With the development of the Internet of Things (IoT) field, more and more data are generated by IoT devices and transferred over the network. However, a large amount of IoT data is sensitive, and the leakage of such data is a privacy breach. The security of sensitive IoT data is a big issue, as the data is shared over an insecure network channel. Current solutions include symmetric encryption and access

Internet of Things (IoT) is enhancing the intelligence of the societies through a rapid transition to a smarter, automatic, responsive world due to the dramatic increase in the number of sensors deployed around the world. Collecting, modeling, and reasoning data generated by sensors play a crucial role in data analysis. Analyzing and interpreting real-time information transmitted through heterogeneous

The constant development of interrelated computing devices and the emergence of new network technologies have caused a dramatic growth in the number of Internet of Things (IoT) devices. It has brought great convenience to people’s lives where its applications have been leveraged to revolutionize everyday objects connected in different life aspects such as smart home, healthcare, transportation, environment

Sparse triangular solve (SpTRSV) is an important kernel in many scientific computing applications. In traditional viewpoints, accelerating SpTRSV by parallelizing the solution process is a challenging task. Dependencies among the variables that exist in the solution process not only restrict the parallelism that can be achieved, but also introduce large synchronization overhead among the parallel tasks

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in

Content-centric networking (CCN) becomes an imminent Internet architecture that emphasizes the data-centric approach for information retrieval instead of searching for the hosts in the network. CCN offers data (content) caching and its distribution capabilities to reduce average latency, the load of data servers, and improve network bandwidth utilization during content delivery. In this direction,

Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem.

Software defined network (SDN) decouples the network control and data planes. Despite various advantages of SDNs, they are vulnerable to various security attacks such anomalies, intrusions, and Denial-of-Service (DoS) attacks and so on. On the other hand, any anomaly and intrusion in SDNs can affect many important domains such as banking system and national security. Therefore, the anomaly detection

Cloud computing provides effective ways to rapidly provision computing resources over the Internet. For a better management of resource provisioning, the system requires to predict service-level agreements (SLAs) such as virtual machine (VM) startup times under various conditions of computing resources. The VM startup time is an important SLA parameter, which can impact other SLA parameters such as

Quality of Service (QoS) of virtual machines (VMs) is guaranteed by the Service Level Agreements (SLAs) signed between users and service providers during the renting of VMs. A typical idea to ensure the SLAs being reached is to predict the QoS of VMs accurately and then take the appropriate measures according to the prediction results timely. However, the QoS is affected by multiple VM-related features

Wireless network-on-chip (WiNoC) has been introduced as an efficient communication paradigm that can provide high bandwidth and low latency wireless links among long-distance cores. However, the increase of demand for using these shared wireless links and the presence of few numbers of these channels on a chip leads to port contention in WiNoCs. This problem increases the average packet latency as

Devising appropriate defense strategies against malware propagation in complex networks with minimal budget is a challenging problem in research community. This paper studies and compares various immunization strategies such as random immunization, targeted immunization, acquaintance immunization and high-risk immunization to prevent the outbreak of malware. Also, three measures of node centrality

This study aimed to develop a dynamic benchmark automation suite to measure a range of benchmark performances and evaluate the various high-performance computing (HPC) systems. Our suite supports an automated scaling test and profiling data based on hardware performance counters to analyze the system characteristics. We selected four HPC benchmarks—STREAM, High-Performance Linpack, High-Performance

Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other’s weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more

Cloud Computing has become a reliable solution for outsourcing business data and operation with its cost-effective and resource-efficient services. A key part of the success of the cloud is the multi-tenancy architecture, where a single instance of a service can be shared between a large number of users, also known as tenants. Service selection for multiple tenants presents a real challenge that has

Scientific workflow applications are used by scientists to carry out research in various domains such as Physics, Chemistry, Astronomy etc. These applications require huge computational resources and currently cloud platform is used for efficiently running these applications. To improve the makespan and cost in workflow execution in cloud platform it requires to identify proper number of Virtual Machines

In cloud computing environments, it is a great challenge to schedule a workflow application because it is an NP-complete problem. Particularly, scheduling workflows with different Quality of Service (QoS) constraints makes the problem more complex. Several approaches have been proposed for QoS workflow scheduling, but most of them are focused on a single QoS constraint. Therefore, this paper presents

Applying the load balancing technique to allocate requests that dynamically enter the cloud environment is contributive in maintaining the system stability, reducing the response time, and increasing the resource productivity. One of the main challenges in dynamic load balancing is that it increases inter-VM communication overheads (swapping files between VMs). In most of the methods proposed for load

Cloud platforms usually offer several types of Virtual Machines (VMs) with different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in the Amazon EC2 cloud, the user pays per use for on-demand VMs while spot VMs are instances available at lower prices. However, a spot VM can be terminated or hibernated by EC2 at any

Considering cloud computing continues to grow and flourish, the increasing number of cloud infrastructures results in unlimited resources and convenient pay-as-you-go services, which makes it essential to ensure software integrity (including OS, apps, and configurations) on such massive devices to guarantee both privacy and safety. As a key technical solution, remote attestation allows a remote entity

Since the adoption of portable devices like smartphones or smartwatches, the usage of applications has rapidly increased for supporting diverse daily activities from connecting with friends to measuring running distance. Since the Apps are an ubiquitous tool in everyday life, it is also an opportunity for a new digital business model. However, there are legacy applications implementing the core business

In the recent years, the Internet of Things has been becoming a vulnerable target of intrusion attacks. As the academia and industry move towards bringing the Internet of Things (IoT) to every sector of our lives, much attention needs to be given to develop advanced Intrusion Detection Systems (IDS) to detect such attacks. In this work, we propose a novel network-based intrusion detection method which

In any business network for record transactions, validation, and track assets, we have used a Blockchain platform, which is shared a distributed ledger that used cryptography techniques. Globally, there are different rules and operating procedures in the supply chain network. These regulations are used for end-to-end tracking between industries of different countries. The major problem faced today

The unprecedented growth in data volume results in an urgent need for a dramatic increase in the size of data center networks. Accommodating millions to even billions of servers in the structure of data center networks, while providing highly-efficient interconnecting architectures, is of prominent importance nowadays. Generally, high scalability, noticeable fault-tolerance, great network capacity

In recent years, several researchers have developed web-based product recommendation systems to assist customers in product search and selection during online shopping. In addition, the product recommendation systems deliver true personalization by recommending the products based on the other customer’s preferences. This study has investigated how the product recommendation system influences the customer’s

Minimum latency problem (MLP) is an NP-Hard combinatorial optimization problem. Metaheuristics use perturbation and randomization to arrive at a satisfactory solution under time constraints. The current work uses deterministic local search heuristic (DLSH) to identify a satisfactory solution without setting up metaheuristic parameters. A move evaluation procedure is proposed for the swap approach which

With the development of IoT devices and web services, the objects of the real world are more interconnected, which allows applications to extend their characteristics in different fields, including industrial or home environments, among other possible examples such as health, trade, transport, or agriculture. However, this development highlights the challenge of interoperability, because devices are

In large-scale atmospheric simulations, microphysics parameterization often takes a large portion of simulation time and usually consists of dozens of parameterization schemes. Performance optimizing these schemes one by one on different hardware platforms is tedious and error-prone even for skilled programmers. In this work, we propose AutoWM, a novel domain-specific tool for universal performance

Population-based meta-heuristic algorithms are among the dominant algorithms used to solve challenging real world problems in diverse fields. Whale Optimization Algorithm (WOA) is a recent swarm intelligence meta-heuristic algorithm based on the bubble-net feeding behavior of humpback whales. Despite its capability to solve complex optimization problems, WOA requires enormous amount of computations

Efficiency in cloud servers’ power consumption is of paramount importance. Power efficiency makes the reduction in greenhouse gases establishing the concept of green computing. One of the beneficial ways is to apply power-aware methods to decide where to allocate virtual machines (VMs) in data center physical resources. Virtualization is utilized as a promising technology for power-aware VM allocation

With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence

Cloud service has great convenience, because of its complex structure and dynamic nature, it is difficult to choose the best cost-effective strategy in the process of security and privacy information. To solve this problem, we construct a quantitative benefit of game strategy, use evolutionary game theory to build a game model of attack protection, propose the optimal protection strategy selection

With the exponential proliferation of cloud services, the decision of trustworthy cloud service selection has become tremendously challenging nowadays. It demands an accurate decision system to carry out a comprehensive assessment of cloud services from various aspects. The immense complexity and limitations of existing approaches reduce the credibility of the service selection process; thus, further

The cheque based banking transactions are widely used all over the world. The reason is that it is a hustle free and trusted way of money transaction. The existing cheque settlement process involves manual processing of submitted cheques, and large amounts of time for clearance. This paper proposes a framework to automate the cheque settlement process. This framework proposes cheque generation, cheque

Access to a variety of Internet of Things networks can be achieved through end-user devices such as smartphones or tablets. However, these devices are susceptible to theft, loss or unauthorized access. Although end-user devices are equipped with different means of authentication such as fingerprint readers, these methods are only employed at the time of access. Hence, an effective authentication mechanism

One of the technology for increasing the safety and welfare of humans in roads is Vehicular Cloud Computing (VCC). This technology can utilize cloud computing advantages in the Vehicular Ad Hoc Network (VANET). VCC by utilizing modern equipment along with current vehicles, can play a significant role in the area of smart transportation systems. Given the potential of this technology, effective methods

Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these

It is essential to ensure the data security of data analytical frameworks as any security vulnerability existing in the system can lead to a data loss or data breach. This vulnerability may occur due to attacks from live attackers as well as automated bots. However inside attacks are also becoming more frequent because of incorrectly implemented security requirements and access control policies. Thus

High-performance storage devices, such as Non-Volatile Memory express Solid-State Drives (NVMe SSDs), have been widely adopted in data centers. Especially, multiple storage devices provide higher I/O performance compared with a single device. However, the performance can be reduced in the case of workloads with mixed read and write requests (e.g., key-value stores) even though multiple storage devices

The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in science and industry. Whereas Infrastructure as a Service (IaaS) offerings have been commonly employed, more recently, serverless computing emerged as a novel cloud computing paradigm with the goal of freeing developers from resource

High energy consumption (EC) is one of the leading and interesting issue in the cloud environment. The optimization of EC is generally related to scheduling problem. Optimum scheduling strategy is used to select the resources or tasks in such a way that system performance is not violated while minimizing EC and maximizing resource utilization (RU). This paper presents a task scheduling model for scheduling

Cloud Computing is referred to as a set of hardware and software that are being combined to deliver various services of computing. The cloud keeps the services for delivery of software, infrastructure, and platform over the Internet based on the user’s demand. In the IT industry, cloud computing plays an important role to access services anywhere in the world. With increasing demand and popularity

Collaborative filtering which is the most successful technique of the Recommender System, has recently attracted great attention, especially in the field of e-commerce. CF is used to help users find their preferred items by assessing the preferences of other users to find most similar to the active one. Sparse datasets defend the efficiency of CF. Therefore this paper proposes two new methods that

The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce

The problem of minimizing the execution monetary cost of applications on cloud computing platforms has been studied recently, and satisfying the deadline constraint of an application is one of the most important quality of service requirements. Previous method of minimizing the execution monetary cost of deadline-constrained applications was the “upward” approach (i.e., from exit to entry tasks) rather

Data centers are growing densely and providing various services to millions of users through a collection of limited servers. That’s why large-scale data center servers are threatened by the overload phenomenon. In this paper, we propose a framework for data centers that are based on Software-defined networking (SDN) technology and, taking advantage of this technology, seek to balance the load between

This paper presents a novel “Distributed Deep Learning Framework” for a heterogeneous multi-GPU cluster that can effectively improve overall resource utilization without sacrificing training accuracy. Specifically, we employ a hybrid aggregation approach using a parameter-server and all-reduce schemes in order to address potential performance degradation problems in running deep learning applications

As Bitcoin and other cryptocurrencies become widely popular recently, the underlying concept—Blockchain—gets unprecedented attentions. One popular usage of Blockchain is a distributed replicated database. In this paper, we present initial studies on the challenges and opportunities of using Blockchain as a database for Internet-of-Things (IoT) applications. For IoT applications, latency is an important

Ensemble learning is increasingly used in sentiment analysis. Determining the parameter settings of ensemble models, however, is not easy. Besides its own parameters, an ensemble model has base-predictors that have their individual parameters. Some ensemble models use a specific base-predictor and could be optimised using standard metaheuristics such as the Particle Swarm Optimisation (PSO) approach

The MapReduce (MR) scheduling is a prominent area of research to minimize energy consumption in the Hadoop framework in the era of green computing. Very few scheduling algorithms have been proposed in the literature which aim to optimize energy consumption. Moreover, most of them are only designed for the slot-based Hadoop framework, and hence, there is a need to address this issue exclusively for

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm

Multi-core systems has evolved enormously during the last decade with the improvement in the integration technology which makes it possible to house large number of transistors on a single chip. Keeping large number of cores on a single chip results in heat dissipation, which is a serious problem and cannot be overlooked. To handle this, not only the green hardware but energy-efficient software techniques

The Internet unfolded enormous opportunities to the modern computing world where not only humans but also computers and machines, as well as any tiny sensing devices, can communicate and collaborate. The Internet of Things (IoT) is still a new concept in its early stages after 20 years of successful usage in various application domains. Nowadays, the "Internet of Things Ecosystem" term is being used

High-density, high-sampling rate EEG measurements generate large amounts of measurement data. When coupled with sophisticated processing methods, this presents a storage, computation and system management challenge for research groups and clinical units. Commercial cloud providers offer remote storage and on-demand compute infrastructure services that seem ideal for outsourcing the usually burst-like

This paper proposes a new version of the power of two choices, SQ(d), load balancing algorithm. This new algorithm improves the performance of the classical model based on the power of two choices randomized load balancing. This model considers jobs that arrive at a dispatcher as a Poisson stream of rate \(\lambda n,\)\(\lambda < 1,\) at a set of n servers. Using the power of two choices, the dispatcher

Advances in data acquisition have generated an enormous amount of data that captures business, commercial, technological and scientific information. However, some occurrences are rare or unusual, irrespective of a large amount of data available. These rare occurrences in data mining are usually referred to as outliers or anomalies. All these rare occurrences are infrequent. Sometimes it varies from

METAFONT is a parameterized font that can generate different font styles by changing its parameters. It generates bitmaps fonts (Generic Font and TeX Font Metric) that are commonly used in TeX-based environment. The bitmap fonts generated from METAFONT can be utilized in devices of any size or resolution without creating a new font file for each. However, these fonts cannot be used outside the TeX

The low capacity of main memory has become a critical issue in the performance of systems. Several memory schemes, utilizing multiple classes of memory devices, are used to mitigate the problem; hiding the small capacity by placing data in proper memory devices based on the hotness of the data. Memory tracers can provide such hotness information, but existing tracing tools incur extremely high overhead

Software-defined networking (SDN) is a state-of-the-art architectural approach to network management that allows more flexible management of a complicated large-scale network. For providing services with high performance, availability, and scalability, server clustering is widely used. This study develops a server cluster based on OpenFlow, which is the most common SDN protocol. To utilize cluster

Over the past few years, cloud computing model (e.g., Spark) has aroused huge attention. Differential evolution (DE) has been applied to cloud computing models by a number of researchers for its merits in solving large-scale global optimization problems (LSGO), and remarkable results have been achieved. Moreover, we noticed that a combination of better topology and migration strategy is critical to