In high-performance computing, the general matrix-matrix multiplication (xGEMM) routine is the core of the Level 3 BLAS kernel for effective matrix-matrix multiplication operations. The performance of parallel xGEMM (PxGEMM) is significantly affected by two main factors: the flop rate that can be achieved by calculating the operations and the communication costs for broadcasting submatrices to others

Software defined network (SDN) has shown significant advantages in numerous real-life aspects with separating the control plane from the data plane that provides programmable management for networks. However, with the increase in the network size, a single controller of SDN imposes considerable limitations on various features. Therefore, in networks with immense scalability, multiple controllers are

Unmanned aerial vehicle (UAV) path planning is a complex optimization problem, which aims to achieve an optimal or nearly optimal flight path despite various threats and constraints. In this paper, an improved version of Gray Wolf Optimization (GWO) is proposed to solve the UAV 3D path planning problem which considers the dynamics of the UAV. In improved GWO, a variable weighting called "align coefficient"

Technological evolution of mobile devices, such as smart phones, laptops, wearable and other handheld devices have come up with the emergence of different user applications in learning, social networking, entertainment, and community computing domains. Many of such applications are fully or partially offloaded to the nearby server capable with high computing and storage resources. The delivery of task

Network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work

The exponential growth of services in the internet with rapid development of technologies results produces huge growth in the traffic, which maximizes the possibility of increase in attacks by the attackers in the network. Several researchers have developed various techniques to defend these attacks and most of them are machine learning based approaches. The machine learning based techniques relay

Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for each application such as scalability, security, privacy, and reliability. During the deployment of IoT-based Cloud applications, the demand for Cloud tenants is dynamic that makes challenging to maintain scalability of the system. Developing

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given

Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable computational resources for computation and data-intensive large scale workflow deployment. For business as well as scientific applications, optimal scheduling of workflow is emerged as a major concern. Optimization of scheduling

With the penetration of Network Function Virtualization (NFV), network functions, traditionally deployed as proprietary physical equipment like firewalls, Network Address Translations (NATs), are gradually being implemented as software and deployed on standardized hardware. One of the crucial challenges in this paradigm is how to place the software implemented network functions to minimize the number

Quaternion moments are widely used in several applications, such as image classification, object recognition, and multimedia security. The computation of these moments requires a vast computational time, especially for big-size images. Several attempts to accelerate quaternion moments are not enough to process big-size color images with the desired speedup. In this work, we proposed a new parallel

A correction to this paper has been published: https://doi.org/10.1007/s10586-021-03273-9

The Internet of Things (IoT) has infiltrated extensively into our lifestyles. Nevertheless, IoT privacy remains a significant obstacle, primarily because of the large size and distributed existence of IoT networks. Also, numerous safety, authentication, and maintenance problems of IoT systems have been overcome by the decentralized existence of blockchain. To obviate these privacy difficulties, the

Social data analysis has become a vital tool for businesses and organisations for mining data from social media and analysing for diverse purposes such as customer opinion mining, pattern recognition and predictive analytics. However, the high level of volatility for social data means application updates due to analytical results requires spontaneous integration. In addition, while cloud computing

Data stream mining is an important research topic that has received increasing attention due to its use in a wide range of applications, such as sensor networks, banking, and telecommunication. The phenomenon of data streams evolving over time is known as concept drift. In addition, the presence of multiple classes aggravates the problem of a loss in performance during the process of drift detection

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that allows the programming and debugging of such

Over the last decades, web services are used for performing specific tasks demanded by users. The most important task of service’s classification system is to match an anonymous input service with the stored pre-classified web services. The most challenging issue is that web services are currently organized and classified according to syntax while the context of the requested service is ignored. Due

Biclustering is a data mining technique that allows us to find groups of rows and columns that are highly correlated in a 2D dataset. Although there exist several software applications to perform biclustering, most of them suffer from a high computational complexity which prevents their use in large datasets. In this work we present ScalaParBiBit, a parallel tool to find biclusters on binary data,

Energy consumption in data centers grows rapidly in recent years. As a widely-applied energy-efficient method, workload consolidation also has its own limitations that may bring some negative effects, such as performance degradation, QoS violation, localized hotspots and so on, which is especially true when optimal objectives are inherently conflict. In this paper, we present a power and thermal-aware

Data retrieval in Named Data Networking (NDN) is based on content names irrespective of their hosting location. The NDN architecture introduces original naming, forwarding and caching techniques to improve data delivery efficiency. These distinctive techniques make the TCP congestion control scheme not suitable for the NDN architecture. In particular, the in-network caching and the multi-path forwarding

Economic denial of sustainability (EDoS) attack is a new type of distributed denial of service (DDoS) attack which targets the economic resources of cloud adopters by exploiting the auto-scaling features of the cloud. EDoS attack has become a significant threat to cloud adopters as it can lead to bankruptcy or withdrawal from cloud services and this in turn entails unavailability of the cloud services

The wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in smart city applications for data sensing, collecting, and transmitting. Advanced metering infrastructure is an automatic system for the reading of electricity consumption by individual users. The reading of data from meters

In order to provide uninterrupted services and fulfill the requirements of customers, Horizontal Cloud Federation Formation (HCFF) has been proposed as a new model enabling cloud providers to cooperate and enlarge their virtual machine infrastructure capacity. In this paper, we have established a synthesis on the main related works in the literature by classifying them into two classes: reactive protocols

Supply chains play today a crucial role in the success of a company’s logistics. In the last years, multiple investigations focus on incorporating new technologies to the supply chains, being Internet of Things (IoT) and blockchain two of the most recent and popular technologies applied. However, their usage has currently considerable challenges, such as transactions performance, scalability, and near

Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the

Traditional wireless technologies have evolutionary converged to Internet of Thing (IoT) for devices and service interactions. In the past decade, the academia, industry 4.0 and end-user interest has also grown drastically in IoT applications and their services. However, this increase in IoT services demand has witnessed a new challenge of seamless interaction among heterogeneous devices that are varied

Integrity audit technology is proposed to protect data in remote cloud servers from being tampered with. However, the challenge is that the computational complexity is too large for users with resource-constrained devices during integrity verification. Also, many prior works lack support for dynamic operations. In this paper, a trusted third party is employed to complete the public auditing to reduce

Ever since the internet became part of the everyday lives of humans providing network security has been considered of utmost importance. Over the years lot of time and energy has been devoted by people in the research community and industry to provide better, improved and secure mechanisms to ensure secure communications on the internet. Amongst the many fields of study, the most prominent and ever

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration

The application of the Internet of Things concept in domains such as industrial control, building automation, human health, and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices, their heterogeneity and the typical limitations

An essential goal of the Internet of Vehicles (IoV) is to allow vehicles’ peer connections and contact a service provider through a secure communication channel. However, privacy and authentication are considered the main objectives of secure communication. In this paper, we propose an efficient Online/Offline signcryption of heterogeneous systems based on blockchain to secure data sharing between

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the

Cloud service systems typically rank the tasks from diverse users according to their privileges. Besides providing high performance service to membership-based users, extensive cloud service systems tend to offer service trials to normal users. In this paper, aiming to improve the utility and flexibility of cloud service systems, we propose an improved cloud architecture. In this architecture, membership-based

Deep Learning (DL) has achieved remarkable progress over the last decade on various tasks such as image recognition, speech recognition, and natural language processing. In general, three main crucial aspects fueled this progress: the increasing availability of large amount of digitized data, the increasing availability of affordable parallel and powerful computing resources (e.g., GPU) and the growing

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-021-03251-1.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-021-03250-2.

As cloud data centers are dramatically growing, various applications are moved to cloud data centers owing to cost benefits for maintenance and hardware resources. However, latency-critical workloads among them suffer from some problems to fully achieve the cost-effectiveness. The latency-critical workloads should show latencies in a stable manner, to be predicted, for strictly meeting QoSs. However

In the new era of big data, numerous information and technology systems can store huge amounts of streaming data in real time, for example, in server-access logs on web application servers. The importance of anomaly detection in voluminous quantities of streaming data from such systems is rapidly increasing. One of the biggest challenges in the detection task is to carry out real-time contextual anomaly

In recent years, how to design efficient auditing protocol to verify the integrity of users’ data, which is stored in cloud services provider (CSP), becomes a research focus. Homomorphic message authentication code (MAC) and homomorphic signature are two popular techniques to respectively design private and public auditing protocols. On the one hand, it is not suitable for the homomorphic-MAC-based

Trust management systems give way to trustworthy interactions in cloud computing. However, malicious cloud users can intentionally provide unfair ratings to benefit or reduce a cloud provider’s reputation. This paper proposes a novel detection strategy to supervise cloud users’ feedback and detect unfair rating attacks for cloud environments based on the Fast Entropy metric and named Feedback Fast

Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and Platform-as-a-Service (PaaS). The research community is able to get access to resources on-demand within a short period of time. But, as the demand for cloud resources is dynamic in nature, this affects resource availability during

Cognitive radio (CR) is a new intelligent wireless technology that aims at improving spectrum utilization by allowing opportunistic access to the underutilized licensed spectrum. Wireless CR operating environment is typically characterized by its unreliable and unpredictable channel conditions and time availability due to fading and the randomness of primary radio (PR) activities. In such environment

Distributed Denial of Service (DDoS) plays a significant role in threatening the cloud-based services. DDoS is a kind of attack which targets the CPU, bandwidth and other resources and makes them unavailable to benign users. The DDoS attack has an enormous impact on multi-tenant cloud network than the traditional network due to the cloud features like virtualization, load balancing, resource scaling

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these

Classic security methods become less effective against the Internet of Things (IoT) cyber-attacks, such as cryptography. An urgent need for real-time and lightweight detection of cyber-attacks is needed to secure IoT networks. This demand is achieved by a reliable and efficient intrusion detection system (IDS) that can meet IoT environments' high scalability and dynamicity. Herein, we analyzed the

This study is to find the factors that affect the spatial change of forest land and purposefully predict the evolution trend of forest land space, so as to facilitate the rural planning work. The rural forest land situation in Zhangjiakou City of Hebei Province is analyzed, and the future evolution and development of forest space are predicted through analysis of correlation between the forest land

Storage workload prediction is a critical step for fine-grained load balancing and job scheduling in realtime and adaptive cluster systems. However, how to perform workload time series prediction based on a deep learning method has not yet been thoroughly studied. In this paper, we propose a storage workload prediction method called CrystalLP based on deep learning. CrystalLP includes workload collecting

Intrusion detection is one of the most crucial activities for security infrastructures in network environments, and it is widely used to detect, identify and track malicious threats. A common approach in intrusion detection systems (IDSs) specifically in anomaly detection is evolutionary algorithm that works as intrusion detector. Still, it has been challenging to design a precise and reliable IDS

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This

Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an increase in the efficiency of mobile applications in the two decades since the advent of the cloud paradigm. However, task offloading may not be a suitable option for delay-sensitive mobile applications because the cloud server

Data centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance counters and power consumption are their key “vital signs”. State-of-the-Art (SoA) monitoring systems provide built-in tools to collect performance measurements, and custom solutions to get insight on their power consumption. However, with the increase in

Mobile cloud computing augments smart-phones with computation capabilities by offloading computations to the cloud. Recent works only consider the energy savings of mobile devices while neglecting the cost incurred to the tasks which are offloaded. We might offload several tasks to minimize the total energy consumption of mobile devices; however, this could incur a huge monetary cost. Furthermore,

Periodic behaviors are essential to understanding objects’ movements. In real world situations, the collective movement of moving objects hides useful periodic patterns that people are more interested in. Discovering such periodic patterns is helpful in exploring human mobility, which can benefit many applications, such as urban planning, traffic management and public security. However, the previous

Recently Mobile adhoc networks (MANETs) have received the great attention of researchers as these models provide a wide range of applications. But MANET nodes are prone to various security threats. To overcome this issue, many trust management frameworks have been implemented in the literature. It has been found that the use of machine learning can predict trust values more efficiently. However, machine

Cloud computing is a preferred option for organizations around the globe, it offers scalable and internet-based computing resources as a flexible service. Security is a key concern factor in any cloud solution due to its distributed nature. Security and privacy are huge obstacles faced in its success of the on-demand service as it is easily vulnerable to intruders for any kind of attack. A huge upsurge

With the rapid development of multimedia and communication technologies, image technology has become more and more widely used in agriculture. Aiming at the advantages of structural similarity and MTF combined algorithm, this paper proposes an image quality evaluation algorithm based on structural similarity and MTF, which mainly adopts improved gamma correction method, dual homomorphic filter correction

With the first commercially available 5G infrastructures, worldwide’s attention is shifting to the next generation of theorised technologies that might be finally deployable. In this context, the cybersecurity of edge equipment and end-devices must be a top priority as botnets see their spread remarkably increase. Most of them rely on algorithmically generated domain names (AGDs) to evade detection

The modeling of complex computational applications as giant computational workflows has been a critically effective means of better understanding the intricacies of applications and of determining the best approach to their realization. It is a challenging assignment to schedule such workflows in the cloud while also considering users’ different quality of service requirements. The present paper introduces