Identifying the moderating effect of trust on the adoption of cloud‐based services

Cloud computing provides a way to coordinate and share relevant information and data on real‐time basis over an organization. The adoption of cloud services is one of the most emerging technological advances in the practice of current competitive business environment. The research done in this article is based on the analysis of the data obtained from the semiconductor sector. Cloud adoption would most likely be the best answer for them. However, because of various types of complexities, semiconductor industries may have to confront with a few trust issues while receiving cloud services. This article aims to identify the trust factors in the adoption of cloud services in semiconductor industries. Further, the moderating effect of these trust elements related to the technological, organizational, and environmental success factors has been discussed here. On the basis of literature survey, a hypothetical model has been developed, and the relationships among the latent variables have been studied by using structural equations. The study reveals that while trust factors moderate the technology and environment‐related success factors, there is not much moderating effect of the trust issues on the organization‐related success factors in the adoption of cloud services in semiconductor industries.     

A new approach for service migration in cloud‐based e‐commerce using an optimization algorithm

Service‐oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e‐commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e‐commerce in clouds—a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large‐scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e‐commerce. As this issue has an NP‐hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud‐oriented e‐commerce. In the presented model, we use the ACO algorithm to take the finest (near‐optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.     

On the performance of proximity‐based services

Proximity‐based services (PBS) are a subclass of location‐based services that aim to detect the closest point of interest by comparing relative position of a mobile user with a set of entities to be detected. Traditionally, the performances of PBS are measured on the basis of the norm of the estimation error. Although this performance criterion is suitable for location‐based services that aim tracking applications, it does not give enough information about the performance of PBS. This paper provides a novel framework quantifying the system performance of PBS by making use of spatially quantized decision regions that are determined according to service properties. The detection problem in PBS is modeled by an M‐ary hypothesis test, and analytical expressions for correct detection, false alarm, and missed detection rates are derived. A relation between location estimation accuracy requirements that are mandated by regulatory organizations and the performance metrics of PBS is given. Additionally, a flexible cost expression that can be used to design high‐performance PBS is provided. A system deployment scenario is considered to demonstrate the results. By using this framework, PBS designers can improve their command on the services’ behavior and estimate service performance before deployment. Copyright © 2011 John Wiley & Sons, Ltd.     

Proficient selection of gateway and base station by adroit algorithm in cloud‐VMesh network

Cloud computing is alluring for the vehicular mesh (VMesh) network. Cloud computing stipulates the computing resources and services on demand using the Internet. The VMesh network is a type of networking where each vehicle must not only capture and disseminate its own data but also serve as a gateway for another vehicle. The cloud‐VMesh network is a convergent technology encompassed of three key technologies, namely, vehicular mesh network, cloud computing, and networking. In this paper, we propose a new routing algorithm for cloud‐VMesh network, dubbed as adroit algorithm. The proposed adroit algorithm addresses the choosing of an optimal gateway and base station in the network layer from and to the service requesters and cloud server. The simulation study shows that the adroit algorithm augments the routing issues such as delay, packet loss, congestion and deployment cost in the cloud‐VMesh network, and it is justified based on the packet delivery ratio, average response time, routing overhead ratio, and packet collision ratio. The simulation results demonstrate that the adroit algorithm outperforms the existing protocols. Copyright © 2016 John Wiley & Sons, Ltd.     

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.     

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.     

Efficient revocable ID‐based encryption with cloud revocation server

The capability to efficiently revoke compromised/misbehaving users is important in identity‐based encryption (IBE) applications, as it is not a matter of if but of when that one or more users are compromised. Existing solutions generally require a trusted third party to update the private keys of nonrevoked users periodically, which impact on scalability and result in high computation and communication overheads at the key generation center. Li et al proposed a revocable IBE scheme, which outsources most of the computation and communication overheads to a Key Update Cloud Service Provider (KU‐CSP). However, their scheme is lack of scalability since the KU‐CSP must maintain a secret value for each user. Tseng et al proposed another revocable IBE scheme with a cloud revocation authority, seeking to provide scalability and improve both performance and security level. In this paper, we present a new revocable IBE scheme with a cloud revocation server (CRS). The CRS holds only one secret time update key for all users, which provides the capability to scale our scheme. We demonstrate that our scheme is secure against adaptive‐ID and chosen ciphertext attacks under the k‐CAA assumption and outperforms both schemes mentioned above, in terms of having lower computation and communication overheads.     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

SEDP‐based detection of low‐rate DoS attacks

Low‐rate Denial of Service (LDoS) is a new type of TCP‐targeted attacks, which attempt to deny bandwidth to TCP flows while sending at sufficiently low‐average rate to elude detection of DoS defense system. Therefore, LDoS attacks are difficult to be detected by routers and counter‐DoS mechanisms. In this paper, an approach of detecting LDoS attacks is proposed by using the technology of signal processing based on the model of spectral energy distribution probability. The proposed approach calculates variances between the incoming traffic of normal TCP and attack flows to a server by using packet sampling sequence within a certain period. The network traffic is converted from the time domain to the frequency domain forming a spectral signal, and the distribution probability of spectral energy is estimated based on spectrum characteristics of rectangular pulses. This approach explores that the energy of LDoS attacks is mostly distributed in the main lobe width while that of normal TCP traffic is just concentrated near zero in frequency domain. Both the spectral energy of normal TCP traffic and LDoS attacks distributed in main lobe are calculated, and an energy threshold is set as decision value based on statistical results according to energy distribution properties. The existence of LDoS attacks is determined and detected by comparing calculated variances with the preset decision threshold value. Tests on the detection performance of the proposed approach were performed in NS‐2 simulation environment, and detection rate was obtained by Hypothesis test. Experiment results show that the proposed approach has higher detection accuracy and less computation consuming. Copyright © 2014 John Wiley & Sons, Ltd.     

MC‐MIPOG: A Parallel t‐Way Test Generation Strategy for Multicore Systems

Combinatorial testing has been an active research area in recent years. One challenge in this area is dealing with the combinatorial explosion problem, which typically requires a very expensive computational process to find a good test set that covers all the combinations for a given interaction strength (t). Parallelization can be an effective approach to manage this computational cost, that is, by taking advantage of the recent advancement of multicore architectures. In line with such alluring prospects, this paper presents a new deterministic strategy, called multicore modified input parameter order (MC‐MIPOG) based on an earlier strategy, input parameter order generalized (IPOG). Unlike its predecessor strategy, MC‐MIPOG adopts a novel approach by removing control and data dependency to permit the harnessing of multicore systems. Experiments are undertaken to demonstrate speedup gain and to compare the proposed strategy with other strategies, including IPOG. The overall results demonstrate that MC‐MIPOG outperforms most existing strategies (IPOG, IPOF, IPOF2, IPOG‐D, ITCH, TConfig, Jenny, and TVG) in terms of test size within acceptable execution time. Unlike most strategies, MC‐MIPOG is also capable of supporting high interaction strengths of t > 6.     

LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

Nowadays, with the development of communication systems, massively multiplayer online games (MMOGs) have become very popular. In these games, the players all over the world dynamically interact with each other by sending play actions such as shootings, movements, or chatting in the form of MMOG sessions in real time through a large‐scale distributed environment. Leveraging affordable cloud computing to host such services is a widely investigated issue. It is because the arrival rate of players to the game environment has to make fluctuations, and the players expect services to be always available with an acceptable quality of service (QoS), especially in terms of the response time. Therefore, the dynamic provisioning of resources in order to deal with fluctuating demands due to variability in the arrival rate of players of the MMOG services is highly recommended. In this paper, we propose a learning‐based resource provisioning approach for MMOG services that is based on the combination of the autonomic computing paradigm and learning automata (LA). The remarkable performance of the proposed approach in terms of response time, cost, and allocated virtual machines (VMs) is assessed through simulation and comparison with the existing approaches.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

A simplified deniable authentication scheme in cloud‐based pay‐TV system with privacy protection

Cloud computing is a milestones for computing model, which enables on‐demand, flexible, and low‐cost usage of computing resources, especially for cloud storage. Nowadays, the services of cloud‐based pay‐TV systems are emerging endlessly. But these pay‐TV systems' privacy is not given enough attention. The users not only care about their information revealed during transmission processes but are also concerned about whether the video contents that they have seen were recorded by the pay‐TV systems or not. In this work, I propose a novel deniable authentication protocol in a cloud‐based pay‐TV system, named DAP‐TV, aiming to achieve mutual authentication, deniability, and privacy protection in cloud‐based pay‐TV systems. The unique feature of our scheme is deniability which means a pay‐TV system to identify a user is a legal user, but the pay‐TV system cannot prove video contents that the user has seen to any third party over an unsecured network. In additon, our scheme is based on chaotic maps, which is a highly efficient cryptosystem and is firstly used to construct a deniable authentication scheme in pay‐TV systems. Finally, we give the formal security proof and efficiency comparison with recently related works.     

Medium earth orbit optical satellite communication networks: Ground terminals selection optimization based on the cloud‐free line‐of‐sight statistics

In this paper, a methodology for the generation of cloud coverage time series correlated on both temporal and spatial domains and the estimation of cloud‐free line‐of‐sight (CFLOS) probability for a single optical ground station (OGS) and a network of OGSs (multiple OGS) for medium earth orbit (MEO) constellation satellite communication systems is presented. Spatial diversity is employed as a mitigation technique against cloud blockage to increase the availability of an OGS network (OGSN). In the second part of the paper, an effective algorithm based on the ant colony optimization (ACO) algorithm is proposed for the optimum selection of OGSs forming an OGSN. The proposed algorithm takes into consideration the spatial correlation between the OGSs. In the simulation results section, a MEO constellation system with 12 MEO satellites has been assumed. The integrated liquid water content (ILWC) statistical parameters that are necessary for the CFLOS time series are taken from ECMWF Re‐Analysis (ERA)‐Interim database, European Centre for Medium‐Range Weather Forecasts (ECMWF). Finally, numerical results for the optimum selection of OGSs using the proposed methodology are exhibited and commented. Interesting conclusions are drawn, and future work with technical challenges is briefly described.     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.     

Throughput reliability analysis of cloud‐radio access networks

This paper develops a stochastic geometry‐based analytical approach for calculating the throughput reliability of a cloud‐radio access network (C‐RAN) comprising randomly distributed remote radio heads (RRHs) and randomly located users. A tunable distance‐based RRH transmit power control mechanism along with cooperative joint transmissions by the RRHs is employed to achieve power savings and high throughput reliability. The analytical result for the throughput reliability serves as input to analysis of per user achievable average rate and C‐RAN network‐level performance metrics of spectral efficiency and energy efficiency. The analytical results are validated by Monte Carlo simulation results with good agreement, thus confirming the accuracy of the developed analytical approach. The key finding from the analysis is that by carefully tuning the RRH transmit power and cooperation parameter (cluster radius), it is possible to realize a threefold improvement in the energy efficiency along with 108% enhancement in the spectral efficiency of C‐RANs. Copyright © 2016 John Wiley & Sons, Ltd.     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.     

A critical evaluation of location based services and their potential

This Editorial lead article for the Journal of Location Based Services surveys this complex and multi-disciplinary field and identifies the key research issues. Although this field has produced early commercial disappointments, the inevitability that pervasive location-aware services on mobile devices will emerge means that much research is needed to inform these developments. The article reviews firstly: the science and technology of positioning, geographic information science, mobile cartography, spatial cognition and interfaces, information science, ubiquitous computing; and secondly the business, content and legal, social and ethics aspects, before synthesising the key issues for this new field.     

一种基于动态关键路径的云工作流调度算法探讨

云工作流调度算法是信息传输和沟通的主要方式。为适应当前活动实践需求,将云计算作为计算机运转调节的主要手段,合理进行云环境下工作调度因素的调节,在探索信息技术沟通渠道创新中发挥着不可忽视的作用。文章结合国内技术分析的基本情况,首先阐述了云工作流调度算法路径研究价值,其次着重从集合式工作调度、单元限制条件分析等方面,探究一种基于动态关键路径的云工作流调度算法要点,以达到明确技术关键条件,促进云工作服务手段调节革新的目的。     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.