Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale

The security of Bitcoin heavily relies on the distributed consensus algorithm. There are many consensus algorithms used in different cryptocurrencies to provide security for transactions in the blockchain. Along with the consensus algorithm, the blockchain structure is also an important aspect that affects the performance of the cryptocurrency. However, most of the state-of-the-art schemes have some

Non-orthogonal multiple access (NOMA) along with cognitive radio (CR) have been recently configured as potential solutions to fulfill the extraordinary demands of the fifth generation (5G) and beyond (B5G) networks and support the Internet of Thing (IoT) applications. Multiple users can be served within the same orthogonal domains in NOMA via power-domain multiplexing, whilst CR allows secondary users

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage

Container-based virtualization is a new technology used by cloud providers to provide cloud services to end-user. This technology has various advantages (e.g. lightweight, quickly deployable, and efficient for resource utilization) for executing an application. It reduces the operating cost, carbon emission, and allocates the resources dynamically. Different cloud applications have different requirements

Time-varying resource allocation allows users to define their own unique resource requirement plans during different time periods. This mode of allocation can increase the flexibility of resource usage and reduce resource usage costs for users. Moreover, combining this approach with an auction mechanism can enable resource providers to obtain greater social welfare and benefits; therefore, such resource

Vehicular cloud network (VCN) enables the vehicles to act as servers in the cloud environment, considering the abundance of computing and storage resources. Service provisioning may be challenging due to the characteristic of VCN such as service pricing, resource variability, and resource mobility. In this regard, the most important challenges of service provisioning are quality of service (QoS), service

The Vehicle Routing Problem (VRP) is an NP-hard scheduling problem for goods transportation with vehicle capacity and transportation cost constraints. This paper presents GPU-based parallel strategies for the Local Search Heuristic (LSH) algorithm to solve the large-scale Capacitated Vehicle Routing Problem (CVRP) instances. This work employs a combination of five improvement heuristic approaches to

Deep neural networks (DNNs) have been employed to different devices as a popular machine learning algorithm (ML) owing to deploy the Internet of Things (IoT), data mining in cloud computing, and web search engines, which MLs had an impressive effect on IoT’s edge level nodes. Deploying DNN-based applications leads to memory access problems, including communication delay, energy efficiency, and bandwidth

A recent market prediction is that 5G Fixed Wireless Access (FWA) will more than double over the next five years and trials at the same period in London suggest promising results. However, the shift to 5G FWA has raised a new set of research challenges in relation to speed of deployment and re-deployment, coverage, power consumption, end user mobility and last mile connectivity, to name just a few

Most of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these

Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent

With the explosive growth of the Internet of Things (IoT), fog computing emerged as a new paradigm, in an attempt to minimize network latency. Fog computing extends the cloud to the network edge, closer to where the IoT data are generated. Typically, fog resources are of limited capacity. On the other hand, IoT applications are becoming more and more complex and computationally demanding, requiring

When each task of the longest path in a task-dependent scientific workflow must meet a deadline, the path is called critical. Tasks in a critical path have priority over tasks in non-critical paths. Considering this fact that less methods have already dealt with the critical path problem for workflow scheduling in cloud, this study aims to present a critical-path based method to consider the problem

Cloud computing is being widely adopted in the industry due to providing more computation power and improve resource utilization. In cloud computing systems, many users execute various types of applications that produce a large amount of data. To handle a large amount of data, cloud computing systems provide various, high-performance, and large-scale clustered storage devices. With the large capacity

Nowadays, outsourcing sensitive data to the cloud becomes popular. Outsourcing sensitive data to the cloud raises confidentiality concerns due to the loss of data control by the data owner. Data must be encrypted before outsourcing to ensure data confidentiality. However, when data is encrypted, a trade-off must be considered between efficiency and confidentiality. Homomorphic encryption allows computing

HPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is

The emergence and massive growth of cloud computing increased the demand for task scheduling strategies to utilize the full potential of virtualization technology. Efficient task scheduling necessitates efficiency, reduced makespan and execution time, and improvement ratio. Additionally, secure scheduling is a pivotal element in highly distributed environments. Task scheduling is an NP-complete problem

In large organizations, we achieve security by limiting unnecessary access to shared resources based on the users' roles. It reduces administrative work and enhances security. Due to the security requirements of role-centric authentication and access management, it is challenging to create an efficient centralized system for distributed applications. Centralized authentication and access control systems

In file systems, a single write system call can make multiple modifications to data and metadata, but such changes are not flushed in an atomic way. To retain the consistency of file systems, conventional approaches guarantee crash consistency in exchange for sacrificing system performance. To mitigate the performance penalty, non-volatile memory (NVM) technologies are believed to be a good candidate

A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer’s dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources.

As the environments that human live are complex and uncontrolled, the object manipulation with humanoid robots is regarded as one of the most challenging tasks. Learning a manipulation skill from human Demonstration (LfD) is one of the popular methods in the artificial intelligence and robotics community. This paper introduces a deep learning based teleoperation system for humanoid robots that imitate

Human action recognition (HAR) is one of the most challenging tasks in the field of computer vision due to complex backgrounds and ambiguity action, etc. To tackle these issues, we propose a novel action recognition framework called Semantic Feature and High-order Physical Feature Fusion (SF-HPFF). Concretely, we first calculate attention pooling module with a low-rank approximation to remove the information

The smart grid has attracted great attention as a new energy network. The power supplier collects the total consumption data through the smart meter. The data aggregation is a common operation in smart grid. However, the majority of existing data aggregation schemes are based on homomorphic encryption, suffering from heavy computation overhead. Recently, Gope et al. put forward a masking-based data

Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic

There has been a debate surrounding cloud federation in recent studies partly because of the cloud providers’ profit increases and also because of resource utilization improvement. However, the main part of these studies suffers from the lack of considering integrity issues. In this paper, we provide a formal definition of cloud federation integrity in a proposing architecture and introduce a resource

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers’ and service providers’ perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider

The Industrial Internet of Things uses intelligent sensors to collect the physical properties of objects placed on a large area. It provides innovative service and network functioning, such as time-bounded data delivery and efficient sensor sleep cycle management. Intelligent scheduling is crucial to assisting the situation. The periodic scheduling of radio transceivers of sensor nodes into sleep or

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container

SpMV (Sparse matrix-vector multiplication) is an essential component in scientific computing and has attracted the attention of researchers in related fields at home and abroad. With the continuous expansion of matrix data, the efficient parallel SpMV algorithm has become a research hotspot for research experts in related fields. The sparse matrix compression format as a critical point to improve computing

In recent years, virtualization is one of the key technologies of next-generation data centers. However, the problem of virtualization technology is that each instance needs to run a client operating system and a lot of applications. Therefore, it might generate a heavy load and affect the system efficiency and performance. In this work, the performance evaluation of three environments (bare-metal

This paper proposes an energy-efficient service scheduling mechanism in federated edge cloud (FEC) called ESFEC, which consists of a placement algorithm and three types of reconfiguration algorithms. Unlike traditional approaches, ESFEC places delay-sensitive services on the edge servers in nearby edge domains instead of clouds. In addition, ESFEC schedules services with actual traffic requirements

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users’ tasks on the available

Job scheduling in MapReduce plays a vital role in Hadoop performance. In recent years, many researchers have presented job scheduler algorithms to improve Hadoop performance. Designing a job scheduler that minimizes job execution time with maximum resource utilization is not a straightforward task. The primary purpose of this paper is to investigate agents affecting job scheduler efficiency and present

One of the goals of cloud service providers is to satisfy service-level agreements without significant over-provisioning in data center clusters. Efforts to meet these requirements have been mainly based on resource over-provisioning rather than identifying performance bottlenecks. While increasing parallelism tends to reduce the average and tail latency, the joint impact of concurrent job scheduling

More and more cloud data centers provide numerous cloud computing services. However, how to meet customer needs, improve efficiency and reduce costs are important issues that cloud service providers must deal with. For customers, it is very important to consider the quality of service requirements provided by the data center providing public cloud services. Besides, data center operators should consider

The rapid growth in virtualization solutions has driven the widespread adoption of cloud computing paradigms among various industries and applications. This has led to a growing need for XaaS solutions and equipment to enable teleworking. To meet this need, cloud operators and datacenters have to overtake several challenges related to continuity, the quality of services provided, data security, and

The cloud computing environments rely heavily on virtualization that enables the physical hardware resources to be shared among cloud users by creating virtual machines (VMs). With an overloaded physical machine, the resource requests by virtual machines may not be fulfilled, which results in Service Level Agreement (SLA) violations. Moreover, the high performance servers in cloud data centers consume

Nowadays, cloud computing environments have become a natural choice to host and process a huge volume of data. The combination of cloud computing and big data frameworks is an effective way to run data-intensive applications and tasks. Also, an optimal arrangement of data partitions can improve the tasks executions, which is not the case in most big data frameworks. For example, the default distribution

Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means

The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More

According to the previous studies in the field of economics-oriented cloud resource allocation using game theory, finding an equilibrium point for price is difficult in many cases. This is due to the stochastic situations of the cloud market. So, to tackle this problem we should find a space to describe behavior of the players’ strategies that be independent from the equilibrium point. Therefore, a

In order to support a variety of Internet of Things (IoT) and smart city applications, it is necessary to provide computing and networking resources at cloud, fog and edge levels. Fortunately, evolution of Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies are greatly supporting operators to deploy their data centers at reduced expenditures by integrating cloud

Software defined networks and the Internet of Things (IoT) are two major and emerging developments in networking that have much in common and their survival depends on each other. Software Defined Networking (SDN) is one of the 5G enabling innovations that can help design complex, manageable, cost-effective and adaptable networks. On the other hand, Edge Computing (EC) will do automatic analytical

In most storage systems, the storage nodes store data on a local filesystem. Thus, unless they have a dedicated caching layer, they benefit from the usual filesystem cache in the host’s free memory. However, in erasure-coded storage systems, caching is effective only if all the systematic fragments corresponding to an object are in the cache. In this work, we propose a new caching policy adapting traditional

The use of cloud-edge technology creates significant potential for cost reduction, efficiency and resource management. These features have encouraged users and organizations to use intelligence federated cloud-edge paradigm in Internet of Things (IoT). Human Resource Management (HRM) is one of the important challenges in federated cloud-edge computing. Since hardware and software resources in the edge

The many-to-many assignment problem (M- MAP), and the CPU/FPGA scheduling problem are two correlated issues in the field of combinatorial optimization. The framework for Kernighan Lin-driven logarithmic barrier approach (KD-LBA) is made to solve the many-to-many assignment problem. KD-LBA is a deterministic technique, which initiates to achieve the globally optimal solutions for MMAP using logarithmic

A correction to this paper has been published: https://doi.org/10.1007/s10586-021-03335-y

Scholarly big data refer to the rapidly growing scholarly source of information, including a large number of authors, papers, and massive scale scholarly networks. Extracting the profile attributes for Web users is an important step in Web user analysis. For Web scholarly users, profile attributes extraction should integrate multi-source and heterogeneous information resources. However, the traditional

Cloud computing is a new paradigm of computing. This paradigm delivers services over the internet and eliminates requirements for local data storage. Instead of purchasing hardware and software, cloud computing enables users to use storage or applications as a service. Scheduling is the process of allocating the available resources in cloud environment. Scientific workflows consist of a large number

The Compact Muon Solenoid (CMS) experiment heavily relies on the CMSWEB cluster to host critical services for its operational needs. The cluster is deployed on virtual machines (VMs) from the CERN OpenStack cloud and is manually maintained by operators and developers. The release cycle is composed of several steps, from building RPMs to their deployment, validation, and integration tests. To enhance

Unlike other word embedding models that learn word vectors for a collection of words sequentially, this paper proposes a non-sequential refinement approach to improve the vectors of particular words non-sequentially using a string matching algorithm to speed up the process. The key idea is to change the order of training in the embedding learning model and force it to learn the vector of a particular

The Internet, and many of the related things, hence the term Internet of Things, IoT, continue to expand and take more roles in human lives. Indeed, this enables us to be connected with our devices and the environment. The Internet also enabled us to be continuously informed about the status of our cars, homes, health, family, friends, etc. However, such exposure or publicity for all those things around

Blockchain is among the most promising new technologies due to its unique features, encompassing security, privacy, data integrity, and immutability. Blockchain applications include cryptocurrencies such as Bitcoin. Recently, many other applications have begun to deploy blockchain in their systems. These applications include internet of things (IoT) environments. Although deploying blockchain in IoT

For the last decade, social networking websites have boosted interaction among people through the use of digital communication such as chats, comments, discussion boards and exchange of documentation. This lead to mutual learning and sharing of all kind of information. This phenomenon has attracted many researchers and techniques aiming at discovering and prediction links between people have been developed

Due to the rapid development of diversified technology, people may use multiple electronic devices, such as personal computers, tablets, and smartphones, to connect to the Internet in their daily lives. Switching between devices enables a user to use e-commerce on various platforms. The complexity of consumer behavior is directly proportional to the number of involved devices. Additionally, since the

The notion of Remote Data Auditing (RDA) has been put forward to achieve the integrity verification for cloud data. However, most of the existing RDA techniques suffer from heavy computational overhead or security attacks. To address this challenge, we introduce an online/offline remote data auditing (OORDA) framework that defines the data auditing process as online and offline phases. Then a concrete

Green computing is an important factor to ensure the eco-friendly use of computers and their resources. Electric power used in a computer converts into heat and thus, the system takes fewer watts ensuring less cooling. This lower energy consumption allows to be less costly to run as well as reduces the environmental impact of powering the computer. One of the most challenging problems for the modern

Workflow scheduling in cloud computing environments is nowadays a hot topic as scientific workflows application are gradually taking advantage of commercial cloud assets. Common users’ quality of service (QoS) requirements are the respect of defined budget and deadline when executing their workflow job. Since execution cost minimization and completion time minimization are contradictory objectives

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes