instead, tasks will be launched for execution as soon as their data requirements are available. Hence, more parallelism is exposed and applications can achieve higher levels of performance by overlapping the execution of tasks. Towards achieving this goal, in this paper we propose applying two changes to task-based workflow systems. First, modifying the dependency relationships of tasks to be specified not only in terms of predecessor and successor tasks but also in terms of the data that caused these dependencies. Second, triggering the release of dependencies as soon as a predecessor task generates the output data instead of having to wait until the end of the predecessor execution to release all of its dependencies. We realize this proposal using PyCOMPSs: a task-based programming model for parallelizing Python applications. Our experiments show that using an eager approach for releasing dependencies achieves more than 50% performance improvement in the total execution time as compared to the default approach of releasing dependencies.

Occlusion-robust method for RGB-D 6-DOF object tracking with particle swarm optimization

Visual trackers can be used to determine the trajectories of rigid 3D objects with 6-DOF. This functionality is required for many types of applications, such as augmented reality or robotics, and its efficiency is commonly related to the accuracy, robustness and execution time of these trackers. Several works contributed to the improvement of these techniques, among which stand out optimization-based, learning-based, and hybrid methods, that is, that use both cooperatively. However, despite the variety of 3D tracking object techniques, in general they have difficulty to perform tracking efficiently in challenging environments as those with occlusion or quick rotation and translation movements of the object. In this context, we propose improvements in the 6-DOF tracking of arbitrary 3D objects that uses particle swarm optimization and try to overcome those limitations. In this type of tracker, a top-down approach is used in which, for each video frame, a pose hypothesis set is created and optimized in order to find the pose as close as possible to the tracked object real pose. To perform this task, a fitness function uses 3D scene information obtained from RGB-D sensors to evaluate each hypothesis. The proposed approach tries to solve problems that in practice reduce the efficiency of this optimization, and are associated with the dynamism of the search space, the definition of the limits of the search subspace at each iteration, the exclusion of global optimum of search subspace, the premature convergence to local optima, the different types of object occlusion, the definition of a fitness function independent of the tracked object class and environment characteristics, and the execution time for processing all steps of the particle swarm optimization. Therefore, in this scenario we present a tracker algorithm with a novel fitness function based on the harmonic mean of 3D coordinate, color and normal vector distances. We also introduce a new approach for computing the boundaries of solutions subspace at runtime, observing the inertia of the target object. We present a robust method for filtering the visible model points. Finally, in order to improve execution time, we employ a dynamic region of interest selection in the scene point cloud and implement the particle swarm optimization algorithm completely in GPU. Experiments have shown that such changes led to improvements in accuracy, robustness and execution time. When compared to a state-of-the-art learning-based technique, our tracker was, on average, 19.3% and 16.3% more accurate with respect to translation and rotation errors, respectively, and presented 43.2% less tracking fails. Our tracker was also 5–17 times faster than an existing particle swarm optimization-based technique.

Multi-analysis surveillance and dynamic distribution of computational resources: Towards extensible, robust, and efficient monitoring of environments

Intelligent surveillance has been a hot area of research for the past two decades. However, the integral security of environments remains a challenge due to the presence of new types of threats and the very dynamism and complexity of these environments. To make further progress, new proposals are needed to facilitate the design and deployment of multi-analysis surveillance systems where different types of analysis are simultaneously performed and the available computation resources are limited. Such systems need to provide three main characteristics: extensibility, robustness and efficiency. Extensibility to add new analysis components when new events of interest must be monitored. Robustness to avoid that failures in one analysis component affect the rest. Efficiency to analyze environments in real-time and to support decision-making processes that address the detected anomalies.This paper proposes a formal model for the multi-analysis surveillance of environments by means of the named components of normality, designed to deploy surveillance systems that satisfies the three previously mentioned characteristics. Extensibility thanks to the activation and deactivation of components of normality based on the monitoring needs of the analyzed environment. Robustness as a result of the isolation of these components such that a failure in one of them does not spread to the rest. Finally, efficiency due to the dynamic allocation of resources that benefits the components that detect anomalies at a given time. The experimental results prove that such dynamic allocation improves execution times and reduces waiting times.

Efficient association mapping from k-mers—An application in finding sex-specific sequences

Genome wide association studies (GWAS) attempt to map genotypes to phenotypes in organisms. This is typically performed by genotyping individuals using microarray or by aligning whole genome sequencing reads to a reference genome. Both approaches require knowledge of a reference genome which hinders their application to organisms with no or incomplete reference genomes. This caveat can be removed by using alignment-free association mapping methods based on k-mers from sequencing reads. Here we present an improved implementation of an alignment free association mapping method. The new implementation is faster and includes additional features to make it more flexible than the original implementation. We have tested our implementation on an E. Coli ampicillin resistance dataset and observe improvement in execution time over the original implementation while maintaining accuracy in results. We also demonstrate that the method can be applied to find sex specific sequences.

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

FPGAs are promising target architectures for hardware acceleration of database query processing, as they combine the performance of hardware with the programmability of software. In particular, they are partially reconfigurable at runtime, which allows for the runtime adaptation to a variety of queries. However, reconfiguration costs some time, and a region of the FPGA is not available for computations during its reconfiguration. Techniques to avoid or at least hide the reconfiguration latencies can improve the overall performance. This paper presents optimizations based on query look-ahead, which follows the idea of exploiting knowledge about subsequent queries for scheduling the reconfigurations such that their overhead is minimized. We evaluate our optimizations with a calibrated model for various parameter settings. Improvements in execution time can be “calculated” even if only being able to look one query ahead.

Constraints Based Heuristic Approach for Task Offloading In Mobile Cloud Computing

Mobile devices are supporting a wide range of applications irrespective of their configuration. There is a need to make the mobile applications executable on mobile devices without concern of battery life. For optimizing mobile applications computational offloading is highly preferred. It helps to overcome the severity of scarce resources constraint mobile devices. In offloading, which part of the application to be offloaded, on which processor and what is available bandwidth rate are the main crucial issues. As subtasks of mobile applications are interdependent, efficient execution of application requires research of favorable wireless network conditions before to take the offloading decision. Broadly in mobile cloud computing the applications is either delay sensitive or delay tolerant. For delay sensitive applications completion time has the highest priority whereas for delay tolerant type of applications depending on the network conditions decision of offloading can be taken. Sometimes, computation time on a cloud server is less but it consumes high communication time which ultimately gives inefficient offloading results. To address this issue, we have proposed a heuristic based level wise task offloading (HTLO). It includes computation time, communication time and maximum energy available on the mobile device to take the decision of offloading. For simulation study, a mobile application is considered as a directed graph and all the tasks are executed on the basis of their levels. The overall results of the proposed heuristic approach are compared with state-of-the-art K-M LARAC algorithm and results show the improvement in execution time, communication time, mobile device energy consumption and total energy consumption.

Asymmetric Encryption Method Proposed for Arabic Letters Using Artificial Neural Networks

Asymmetric encryption algorithms suffer the problem of high execution time. This paper presents a proposed methodology to perform encryption and decryption of messages written in Arabic, using artificial intelligence represented by artificial neural networks based on the RSA algorithm. The method is based on dividing the message into partial messages. The arrays of weights and biases in the neural network layers are considered the encryption or decryption key, according to their function. All steps of the proposed method and how it works were shown and used in the MATLAB environment to design a system for encrypting and decrypting messages written in Arabic. The results proved the effectiveness of the proposed methodology and its superiority over the RSA algorithm in terms of execution time for both encryption and decryption. The encryption time in the proposed algorithm is close to the time of decryption, unlike what it was in the RSA algorithm, where the encryption time was relatively much greater than the decryption time. The proposed method was tested on four text files of 50KB, 100KB, 150KB, and 200KB, over two hundred iterations. The average improvement in execution time was 1,330ms and 4,497.5ms for encoding and decoding, respectively. KEYWORDS Data protection, decryption, execution time, security, RSA algorithm, performance

A Hybrid Encryption Solution to Improve Cloud Computing Security using Symmetric and Asymmetric Cryptography Algorithms

Ensuring the security of cloud computing is one of the most critical challenges facing the cloud services sector. Dealing with data in a cloud environment, which uses shared resources, and providing reliable and secure cloud services, requires a robust encryption solution with no or low negative impact on performance. Thus, this study proposes an effective cryptography solution to improve security in cloud computing with a very low impact on performance. A complex cryptography algorithm is not helpful in cloud computing as computing speed is essential in this environment. Therefore, this solution uses an improved Blowfish algorithm in combination with an elliptic-curve-based algorithm. Blowfish will encrypt the data, and the elliptic curve algorithm will encrypt its key, which will increase security and performance. Moreover, a digital signature technique is used to ensure data integrity. The solution is evaluated, and the results show improvements in throughput, execution time, and memory consumption parameters.

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers’ virtual network requests to the substrate network. This problem is known to be a Non-deterministic Polynomial-time hard (NP-hard) and heuristic solutions have been developed to solve this kind of problem. The current trend toward Software-Defined Networking (SDN) has allowed new possibilities in virtual network embedding. In this work, we propose a distributed virtual network embedding for SDNs called DVSDNE using multi-agent systems. This framework could be used to run a centralized VNE algorithm in a distributed manner to scale these algorithms with respect to network size. DVSDNE uses agents to spread the load across the substrate network. Our simulation results show the effectiveness of the proposed algorithm. Results show that DVSDNE improves execution time of embedding algorithms in large scale substrate networks, while embedding results such as acceptance ratio, revenue to cost ratio, average latency to controller, and maximum latency to controller remain comparable.

Mining method of massive data of mobile library under information asymmetry facing large-scare database

The current method generates a large number of candidate sets when mining data in mobile libraries under asymmetric information, and the mining time and efficiency are poor. To this end, a new method for mobile library massive data mining based on improved Apriori algorithm is proposed to collect, clean and reduce massive data. Calculate the reader's interest distance by analyzing the borrowed historical data, and use the Apriori algorithm to find the association rules in the frequent itemsets of the data. In order to make up for the shortcomings of the current method, while filtering out infrequent candidate sets, the corresponding transaction set is also collaboratively filtered, which can reduce the amount of calculation and time consumption. Experimental results show that the proposed method can mine more valuable rules. The improved execution time is only 10 s, the CPU utilization exceeds 90%, and the acceleration ratio exceeds 1.81 s, which can better meet the needs of decision makers.

Effective Job Execution in Hadoop Over Authorized Deduplicated Data

Existing Hadoop treats every job as an independent job and destroys metadata of preceding jobs. As every job is independent, again and again it has to read data from all Data Nodes. Moreover relationships between specific jobs are also not getting checked. Lack of Specific user identities creation and forming groups, managing user credentials are the weaknesses of HDFS. Due to which overall performance of Hadoop becomes very poor. So there is a need to improve the Hadoop performance by reusing metadata, better space management, better task execution by checking deduplication and securing data with access rights specification. In our proposed system, task deduplication technique is used. It checks the similarity between jobs by checking block ids. Job metadata and data locality details are stored on Name Node which results in better execution of job. Metadata of executed jobs is preserved. Thus by preserving job metadata re computations time can be saved. Experimental results show that there is an improvement in job execution time, reduced storage space. Thus, improves Hadoop performance.

LBMA and IMAR2: Weighted lottery based migration strategies for NUMA multiprocessing servers

SummaryMulticore NUMA systems present on‐board memory hierarchies and communication networks that influence performance when executing shared memory parallel codes. Characterizing this influence is complex, and understanding the effect of particular hardware configurations on different codes is of paramount importance. In this article, monitoring information extracted from hardware counters at runtime is used to characterize the behavior of each thread for an arbitrary number of multithreaded processes running in a multiprocessing environment. This characterization is given in terms of number of operations per second, operational intensity, and latency of memory accesses. We propose a runtime tool, executed in user space, that uses this information to guide two different thread migration strategies for improving execution efficiency by increasing locality and affinity without requiring any modification in the running codes. Different configurations of NAS Parallel OpenMP benchmarks running concurrently on multicore NUMA systems were used to validate the benefits of our proposal, in which up to four processes are running simultaneously. In more than the 95% of the executions of our tool, results outperform those of the operating system (OS) and produces up to 38% improvement in execution time over the OS for heterogeneous workloads, under different and realistic locality and affinity scenarios.

Hybrid scheduling for scientific workflows on hybrid clouds

Scientific workflows consist of many interdependent tasks dictated by their data dependencies. As these workflows are becoming resource-intensive in both data and computing, private clouds struggle to cope with their resource requirements. Private cloud’s limitations are claimed to be addressed by public clouds, however, the complete offloading of workflow execution to public clouds causes excessive data transfer. In this paper, we address the problem of scheduling scientific workflows on hybrid clouds aiming at cost reduction while improving execution time, with the consideration of public cloud costs, by minimizing data movements between private cloud and public cloud. To this end, we develop Hybrid Scheduling for Hybrid Clouds (HSHC) as a novel data-locality aware scheduling algorithm. HSHC adopts a hybrid approach consisting of static phase and dynamic phase. The former solves the problem of workflow scheduling, using an extended genetic algorithm, with static information of workflows and resources. The latter dynamically adapts the static schedule in response to changing execution conditions, such as locality of intermediate output data and performance degradation of tasks and resources. We evaluate HSHC with both random workflows and real-world scientific applications in execution time and cost. Experimental results compared with seven state-of-the-art algorithms demonstrate HSHC significantly reduces the cost by up to 40% and improves the execution time by up to 25%.

Green coffee beans feature extractor using image processing

This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the “pixel pair formation” that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively.

Rapid Detection of Local Communities in Graph Streams

We examine the problem of uncovering communities in complex networks whose elements and their respective associations manifest as streams of data. Community detection is applied in emerging computational environments and concerns critical applications in diverse areas. Despite the already expended related research efforts, the task of revealing the community structure of massive and rapidly-evolving networks remains very challenging. There is an emerging need for online approaches that ingest graph data as a stream. We propose a streaming-graph community-detection algorithm that expands seed-sets of nodes to communities. We consider an online setting and process a stream of edges while aiming to form communities using partial knowledge of the graph. We maintain very limited information regarding the nodes of the graph and the communities we seek, so that we effectively process large scale networks. In addition, we develop a technique that increases the accuracy of our algorithm considerably and propose a new clustering algorithm that allows for automatically deriving the size of the communities sought. Our experimental evaluation using ground-truth communities for a wide range of large real-word and synthetic networks shows that our approach achieves accuracy comparable or even better to the state-of-the-art non-streaming community detection algorithms. More importantly, we attain significant improvements in both execution time and memory requirements.

A causal model for short‐term time series analysis to predict incoming Medicare workload

AbstractWe have investigated methodologies for predicting radiologists' workload in a short time interval by adopting a machine learning technique. Predicting for shorter intervals requires lower execution time combined with higher accuracy. To deal with this issue, an ensemble model is proposed with the fixed‐batch‐training method. To excel in the execution time, a fixed‐batch‐training method is used. On the other hand, the ensemble of multiple machine learning algorithms provides higher accuracy. The experimental result shows that this predictive model can produce at least 10% higher accuracy in comparison with the other available widely used short‐term time series forecasting models. In the studied medical system, this gain in accuracy for the earlier prediction of workload can reduce the Medicare relative value unit cost by $1.1 million annually, which we have formulated and shown in this paper. The proposed batch‐trained ensemble of experts model has also provided at least a 6% improvement in execution time compared with the other studied models.