With the increasing demand for dynamic and customised resource provisioning for computational experiments in e-Science, solutions are required to mediate different participants’ varied demands for such resource provision. This paper presents a novel negotiation protocol based on a new collaboration model. The protocol allows e-Scientists, the manager of an e-Scientist’s collaboration, and resource

With the popularization of cloud computing, various cloud services have emerged, and hybrid clouds that can take advantage of combining public and private clouds are attracting attention. However, because of their variety, determining a combination of cloud services suited to the user’s current environment and requirements is expensive when deploying a hybrid cloud. Even if the required services are

The generation of a feature matrix is the first step in conducting machine learning analyses on complex data sets such as those containing DNA, RNA or protein sequences. These matrices contain information for each object which have to be identified using complex algorithms to interrogate the data. They are normally generated by combining the results of running such algorithms across various datasets

The tasks with continuous state and action spaces are difficult to be solved with high sample efficiency. Model learning and planning, as a well-known method to improve the sample efficiency, is achieved by learning a system dynamics model first and then using it for planning. However, the convergence of the algorithm will be slowed if the system dynamics model is not captured accurately, with the

In the field of computer science, cloud computing plays an inevitable role in offering on-demand services to end users. The capability, and hence the advantage, of a cloud environment is widened when multiple clouds interact. However, this interaction is usually done in a static environment and/or with the help of insecure brokers. To the best of our knowledge, the Cloud Inter-operation Toolkit (CIT)

The face detection has become one of the most important topics in machine learning and computer vision in the last few decades. Many papers have been published utilizing various methods for face detection. One of the most popular face detectors used in many practical applications with heterogeneous computing architecture is Viola-Jones method. Despite of being a real-time and robust face detector,

MapReduce framework is used for the distribution and parallelization of large-scale data processing. This framework breaks a job into several MapReduce tasks and assigns them to different nodes. A weak performance of a node in executing a task may result in a long execution of the job which is called Straggler Task. Also, detecting the nodes with the weak capability and assigning their tasks to other

Computed tomography (CT) plays key roles in radiotherapy treatment planning and PET attenuation correction (AC). Magnetic resonance (MR) imaging has better soft tissue contrast than CT and has no ionizing radiation but cannot directly provide information about photon interactions with tissue that is needed for radiation treatment planning and AC. Therefore, estimating synthetic CT (sCT) images from

This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data

The evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and

In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing

A contemporary approach for acquiring the computational gains of depth in recurrent neural networks (RNNs) is to hierarchically stack multiple recurrent layers. However, such performance gains come with the cost of challenging optimization of hierarchal RNNs (HRNNs) which are deep both hierarchically and temporally. The researchers have exclusively highlighted the significance of using shortcuts for

Forecasting on different levels of the management system of a cloud data center has received increased attention due to its significant impact on the cloud services quality. Making accurate forecasts, however, is challenging due to the non-stationary workload and intrinsic complexity of the management system of a cloud data center. It is possible to prevent excessive resource allocation and service

From the advent of Grid technology – as the new paradigm of distributed computing – to the current days of Cloud computing models, the continuous need of new tools and services to match the scientific community requirements has been addressed in Europe through dedicated software development projects for e–Infrastructure creation, operation and management. This work presents the most significant software

“Volunteer computing” is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC’s features, architecture, implementation, and

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices

Cloud computing using virtualization technology has emerged as a new paradigm of large-scale distributed computing. One of its fundamental challenges is to schedule a vast amount of heterogeneous tasks while maintaining load balancing amongst different heterogeneous virtual machines (VMs) to meet both cloud users and providers’ requirements, such as minimum makespan low monetary costs, and high resource

Distributed network architecture of heterogeneous computing faces with such problems as strict performance constraints of network control, unpredictable mapping relationship between computing data algorithms of different mobile terminals and inconsistency between computing algorithms and link control of data networks. In order to solve the above problems, we begin with software definition network architecture

Aiming at the high complexity of existing crowd abnormal detection models, the inability of traditional CNN to extract time-related features, and the lack of training samples, an improved spatial-temporal convolution neural network is proposed in this paper. The algorithm firstly uses the aggregation channel feature model to process the surveillance image, and selects the suspected object region with

Cryptomining malware (also referred to as cryptojacking) has changed the cyber threat landscape. Such malware exploits the victim’s CPU or GPU resources with the aim of generating cryptocurrency. In this paper, we study the potential of using deep learning techniques to detect cryptomining malware by utilizing both static and dynamic analysis approaches. To facilitate dynamic analysis, we establish

Accurate detection and capture of anomaly data in complex network data stream is an important part of ensuring network security. Traditional methods cannot adapt to the high dynamic changes of abnormal data characteristics in complex network. Thus, the detection accuracy is reduced. In this paper, a k-means parallel clustering algorithm is proposed. It is optimized by particle swarm optimization with

Firstly, this paper introduces the types of clustering algorithm, and introduces the classical K-means algorithm and canopy algorithm in detail. Then, combining the map reduce computing model and spark cloud computing framework, this paper introduces the parallel Canopy-K-means algorithm after using Canopy algorithm to optimize the initial value of K-means algorithm. However, because Canopy algorithm

Accurate detection of pupil position in successive frames of eye videos is finding applications in many areas including assistive systems and E-learning. Processing the big datasets of eye videos in such systems requires robust and fast eye-tracking algorithms that can predict the position of eye pupil in consecutive video frames. As a major technique, particle filters provide adequate speed but have

Abstract In order to improve the accuracy and efficiency of the clustering mining algorithm, this paper focuses on the clustering mining algorithm for large data. Firstly, the traditional clustering mining algorithm is improved to improve the accuracy, and then the improved clustering algorithm is parallelized to improve the efficiency. In order to improve the accuracy of clustering, an incremental

Abstract Image based face verification has attracted extension attention in the fields of pattern recognition and intelligent vision. With difference in age, cross-age face verification from facial images remains a challenging work because of a large number of facial variations caused by shape, skin color and wrinkles and so on. This study proposes a multi-task deep metric learning with boundary discriminative

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect

Abstract With our lives being increasingly digital, most users are concerned about their online privacy. Still, many users provide manifold data online and show no protection behaviors. Research has found different explanations for this privacy paradox: users perform a privacy calculus (weighing benefits and concerns about data sharing), make affective and inconsiderate decisions, or are overtaxed

Abstract In the current era of information, communication, and technology the data is being generated at an exponential rate. This provides machine learning and data mining algorithms an opportunity to learn from huge data repositories. However, at the same time, the big data poses many challenges. Data uncertainty being the key concern of the modern data mining systems. This work addresses the problem

Abstract In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution

Abstract Thanks to evolving web technologies, computational platforms, automation tools and open-source software business model, today, it is possible to develop powerful automatic and virtualized web services for complex physical problems in engineering and design. In particular, in this work, we are presenting a new web based HPC-cloud platform for automatic virtual towing tank analysis. It is well

Abstract Cloud computing and Internet of Things (IoT) are two of the most important technologies that have significantly changed human’s life. However, with the growing prevalence of Cloud-IoT paradigm, the load imbalance and higher SLA lead to more resource wastage and energy consumption. Although there are many researches that study Cloud-IoT from the perspective of offloading side, few of them have

Abstract Mobile Cloud Computing (MCC) aims at leveraging remote resources to boost application performance on mobile devices while conserving resources such as battery, memory, and storage. Offloading computations and outsourcing tasks are, however, associated with numerous challenges known from distributed systems. Typical mobile applications have a monolithic design and are not laid out for a distributed

Abstract Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device’s capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation