The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed

For the current research on computing offloading, most of them only considers the multi-user task offloading decision problem or only considers the wireless resource and computing resource allocation. They have failed to comprehensively consider the impact of offloading decision and resource allocation on computing offloading performance, and it is difficult to achieve efficient computing offloading

In a fog-cloud environment, the tasks submitted by end-users are first sent to intermediate nodes called fog nodes. If the computing resources in fog nodes are insufficient, the nodes will offload tasks to the remote cloud. Naturally, intermediate nodes are reluctant to offload tasks to upstream entities, which in turn results in a degradation in network performance. To motivate such reluctant nodes

Cloud Computing has been successful in providing substantial amounts of resources to deploy scalable and highly available applications. However, there is a growing necessity of lower latency services and cheap bandwidth access to accommodate the expansion of IoT and other applications that reside at the internet’s edge. The development of community networks and volunteer computing, together with the

Aiming at the problems of increased latency and energy consumption and decreased service quality caused by current vehicle networks, this paper proposes a computing offloading resource allocation strategy based on deep reinforcement learning in Internet of Vehicles. Firstly, the system architecture for Internet of Vehicles is designed, calculation model and communication model of computing offloading

Fog computing provides a distributed computing paradigm that executes interactive and distributed applications, such as the Internet of Things (IoT) applications. Large-scale scientific applications, often in the form of workflow ensembles, have a distributed and interactive nature that demands a dispersed execution environment like fog computing. However, handling a large-scale application in heterogeneous

Fog Computing has emerged as a virtual platform extending Cloud services down to the network edge especially (and not exclusively) to host IoT applications. Data replication strategies have been designed to investigate the best storage location of data copies in geo-distributed storage systems in order to reduce its access time for different consumer services spread over the infrastructure. Unfortunately

Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are

Novel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether

This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise

IoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this

Smart cities use Information and Communication Technologies (ICT) to enrich existing public services and to improve citizens’ quality of life. In this scenario, Mobile CrowdSensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. MCS allow people roaming around with their smart devices to collectively sense, gather, and share data, thus leveraging the

A Correction to this paper has been published: https://doi.org/10.1007/s10723-021-09562-2

A pilot grid computing test infrastructure has been created between the Central Laboratory of Applied Physics (CLAP), part of Bulgarian Academy of Sciences, and RISC Software GmbH, a subsidiary research company of Johannes Kepler University in Austria. The infrastructure is used by specialists in applied physics for training in the application of grid technologies. A specific application has been developed

Time-varying resource allocation in which the resource requirements of a job can vary over time is a new challenge in cloud computing. Time-varying resource allocation can be combined with an auction mechanism to improve the social welfare of resource providers. However, existing research results are based on fixed resource requirements and consequently cannot be used in time-varying resource allocation

The essential component of the Big Data system is the processing frameworks and engines responsible for crunching the data. To cope with the growing computing demands of real-time Big Data applications, researchers have proposed several computing frameworks. The core of the computing frameworks i.e., the scheduling mechanisms for real-time stream processing need to accommodate several important aspects

Cloud computing is largely adopted by the current computing industry. Not only users can benefit from cloud scalability, but also businesses are more and more attracted by its flexibility. In addition, the number of offered cloud services (e.g., SaaS, BPaaS, mobile services, etc.) is continuously growing. This raises a question about how to effectively arrange and place them in the cloud, in order

Aiming at the problems of insufficient information and poor recognition rate in single-mode emotion recognition, a multi-mode emotion recognition method based on deep belief network is proposed. Firstly, speech and expression signals are preprocessed and feature extracted to obtain high-level features of single-mode signals. Then, the high-level speech features and expression features are fused by

Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred

Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades

Cloud computing enables the execution of various applications submitted by the users in the virtualized Cloud environment. However, the Cloud infrastructure consumes a significant amount of electrical energy to provide services to its users that have a detrimental effect on the environment. Many of these applications (tasks), like those belonging to the healthcare system, scientific research, the Internet

Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the existing studies ignore context information of the application, requests, sensors, resources, and network. However, in practice, context information has a significant

With the development of 5G and the explosive growth of massive Internet of Health Things devices, existing computing resource allocation strategies have many problems such as long delay and poor security performance. Therefore, this paper proposes an optimization strategy for computing resource allocation of massive IoHT devices considering privacy protection in cloud edge computing environment. Firstly

Software Defined Network (SDN) is an architecture and its main idea is data and control traffic separating which has amended the network flexibility, programmability, and manageability. The SDN-based network contains a physical or conceptual centeralized controller with a global view of the network. The centeralized controller is responsible for decision-making in the network. One of the most effective

In view of the serious problems of increasing delay energy consumption and decreasing service quality caused by complex network state and massive computing data in Internet of vehicles (IOT), a high reliable computing offloading strategy based on edge computing is proposed in this paper. Firstly, the architecture of the Internet of vehicles is designed. The real-time business of terminal vehicles is

Mobile edge computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of mobile devices (MDs). However, MECs contain limited resources, and they should be appropriately managed to prevent resource wastage. Workflow scheduling is a process that tries to map tasks to the most proper set of resources based on some objectives. This paper presents

Low-cost and high-performance execution of nowadays computing-intensive applications will not be possible without large-scale heterogeneous computing platforms. The huge computing power of such platforms raises the problem of the electrical energy consumed by such platforms. One of the key issues to achieve high-performance in such platforms is task-scheduling. Among the heuristics-based compile-time

To handle user requests coming from all over the world, online services need to schedule resources to fulfill the corresponding stochastic demands for various resources in geo-distributed data centers. In order to make full use of the advantages of cloud resources and tap the potential of private infrastructure, the optimal schedule plan should consider the heterogeneity of different kinds of data

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing

Internet of Things (IoT) has been widely applied in various domains, e.g. environmental monitoring, intelligent transport system, video surveillance, etc. In most of the IoT applications, the IoT data is generated from a number of data sources, not just only one source. In addition, IoT data has various types with different processing requirements. The high-priority IoT data should have better storage

Microservice-based architectures are gaining popularity for their benefits in software development. Distributed tracing can be used to help operators maintain observability in this highly distributed context, and find problems such as latency, and analyse their context and root cause. However, exploring and working with distributed tracing data is sometimes difficult due to its complexity and application

Parallel training accelerates the Deep Neural Networks (DNN) training by parallel GPUs. While the in-memory data transmission becomes the cross-node network transmission due to distribution of GPUs on different nodes, which drags the training time. Most researches address it by reducing the data size on network links. However, the factor of network distance is ignored. In this paper, we construct a

A fault management system contains managers that detect faults as well as initiate recovery actions. Such management systems often come in an architecture that is not only a distributed one but also decoupled from the applications. Although an arrangement like this promotes scalability, it unfortunately makes the recovery of applications dependent on the fault management system itself. This work introduces

Cloud computing has been one of significant domains of processing service in social networks like the internet and local networks in recent years. One of the main problems in cloud computing is placing a virtual server onto physical servers. This problem will have a remarkable effect on energy consumption, because if a suitable placement is not chosen for it, a great amount of energy will be used to

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking

Virtual clusters are widely used computing platforms than can be deployed in multiple cloud platforms. The ability to dynamically grow and shrink the number of nodes has paved the way for customised elastic computing both for High Performance Computing and High Throughput Computing workloads. However, elasticity is typically restricted to a single cloud site, thus hindering the ability to provision

We suggest a novel approach to designing totally decentralized data management systems in distributed environments with administratively unrelated or loosely related user groups and in conditions of partial or complete lack of trust between them. This approach is based on the integration of blockchain technology, smart contracts and provenance metadata driven data management. Provenance metadata (PMD)

The growing number of latency-critical applications are posing novel challenges for network operators, cloud/hosting companies, and application providers. Edge Computing is the strongest candidate for providing low-latency responses, but it is not yet clear what edge infrastructures will be like. This paper introduces a new platform for enabling an edge infrastructure according to a disaggregated distributed

Cloud computing enables the evolution of modern software application design. Applications based on microservice architecture are an example. Meanwhile, multiclouds are widely accepted by enterprise as an infrastructure strategy; however, challenges remain. The autonomous and distributable nature of modern applications, as well as the complexity of multicloud infrastructure, often make universal application

Bitcoin has always been used to store arbitrary data, particularly since Bitcoin Core developers added a dedicated method for data storage in 2014: the OP Return operator. This paper provides an in-depth analysis of all OP Return transactions published on Bitcoin between September 14, 2018, and December 31, 2019. The 32.4 million OP Return transactions (22% of all Bitcoin transactions) published during

Air cargo involves large-scale data and multiple stakeholders, i.e., airports, airlines, agents, and clients. How to enable stakeholders to share data in a secure way is essential, since it improves efficiency for various processes among stakeholders. This paper proposes AirCargoChain, a blockchain-based data sharing approach for air cargo, which has the following advantages: (a) secure: we propose

The management of Service-Level Agreements (SLAs) in Edge-to-Cloud computing is a complex task due to the great heterogeneity of computing infrastructures and networks and their varying runtime conditions, which influences the resulting Quality of Service (QoS). SLA-management should be supported by formal assurances, ranking and verification of various microservice deployment options. This work introduces

Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing

In order to meet the rapidly changing requirements of the Cloud-native dynamic execution environment, without human support and without the need to continually improve one’s skills, autonomic features need to be added. Embracing automation at every layer of performance management enables us to reduce costs while improving outcomes. The main contribution of this paper is the definition of autonomic

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple

Recent advances in artificial intelligence, big data, mobile edge computing and embedded systems have successfully driven the emergence and adoption of smart vehicles and vehicle edge computing which will improve road safety, traffic congestions, and vehicle exhaust emissions. The high-mobility, ad-hoc network topology, and diverse vehicle-to-everything (V2X) have brought substantial challenges in

In scheduling workflows in grid environment, concerns such as minimizing the makespan and cost, meeting the time and budget constraints and the possibility of resource failures and so on have motivated researchers to propose numerous scheduling algorithms. Several heuristics and meta-heuristic algorithms have been proposed to address these issues, each of which often only considers one or a few of

Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive

As the Industrial Internet of Things (IIoT) is one of the emerging trends and paradigm shifts to revolutionize the traditional industries with the fourth wave of evolution or transform it into Industry 4.0. This all is merely possible with the sensor-enabled technologies, e.g., wireless sensor networks (WSNs) in various landscapes, where security provisioning is one of the significant challenges for

While many cluster and grid computing frameworks are available, the task of building secure distributed systems or implementing distributed algorithms continue to be a challenging task due to the inherent distributed nature of such systems with multiple failure modes and security issues. In this paper, we present the design and development of remote method delegation (RMD), which is a secure lightweight

We propose a benchmarking test suite to evaluate performance of cloud serverless platforms and an open source software tool to automate the test process. Additionally, we used this setup to compare the commercial offers of Amazon, Google, Microsoft, and IBM. The work builds on ideas and experiments reported in the literature that, nevertheless, did not offer a “standard” set of coherent and comprehensive

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework

With the emergence of internet-based devices, the traditional industrial control system (ICS) networks have evolved to co-exist with the conventional IT and internet enabled IoT networks, hence facing various security challenges. The IT industry around the world has widely adopted the common vulnerability scoring system (CVSS) as an industry standard to numerically evaluate the vulnerabilities in software

NOA-AID (Network Overlays for Adaptive information Aggregation, Indexing and Discovery on the fog) is an approach for decentralized indexing, aggregation and discovery of data belonging to streams. It is organized on two network layers. The upper layer is in charge of delivering an information discovery approach by providing a distributed index structure. The lower layer is devoted to resource aggregation

Typical cloud applications require high-level policy driven orchestration to achieve efficient resource utilisation and robust security to support different types of users and user scenarios. However, the efficient and secure utilisation of cloud resources to run applications is not trivial. Although there have been several efforts to support the coordinated deployment, and to a smaller extent the

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

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,