While load balancing in distributed systems is extensively studied, traditional algorithms often assume static task loads, a simplification that fails under realistic dynamic conditions where rapid load fluctuations undermine balancing effectiveness. Addressing this critical gap, this paper introduces a novel approach by explicitly incorporating load uncertainty. We uniquely reformulate the load balancing problem using statistical performance aggregates—specifically, both mean and variance—to simultaneously optimize for balance and robustness. We propose a fully decentralized algorithm enabling nodes to collaboratively achieve this dual objective using these aggregates. Crucially, our algorithm offers mathematically provable guarantees, ensuring convergence to a global ideal state in logarithmic rounds even amidst varying loads, a guarantee often missing for dynamic scenarios. Simulations using real-world traces validate our approach, demonstrating significantly superior performance robustness and tighter load balancing compared to existing methods that rely solely on instantaneous or static metrics.

Margin distribution has been proven to play a crucial role in improving generalization ability. In recent studies, many methods are designed using large margin distribution machine (LDM), which combines margin distribution with support vector machine (SVM), such that a better performance can be achieved. However, these methods are usually proposed based on single-view data and ignore the connection between different views. In this article, we propose a new multiview margin distribution model, called MVLDM, which constructs both multiview margin mean and variance. Besides, a framework is proposed to achieve multiview learning (MVL). MVLDM provides a new way to explore the utilization of complementary information in MVL from the perspective of margin distribution and satisfies both the consistency principle and the complementarity principle. In the theoretical analysis, we used Rademacher complexity theory to analyze the consistency error bound and generalization error bound of the MVLDM. In the experiments, we constructed a new performance metric, the view consistency rate (VCR), for the characteristics of multiview data. The effectiveness of MVLDM was evaluated using both VCR and other traditional performance metrics. The experimental results show that MVLDM is superior to other benchmark methods.

Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base stations (BSs), synchronizing the information of all servers takes a long time, which is not applicable to the fluctuant environments. Meanwhile, each server from different BSs is typically selfish and rational, and can only obtain the imperfect information from its adjacent servers, which is a challenge for computation offloading among servers from a global perspective. This article proposes a game-based computation offloading scheme with imperfect information in multi-edge environments. First, a non-cooperative game with imperfect information is designed to analyze the complex interactions during synergistic computation offloading among MEC servers. Second, a Synergistic Balancing Offloading Algorithm (SBOA) through distributed decision-making manner to obtain the optimal offloading decision is proposed, which guarantees that the game converges to a Nash Equilibrium (NE) point. Extensive simulation results reveal the fast convergence of SBOA. As the percentage of high-load servers rises and the number of heavy tasks increases, SBOA performs better than other benchmark algorithms in terms of timeliness, effectiveness, and system completion time.

Location-based Systems (LBS) are popular for delivering customized information. However, some issues, such as place credibility, the efficiency of position calculations, and communication latency, pose challenges for indoor LBS. This work proposes the High-performance Perspective Platform (H3P) to help network managers understand network users’ information. The H3P provides indoor positioning services based on Wi-Fi 6 (IEEE 802.11ax) to stabilize service quality and ensure high computation efficiency for rapid service response. It utilizes Apache Kafka and Apache Zookeeper clusters on Kubernetes to handle large amounts of data. Wi-Fi usage data is transmitted to Kafka’s distributed real-time data streaming to enhance position credibility and the immediacy of position calculations. The data structure is also optimized to improve computation efficiency. Experimental results show that H3P improves data latency by up to 69% and data insertion latency by up to 73%. Additionally, H3P offers more stability and efficiency than Chang et al., 2012 in terms of data transmission, with an improvement of approximately 16.15%. This allows administrators to manage the network with user-friendly interfaces and a smooth user experience.

The diffusion model excels as a generative model for continuous data within a single modality.To extend its effectiveness to speech recognition, where the continuous speech frames are used as the condition to generate the discrete word tokens, building a conditional diffusion across discrete state space becomes crucial.This paper introduces a non-autoregressive discrete diffusion model, enabling parallel generation of a word string corresponding to a speech signal through iterative diffusion steps.An acoustic transformer encoder identifies the speech representation, serving as the condition for a denoising transformer decoder to predict the whole discrete sequence.To address the redundancy reduction in cross-modality diffusion, an additional feature decorrelation objective is integrated during optimization.This paper further reduces the inference time by using a fast sampling approach.The experiments on speech recognition illustrate the merit of the proposed method.

Industry 4.0 integrates industrial Internet of Things (IIoT), artificial intelligence, and cloud computing. The advent of the 5G era has undoubtedly provided a new impetus for the development of many Industry 4.0 applications, but it also presents some key security hurdles. The network scale is becoming larger and larger, the network environment is becoming increasingly complex, and security risks are prominent. Frequent issues, such as malicious attacks, privacy information disclosure, and data transmission security. In order to improve the reliability and security of cyberspace, this article proposes a blockchain-based hierarchical IIoT security solution mechanism. In addition, we propose a random reputation voting mechanism and blockchain (RRV-BC) scheme based on verifiable random function and reputation voting to reduce the communication cost during blockchain consensus communication. Meanwhile, the node credit scoring mechanism is introduced to dynamically evaluate the node credit. The simulation results show that the scheme improves the reliability of data communication and the fault tolerance of consensus mechanism by an average of 5% compared with the traditional practical byzantine fault tolerance (PBFT) protocol method.

Mobile edge computing (MEC) offers a promising technology that deploys computing resources closer to mobile devices for improving performance. Most of the existing studies support on-demand remote execution of the computing tasks in applications through program transformation, but they commonly assume that mobile devices merely resort a single server for computation offloading, which cannot make full use of the scattered and changeable computing resources. Thus, for object-oriented applications, we propose a novel approach, called FUNOff to support dynamic offloading of applications in MEC at the function granularity. First, we extract a call tree via code analysis and locate the function invocations that are suitable for offloading. Next, we refactor the code of related object functions according to a specific program structure. Finally, we make offloading decisions referring to the context at runtime and send function invocations to multiple remote servers for execution. We evaluate the proposed FUNOff on two real-world applications. The results show that, compared with other approaches, FUNOff better supports the computation offloading of object-oriented applications in MEC, which reduces the response time by 10.7%-58.2%.

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

With the rapid development of the Internet of Things (IoT) and emerging Mobile Edge Computing (MEC) technologies, various IoT image recognition services are revolutionizing our lives by providing diverse cognitive assistance. However, most existing related approaches are difficult to meet the diversified needs of users because they believe that the MEC platform is a single layer. In addition, due to the mutual interference between the data, it is not easy for them to extract the discriminative features (DFs) necessary to analyze the input data. To this end, this article proposes an IoT image recognition services framework for different needs in the MEC environment, which consists of Hierarchical Discriminative Feature Extraction (HDFE) and Sub-extractor Deployment (Sub-ED) algorithms. We first propose HDFE, which can avoid mutual interference between data by separately optimizing the data structure, thereby generating an extractor that extracts effective DFs. Then there is Sub-ED, which divides the extractor into a series of sub-extractors and deploys them on appropriate MEC platforms. By doing so, the IoT device can connect to the corresponding MEC platform according to its service types, and use the sub-extract to extract DFs. Then, the MEC platform uploads the extracted feature data to the cloud server for further processing, e.g., feature matching. Finally, the cloud server sends the processed result back to the IoT device. Experimental results show that compared with the state-of-the-art approaches, the proposed framework improves recognition accuracy by about 6% and reduces network traffic by up to 94%.

Microwave Power Transfer (MPT) is a promising technology to charge sensor devices (SDs) wirelessly in wireless sensor networks, and Cloud Computing (CC) can significantly promote task processing capacity of SDs. However, the propagation loss can dramatically influence the harvested energy and computation performance. So, for wireless sensor networks, we study an unmanned aerial vehicle-assisted cloud wireless charging system with the cooperation of the cloud server and the unmanned aerial vehicle (UAV). First, the UAV acts as the energy transmitter, and we design a quantitative charging scheme according to the energy-aware of SDs’ battery capacity. Second, the cloud server processes the tasks uploaded by SDs with the cooperation of the UAV, and we consider the communication connection between the cloud server and the UAV. Third, we propose the joint resource-trajectory optimization to reduce the energy consumption of UAVs. We put forward the Chaotically Adaptive Beetle Swarm Optimization Based on Cauchy Mutation (CABSOC) assisted block coordinate descent algorithm for addressing this non-convex problem. Numerical results indicate that the proposed solution can significantly improve the energy performance of the UAV. And the energy consumption is reduced by 11% compared with the solution with network function virtualization (NFV).

About 70% of the earth's surface is covered with water, and providing safe transportation in this vast area is the responsibility of the maritime transportation system. The maritime transportation system provides safety to the vessel's passengers, manages the route of the vessels to avoid collision, and monitors the vessel's condition during the journey. All tasks performed by the maritime transport system require continuous monitoring of the vessel performance, and for this, different IoT devices are installed on the vessel board. The IoT devices collect data related to the physical parameters of the vessel and share it with the coast-side servers. As the shared data contains confidential information about the vessel, there is a need for secure data sharing techniques, allowing only the authenticated personals to access the data received from the maritime IoT devices. In this context, we developed an identity-based secure information sharing scheme for the maritime transport system that maintains proper authentication measures. Our proposed approach uses the concept of identity-based encryption for authentication management. Our proposed approach is IND-sID-CCA secure and works efficiently with maritime IoT devices.

To realize the Interconnection of Everything (IoE) in the 6G vision, the space-based, air-based, and ground-based networks have shown a trend of integration. Compared with the traditional communications system, Space-Air-Ground Integrated Networks (SAGINs) can provide a seamless global network connection, while making full use of different network characteristics for synergy and complementarity. However, the increasing global coverage of the Internet, the growing number and variety of smart terminals, and the emergence of various high-bandwidth services have led to an explosion in communication data transmission. Despite the continuous development of communication technologies such as airborne processing and forwarding and high-throughput satellites, the quality of service (QoS) and quality of experience (QoE) for different users still cannot be guaranteed due to the power limitations of satellites and the scarcity of spectrum resources. In this work, drawing on wireless edge caching, considering that the relay of SAGIN has edge caching capability, the hot task is cached in the network nodes in advance. More, this process is optimized using distributed Deep Reinforcement Learning (DRL), thereby reducing transmission delay and relieving the pressure of task offloading on space-based networks. Compared with advanced related works, the long-term node utilization, link utilization, long-term average revenue-to-cost ratio and acceptance ratio of the proposed algorithm are increased by about 4.22%, 31.36%, 11.75% and 7.14%, respectively.

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance.

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation.

The Second International Workshop on Big Data in Emergent Distributed Environments (BiDEDE) focuses on scalable data management issues in emergent computing environments like (post) cloud and fog/edge/dew computing. All these computing environments aim to smoothly integrate scalable data management and processing into distributed environments, such that communication and computational costs are reduced for higher throughput, lower latencies of applications and extending battery lifetimes of nodes in companion with robust approaches to overcome failures and crashes. While there has been research in these areas for already over one decade, still many open challenges exist because of technology triggers like lightweight virtualization, increasing capabilities of nodes and increasing massive parallelization. This workshop supports lively discussions in these and related areas.

Abstract Nowadays, blockchain and Internet of Things (IoT) are two emerging areas of the Information Technology (IT) sector. These two emerging areas are used in various fields, such as supply chain, logistics and automotive industry. Due to the low processing power and storage space of IoT devices, users' medical information is usually saved in a centralized third party like a clinical repository or a cloud computing environment. Thus, in many cases, users lose control of their medical information, which can result in security disclosure and a single‐point impediment. So, an advanced solution is required to improve the data sharing process, while restricting it in terms of security. Blockchain technology with IoT can significantly affect the healthcare industry by improving its efficiency, security and transparency, as well as can provide more business opportunities. The efficient sharing of Electronic Health Record (EHR) can improve the treatment process, diagnosis accuracy, security and privacy. This article proposes a blockchain‐based IoT architecture to provide enhanced security of healthcare data by using Identity‐Based Encryption (IBE) algorithm. Here, the smart contract defines all the basic operations of the healthcare system, which can be beneficial to all stakeholders. Many experiments are executed to evaluate the efficiency of the proposed scheme. The results show that the proposed scheme is better than the existing renowned schemes.

Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Niño/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (~31%) and 249 (~39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall-induced disaster risk reduction strategies.

Medical imaging systems installed in different hospitals and labs generate images in bulk, which could support medics to analyze infections or injuries. Manual inspection becomes difficult when there exist more images, therefore, intelligent systems are usually required for real-time diagnosis. Melanoma is one of the most common and severe forms of skin cancer that begins from the cells beneath the skin. Through dermoscopic images, it is possible to diagnose the infection at the early stages. In this regard, different approaches have been exploited for improved results. In this study, we propose a two-stream deep neural network information fusion framework for multiclass skin cancer classification. The proposed technique follows two streams: initially, a fusion-based contrast enhancement technique is proposed, which feeds enhanced images to the pretrained DenseNet201 architecture. The extracted features are later optimized using a skewness-controlled moth–flame optimization algorithm. In the second stream, deep features from the fine-tuned MobileNetV2 pretrained network are extracted and down-sampled using the proposed feature selection framework. Finally, most discriminant features from both networks are fused using a new parallel multimax coefficient correlation method. A multiclass extreme learning machine classifier is used to classify lesion images. The testing process is initiated on three imbalanced skin data sets—HAM10000, ISBI2018, and ISIC2019. The simulations are performed without performing any data augmentation step in achieving an accuracy of 96.5%, 98%, and 89%, respectively. A fair comparison with the existing techniques reveals the improved performance of our proposed algorithm.

With astonishing speed, bandwidth, and scale, mobile-edge computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large-scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC servers must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we, in this article, propose neural network-based adaptive frequency adjustment (NAFA), an adaptive processor frequency adjustment solution, to enable an effective plan of the server’s energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server’s processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in the average request acceptance ratio and up to 50% reduction in average request processing time.

Mobile applications can leverage high-quality deep learning models such as convolutional neural networks and deep neural networks to provide high-performance cognitive services. Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. These approaches have two major limitations. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers are used to complete the tasks. Second, these approaches assume the pre-trained deep learning models deployed on cloud servers are static, and will not attempt to automatically upgrade in a context-aware manner. In this article, we propose a cloud-edge collaboration framework that facilitates delivering cognitive services with long-lasting, fast response, and high accuracy properties. We fist deploy a shallow model (i.e., EdgeCNN) on the edge server and a deep model (i.e., CloudCNN) on the cloud server. EdgeCNN can provide durable and rapid response cognitive services, because edge servers not only provide computing resources for mobile applications, but also close to users. Then, we enable CloudCNN to assist in training EdgeCNN to improve the performance of the latter. Thus, EdgeCNN also provides high-accuracy cognitive services. Furthermore, because users may continue to upload data to edge servers in real-world scenarios, we propose to use the ongoing assistance of CloudCNN to further improve the accuracy of the shallow model. Experimental results show that EdgeCNN can reduce the average response time of cognitive services by up to 55.08 percent and improve accuracy by up to 26.70 percent.

A deep neural network (DNN) has become increasingly popular in industrial Internet of Things scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks to the cloud or edges. Supporting such capability is not easy due to two aspects:Adaptability:offloading should dynamically occur among computation nodes.Effectiveness:it needs to be determined which parts are worth offloading. This article proposes a novel approach, called DNNOff. For a given DNN-based application, DNNOff first rewrites the source code to implement a special program structure supporting on-demand offloading and, at runtime, automatically determines the offloading scheme. We evaluated DNNOff on a real-world intelligent application, with three DNN models. Our results show that, compared with other approaches, DNNOff saves response time by 12.4–66.6% on average.

Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities.

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

introduction Share on Introduction to the Special Section on Cyber Security in Internet of Vehicles Authors: Ching-Hsien Hsu Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan 0000-0002-2440-2771Search about this author , Amir H. Alavi Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA 0000-0002-7593-8509Search about this author , Mianxiong Dong Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan 0000-0002-2788-3451Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 81pp 1–6https://doi.org/10.1145/3584746Published:15 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, called WS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

With the widespread deployment of various cloud computing and service-oriented systems, there is a rapidly increasing demand for collaborative quality-of-service (QoS) prediction. Existing QoS prediction methods have made great progress in modeling users and services as well as exploiting contexts of service invocations. However, they ignore the completion of service requests/responses relies on the underlying network topology and the complex interactions between Autonomous Systems. To tackle this challenge, we propose a topology-aware neural (TAN) model for collaborative QoS prediction. In the TAN model, the features of users, services, and intermediate nodes on the communication path are projected to a shared latent space as input features. To jointly characterize the invocation process, the path features and end-cross features are captured respectively through an explicit path modeling layer and an implicit cross-modeling layer. After that, a gating layer fuses and transmits these features to the prediction layer for estimating unknown QoS values. In this way, TAN provides a flexible framework that can comprehensively capture the invocation context for making accurate QoS prediction. Experimental results on two real-world datasets demonstrate that TAN significantly outperforms state-of-the-art methods on the tasks of response time, throughput, and reliability prediction. Also, TAN shows better extensibility of using auxiliary information.

Industrial Internet of Things is focused to improve the performance of smart factories through automation and scalable functions. IoT paradigm, information and communication technology, and intelligent computing are assimilated as a single entity for industrial automation, optimization, sharing and security, and scalability. In a view of the security requirement in smart industry data sharing through IoT, this article introduces a blockchain-assisted secure data sharing (BSDS) model. This model is responsible for administering inbound and outbound security in data acquisition and dissemination. The inbound acquisition is first classified using recurrent learning to identify adverse sequences in data dissemination. In the outbound security measure, end-to-end authentication based on the blockchain information of reputation and sequence differentiation is engaged. The blockchain paradigm controls the data gathering and dissemination instances through the classification and integrity verification in both the industry and processing terminals. For this purpose, the functions of the blockchain are riven for data gathering and monitoring in the smart industry whereas integrity and sequence verification is performed by the nonmining blockchain terminal in the processing environment. The integrated security measures are capable of maximizing the response rate by confining false alarm progression, failure rate, and time delay. Statistical analysis shows that the BSDS achieves a 5.67% high response rate and reduces the failure rate by 2.14%. Further, it achieves 3.12%, maximizes response rate by 6.63%, and reduces delay by 11.91%, respectively.

Abstract This article presents an advanced visualization and analytics approach for financial research. Statistical arbitrage, particularly pairs trading strategy, has gained ground in the financial market and machine learning techniques are applied to the finance field. The cointegration approach and long short‐term memory (LSTM) were utilized to achieve stock pairs identification and price prediction purposes, respectively, in this project. This article focused on the US stock market, investigating the performance of pairs trading on different types of portfolios (aggressive and defensive portfolio) and compare the accuracy of price prediction based on LSTM. It can be briefly concluded that LSTM offers higher prediction precision on aggressive stocks and implementing pairs trading on the defensive portfolio would gain higher profitability during a specific period between 2016 and 2017. However, predicting tools like LSTM only offer limited advice on stock movement and should be cautiously utilized. We conclude that analytics and visualization can be effective for financial analysis, forecasting and investment strategy.

Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node-based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.

Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces information scheduling and optimization framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determined using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of Internet of Things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics, such as delayed information, processing time, audit data, and information distribution, are analyzed for proving the reliability of the framework.

Resource allocation and management in an Internet-of-Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic patterns and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in an account of its interoperability and heterogeneous support, this manuscript introduces a response-aware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by a multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber-physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decision making in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.

Smartphone devices are widely used in our daily lives. However, these devices exhibit limitations, such as short battery lifetime, limited computation power, small memory size and unpredictable network connectivity. Therefore, numerous solutions have been proposed to mitigate these limitations and extend the battery lifetime with the use of the offloading technique. In this paper, a novel framework is proposed to offload intensive computation tasks from the mobile device to the cloud. This framework uses an optimization model to determine the offloading decision dynamically based on four main parameters, namely, energy consumption, CPU utilization, execution time, and memory usage. In addition, a new security layer is provided to protect the transferred data in the cloud from any attack. The experimental results showed that the framework can select a suitable offloading decision for different types of mobile application tasks while achieving significant performance improvement. Moreover, different from previous techniques, the framework can protect application data from any threat.

Presents corrections to author affiliation information in the above named paper.

Leveraging power consumption models in software systems can achieve easy deployment of low-cost, high-availability power monitoring in cloud datacenters that are usually large-scale, heterogeneous and frequently scaling up. However, traditional regression-based power consumption models generally have two drawbacks. First, their mathematical forms are usually fixed and determined a priori. This may cause unacceptable increase of error or over-fitting as the power signatures of cloud servers are usually uncertain. Second, the characteristic of workload dispatched to cloud servers is constantly changing while regression-based models can hardly generalize to a wide range of servers and workload types. As a novel solution, we in this paper propose a server power consumption model based on Elman Neural Network (PCM-ENN), aiming to allow accurate and flexible power estimation. PCM-ENN is an end-to-end black box model capable of learning the temporal relation between samples in a time series of power consumption. We trained and evaluated PCM-ENN on two power sequence datasets collected from heterogeneous hardware and operating systems running quasi-production benchmarks like CloudSuite. Experimental result shows that PCM-ENN generated accurate estimates on server power consumption with only small errors, outperforming widely-used linear regression model and NARX model in terms of accuracy.

Big data analytics and processing require complex architectures and sophisticated techniques for extracting useful information from the accumulated information. Visualizing the extracted data for real-time solutions is demanding in accordance with the semantics and the classification employed by the processing models. This article introduces fuzzy-optimized data management (FDM) technique for classifying and improving coalition of accumulated information based semantics and constraints. The dependency of the information is classified on the basis of the relationships modeled between the data based on the attributes. This technique segregates the considered attributes based on similarity index boundaries to process complex data in a controlled time. The performance of the proposed FDM is analyzed using a real-time weather forecast dataset consisting of sensor data (observed) and image data (captured). With this dataset, the functions of FDM such as input semantics analytics and classification based on similarity are performed. The metrics classification and processing time and similarity index are analyzed for the varying data sizes, classification instances, and dataset records. The proposed FDM is found to achieve 36.28% less processing time for varying classification instances, and 12.57% high similarity index.

The advent of 5G and beyond networks paves the way for significant innovations in industrial applications and services. It does not only offer broadband services through mobile networks. Indeed it unleashes application layer innovation. Potentially beneficial to millions of users. Though it looks attractive, it possesses very complex network functions proven to be well defined and consistent. This special issue is hosted to address three major drawbacks in 6G-enabled NIB industrial applications. The first is to implement a portable solution that constitutes a core network product that can be easily implemented across small hardware platforms. The second is to find cost-effective solutions, with each core supporting a small network. Finally, finding new innovative paradigms for 6G enabled NIB that can operate in very challenging environments in a qualitative, flexible, and scalable manner.

Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.

Software defined network (SDN) architectures are assimilated with vehicular communication networks in order to improve the real-time application support for the driving users. Internet of vehicles (IoV) paradigm provides information-centric application support for the road-side users. Information sharing through the road-side units (RSUs) influences the application services due to the frequent change in physical attributes of the vehicles. Considering the application oriented services and information handling in IoV, this article introduces information-centric content management framework (ICMF) for effective information utilization in the vehicular networks. This framework performs data acquisition, smoothing and management process for effective information analysis and better offloading. The proposed framework incorporates the functions of linear vector quantization for classifying acquired information and segregating it for maximum utilization. This quantization is recurrent in both continuous and alternating learning process to improve the reliability of information handling and management. The performance of the proposed framework is verified using simulations and the results prove its efficiency. The proposed framework is found to maximize resource utilization and offloading ratio with less analysis time and overhead. The simulation is verified for the varying density of vehicles, offloading ratio, and communication time, information utilization.

In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. However, there still remain some challenging issues on this direction, as how to effectively exploit complex contexts to improve prediction accuracy, and how to realize collaborative QoS prediction of multiple attributes. Inspired by the principles of deep learning, we have proposed a universal deep neural model (DNM) for making multiple attributes QoS prediction with contexts. In this model, contextual features are mapped into a shared latent space to semantically characterize them in the embedding layer. The contextual features with their higher-order interactions are captured through the interaction layer and the perception layers. Multi-tasks prediction is realized by stacking task-specific perception layers on the shared neural layers. Armed with these, DNM provides a powerful framework to integrate with various contextual features to realize multi-attributes QoS prediction. Experimental results from a large-scale QoS-specific dataset demonstrate that DNM achieves superior prediction accuracy in term of mean absolute error (MAE) compared with the state-of-the-art collaborative QoS prediction techniques. Additionally, the DNM model has a good robustness and extensibility on exploiting heterogeneous contextual features.

Summary Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K‐means and mixed‐integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.

The articles in this special section focus on advancements of utility and cloud computing technologies and services. Computing is rapidly moving towards a model where it is provided as services that are delivered in a manner similar to traditional utilities such as water, electricity, gas, and telephony. In such a model, users access services according to their requirements, without regard to where the services are hosted or how they are delivered. Several computing architectures have evolved to realize this utility computing vision, including Grid computing, Service- Oriented Architecture (SOA) and Cloud computing,which has recently shifted into the center of attention in the ICT industry. Increasing numbers of IT vendors are promising to offer applications, storage and computation hosting services with conforming Service-Level Agreements

Advanced Driver Assistance System (ADAS) is a typical Cyber Physical System (CPS) application for human–computer interaction. In the process of vehicle driving, we use the information from CPS on ADAS to not only help us understand the driving condition of the car but also help us change the driving strategies to drive in a better and safer way. After getting the information, the driver can evaluate the feedback information of the vehicle, so as to enhance the ability to assist in driving of the ADAS system. This completes a complete human–computer interaction process. However, the data obtained during the interaction usually form a large dimension, and irrelevant features sometimes hide the occurrence of anomalies, which poses a significant challenge to us to better understand the driving states of the car. To solve this problem, we propose an anomaly detection framework based on RBM-LSTM. In this hybrid framework, RBM is trained to extract general underlying features from data collected by CPS, and LSTM is trained from the features learned by RBM. This framework can effectively improve the prediction speed and present a good prediction accuracy to show vehicle driving condition. Besides, drivers are allowed to evaluate the prediction results, so as to improve the accuracy of prediction. Through the experimental results, we can find that the proposed framework not only simplifies the training of the entire neural network and increases the training speed but also greatly improves the accuracy of the interaction-driven data analysis. It is a valid method to analyze the data generated during the human interaction.

The Internet of Things (IoT) is the latest Internet evolution that incorporates billions of sensors, actuators, and related software services that collectively distill high value information, perform actions that affect the physical world, and support a variety of applications controlled by different organizations and individuals. IoT's ability to observe and affect the physical world presents a unprecedented opportunity for creating IoT-based smart services and products that address grant challenges in emerging opportunities in areas such as climate change, precision agriculture, smart health, advanced manufacturing, and smart cities. This special issue identifies and addresses some of the key issues that hinder the development of IoT-based solutions. It includes articles that present the latest innovations in IoT security and privacy, IoT data quality and analysis, IoT resources and task management, as well as examples of IoT-based application services and domains.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the Internet of Things (IoT). However, conventional network gateways reported in the literature are unable to provide sustainable solutions to the challenges posted by the massive amounts of M2M communications requests, especially in the context of the IoT for smart cities. In this paper, we present an admission control model for M2M communications. The model differentiates all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests by routing them into one low-priority queue, aiming to reduce the number of requests from various devices to the access point in the IoT for smart cities. Also, an admission control algorithm is devised on the basis of this model to prevent access collision and to improve the quality of service. Performance evaluations by network calculus, numerical experiments, and simulations show that the proposed model is feasible and effective.

Mobile edge computing is a promising paradigm that brings computing resources to mobile users at the network edge, allowing computing-intensive and delay-sensitive applications to be quickly processed by edge servers to satisfy the requirements of mobile users. In this chapter, we first introduce a hierarchical architecture of mobile edge computing that consists of a cloud plane, an edge plane, and a user plane. We then introduce three typical computation offloading decisions. Finally, we review state-of-the-art works on computation offloading and present the use case of joint computation offloading.

Summary Cloud has become a very popular infrastructure for many smart city applications. A growing number of smart city applications from all over the world are deployed on the clouds. However, node failure events from the cloud data center have negative impact on the performance of smart city applications. Survivable virtual machine placement has been proposed by the researchers to enhance the service reliability. Because of the ignorance of switch failure, current survivable virtual machine placement approaches cannot achieve the best effect. In this paper, we study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Firstly, we formulate the network failure–aware redundant virtual machine placement problem as an integer nonlinear programming problem and prove that the problem is NP‐hard. Secondly, we propose a heuristic algorithm to solve the problem. Finally, extensive simulation results show the effectiveness of our algorithm.

Through virtualization, multiple virtual machines (VMs) can coexist and operate on one physical machine. When virtual machines compete for memory, the performances of applications deteriorate, especially those of memory-intensive applications. In this study, we aim to optimize memory control techniques using a balloon driver for server consolidation. Our contribution is three-fold: (1) We design and implement an automatic control system for memory based on a Xen balloon driver. To avoid interference with VM monitor operation, our system works in user mode; therefore, the system is easily applied in practice. (2) We design an adaptive global-scheduling algorithm to regulate memory. This algorithm is based on a dynamic baseline, which can adjust memory allocation according to the memory used by the VMs. (3) We evaluate our optimized solution in a real environment with 10 VMs and well-known benchmarks ( DaCapo and Phoronix Test Suites). Experiments confirm that our system can improve the performance of memory-intensive and disk-intensive applications by up to 500 and 300 percent, respectively. This toolkit has been released for free download as a GNU General Public License v3 software.

Cloud computing refers to both the applications delivered as services over the Internet and hardware and system software in the cloud server farm that provides those services. The research on server backup of cloud server farm appears to be an important issue of cloud computing economics. Optimal logistic policy should be considered to be a profit-oriented framework simultaneously for providing qualified service to cloud users while the whole cloud center is under construction. The kernel point of the proposed approach is that a novel design pattern is developed for approaching optimal profit on logistics using the finite-source queuing theory. To model the proposed approach for qualitative analysis, a Petri Net model was developed to configure all relevant system aspects in a concise fashion. On quantitative work, a comprehensive mathematical analysis on profit pattern has been made in detail. Relevant simulations have also been conducted tovalidate the proposed optimization model. The design illustration is presented to demonstrate engineering application scenario in cloud environment, hence the proposed approach indeed provides a feasibly profit-oriented framework to meet logistic economy.

Cloud computing provides a useful metaphor for combining capability at different scales. Such environments may therefore consist of devices ranging from handheld smart phones to supercomputers, to serve communities ranging from individuals to whole industries. Cloud computing requirements are also widely regarded as a key enabler for next generation network environments and are expected to drive the creation of both jobs and commercial products. Cloud computing is also sometimes referred synonymously as next generation data centers, with an architecture consisting of network of virtual services (hardware, database, user-interface, and application logic) so that users are able to deploy and access applications globally and on demand at competitive costs depending on user driven quality of service requirements. Developers with innovative ideas for new Internet services no longer require large capital outlays in hardware to deploy their services, or human expense to operate it 1-7. This technology is being driven by and used in a wide range of academic, research, and commercial application areas. This use is producing important new practical experience in a variety of different problem domains in each of these areas. There are also new computational methods, such as mobile agents, cellular automata, and massively parallel neural networks, which are particularly suited to concurrent execution. As an enabler, this technology is leading to a rapid growth in both scientific and information applications that will, in turn, enable additional requirements for cloud computing technologies to be identified. These will impact academia, business, and education 4-6, 8-12. Given this context, this special issue calls for high-quality research papers in the development of cloud computing technology for mobile environments. In particular, the special issue showcased the most recent achievements and developments in the realm of cloud computing. Original research articles were solicited covering theoretical studies, practical applications, new communication technology, and experimental prototypes. All submitted papers were peer reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. The published papers are expected to focus on novel approaches for advanced cloud computing of mobile environments for future computing environments and to present high-quality results for tackling problems arising from the ever-growing advanced cloud computing technologies and services for future computing environments. We have received many manuscripts. Only five manuscripts with high quality were finally selected for this special issue. Each manuscript selected was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. We present a brief overview of each manuscript in the following. Recent advances in cloud computing for mobile environments have created new topics of interest that included the following: (1) cloud economics in mobile and pervasive environments; (2) cloud computing programming and application development; (3) scalability, discovery of services, and data in cloud computing infrastructures; (4) trust and clouds for mobile infrastructure; (5)client-cloud computing challenges; (6) grid computing services and applications for cloud deployments; (7) virtualization, modeling, and metadata in grid; (8) cloud monitoring, control, and management; (9) traffic and load balancing in multi-vendor cloud environments; (10) user profile for cloud environments; (11) performance and security in pervasive and ubiquitous cloud environments; (12) fault tolerance, resilience, survivability, and robustness in cloud environments; and (13) geographical constraints for deploying clouds. And also, the special issues include manuscripts of applications of cloud computing for mobile environments as follows: (1) concurrent solutions to specific problems in academia, industry, and society; (2) concurrent algorithms and computational methods; (3) programming environments, operating systems, tools, concurrent languages, compilers, and interpreters; (4) performance prediction, analysis, models, and results; (5) applications, algorithms, and software technologies arising from the World Wide Web; (6) unification of computing and communication and unification of parallel and distributed computing; (7) social network analysis facilitated through cloud environments; (8) ad hoc and mesh networks via cloud systems; and (9) managing streaming content with cloud environments. In these several topics of cloud computing, some articles proposed the following: Park et al. 13 proposed and experimented the group classification for a dynamic environment, where information of mobile devices is changing. The existing studies, which provide arbitrary cutoff points, are not appropriate in a dynamic environment. The proposed method provides dynamic grouping by setting cutoff points and reflecting the changes of mobile devices after calculating the entropy values. We also proposed a fault tolerance technique for a dynamic environment based on our grouping algorithm. In the proposed algorithm, different fault tolerance techniques are applied to every group according to the different information of mobile devices of each group. Choi and Lim 14, in order to maximize the parallelism within a basic block, we exploit a novel register allocation approach, called as alternative register allocation. The alternative register allocation enables the target processors to improve the parallelism by eliminating the instruction dependency within a translated block. Alternative register allocation divides the register set of the target processor into two separate groups that are used to alternately handle translated blocks. This paper achieved performance improvement by using the two sets of register allocation orders. Alternative register allocation is to maximize the pipelining performance, which is a very popular technique for all modern microprocessors, eliminating the false data dependency. Thus, this paper resolves the false dependency problem and outperforms conventional approach by up to 26.3% for some cases. Sood 15 provided an efficient prediction model for adaptive resource provisioning in the cloud for dynamic resource management. The strategy used in this paper helps the cloud service provider for optimizing resource allocation as well as making the cloud cost-effective. Load-balancing algorithm is used to divide the resource requirements of application in different clouds and, at the same time, to minimize the cost to the best possible extent. The approach used in this paper is best suitable for making the decision to allocate or release a Virtual Machine (VM) in time. This paper had also evaluated the metrics described in manuscript in terms of cost and functionality by performing various experiments. These experiments validate the accuracy of the proposed method, and the results show that the prediction accuracy of the proposed technique is considerably effective. Thus, the proposed approach is highly effective in terms of both cost and functionality. Wang et al. 16 provided a dynamic power model based on division of computation and memory and a static power model based on real-time temperature perception. The evaluation of nine typical applications showed that with the method proposed in this research, about 35% of dynamic energy (within 10% error bounds) subject to performance constraint conditions can be saved, while about 21.6% of static energy could be saved with a performance loss of less than 2.8%. De and De 17 proposed a new mobile cloud architecture, where the interactions between mobile applications and mobile cloud services are decoupled during service invocation and delivery phases. The tuple space model is used in this architecture to decouple such interactions, thereby improving adaptability and robustness of mobile cloud computing. This paper also suggested an approach of modeling and formally verifying the correctness of the proposed architecture, which also validated its adaptability and robustness during interactions between mobile applications and mobile cloud services. The modeling and verification are carried out using the mobile UNITY model. Our special thanks go to Professor Geoffrey C. Fox and Professor David W. Walker who are editors-in-chief of Concurrency and Computation: Practical and Experience and all editorial staffs for their valuable supports throughout the preparation and publication of this special issue. We would like to thank all authors for their contributions to this special issue. We also extend our thanks to the external reviewers for their excellent help in reviewing the manuscripts.

The purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the 2015 International Conference on Cloud Computing and Big Data 1. This conference brings together researchers and industry practitioners in order to exchange information regarding advancements in the state-of-the-art and practice of cloud computing, big data, and social network, as well as to identify emerging research topics and define the future directions of cloud computing, big data, and social network. Nowadays, various social applications such as blogs, e-mail, instant messaging, social networking (Facebook, Twitter, LinkedIn, etc.), wikis, and social bookmarking have been widely popularized by providing digital platforms for social interaction. Today's online social network or mobile social network pervades all aspects of our daily lives and contains vast amount of data. From this vast amount of data, ability is needed to extract and analyze the social networks of a new era that can be consisted of millions of nodes and connections. Meanwhile, various critical issues such as clustering and evolution mining of social networks, modeling and understanding of social behaviors via computational means, information spread and modeling, social influence analysis, social recommendations, etc., provide significant challenges. This special issue is devoted to analysis of these large-scale social structures and what is more important to identify the areas where social network analysis can be applied and provide the knowledge that is not accessible for other types of analysis. This special issue contains research papers addressing the state-of-the-art in social network analysis and its application. A set of carefully selected works was invited based on the original presentations at the 2015 International Conference on Cloud Computing and Big Data 1, which was held in Huangshan, China, 17–19 June 2015. The extended works have been thoroughly reviewed by an international technical reviewing committee, and only nine papers covering a wide range of relevant challenges in social network were selected for this special issue. The manuscripts tackle research on different topics, including networking, infrastructures, algorithms, applications, and miscellaneous. The set of accepted papers can be organized under the following key subjects and subsections and are briefly described in the remaining parts of this section. Data center is the most important infrastructure for many key applications, such as social network analysis, web service, etc. Data center networks usually mix with a large amount of latency-agnostic background flows and a large number of latency-sensitive application flows. Directly using the traditional TCP in data center networks, which is deadline agnostic, may suffer from performance and efficiency problem. The recent works that improve TCP focus on the latency-sensitive flows themselves but cannot effectively ensure deadline for the latency-sensitive flows. In the first paper, ‘Make-way: transporting latency-sensitive flows nonblockingly in oversubscription data center networks’ 2, by Deng Gang, Gong Zhenghu, and Wang Hong, a new data center network transport protocol, called Make-way, is proposed for satisfying the deadlines of latency-sensitive flows. In Make-way, once a latency-sensitive flow encounters congestion, the latency-agnostic background flows will make way for it. Especially, Make-way does not need any special support of hardware modification. Because the latency-agnostic flows in data center networks usually contribute the majority of traffic, by doing so, the latency-sensitive flows may be transported nonblockingly in data center networks and thus can meet their deadlines. Extensive simulation results show that Make-way can meet the deadlines of latency-sensitive flows with a probability of more than 97%. MapReduce has been widely regarded as a flexible, scalable, and easy-to-use distributed programming paradigm for big data processing such as social network data analysis. The second paper, ‘MEMoMR: accelerate MapReduce via reuse of intermediate results’ 3, by Hong Yao, Jinlai Xu, Zhongwen Luo, and Deze Zeng, tries to accelerate the MapReduce performance from the intermediate result-reusing aspect. The authors observe that existing intermediate result-reusing mechanism is not efficient enough, as many input/output operations are wasted. Efficient reusing of the intermediate results could potentially improve the MapReduce performance. Inspired by such fact, they propose a framework, named more efficient intermediate result reusing for MapReduce (MEMoMR), by introducing a novel reusing mechanism that can substantially reduce the input/output overhead. To this end, they invent a new metadata description method and apply it in the reusing phase. They practically realize MEMoMR and evaluate its performance by implementing it in a real cluster. The experiment results show that MEMoMR can improve the system performance as high as 23.4%, comparing against Dache. Stream processing is one of the key technologies for data processing in social networks. In order to speed up processing in stream processing systems, a data analysis operator could be partitioned into n parallel tasks, which are usually deployed on m nodes coexisting with other application operators. Because the node performance can vary in unpredictable ways, the tasks should be redistributed at runtime for stream applications to meet their strict latency requirements. In order to redistribute the tasks to the best node and dynamically adapt to resource or load fluctuations, the third paper, ‘Runtime-aware adaptive scheduling in stream processing’ 4, by Yuan Liu, Xuanhua Shi, and Hai Jin, presents a runtime-aware adaptive schedule mechanism that aims at minimizing the operator processing latency and minimizing the latency difference between different nodes' tasks. A new abstraction called performance cost ratio (PCR) is proposed, which evaluates the node performance. The higher the node's PCR is, the less cost the node will pay for processing one tuple and the more tasks should be deployed on it. The PCR-based quantitative algorithm applies itself to make task loads quantized to the processing capacity of nodes, move the minimum amount of operator's tasks, and keep the tasks locally at the same time. A runtime-aware adaptive scheduler is implemented as an extension to stream processing system, Storm. Matrix factorization is one of leading techniques for many applications, including social network-based recommendation systems. Many parallel stochastic gradient descent (SGD) methods have been proposed to address the matrix factorization issue on shared-memory (multi-core) systems and distributed systems. However, these methods cannot be accelerated significantly on graphics processing unit (GPU) systems because the serious over-writing problem and thread divergence may occur. The fourth paper, ‘GPUSGD: a GPU-accelerated stochastic gradient descent algorithm for matrix factorization’ 5, by Jing Jin, Siyan Lai, Su Hu, Jing Lin, and Xiaola Lin, proposes an efficient GPU algorithm, named GPUSGD, to solve the matrix factorization problem based on SGD method. The proposed GPUSGD not only can handle the over-writing problem but also can avoid the performance loss caused by the thread divergence. The experimental results show that, compared with the existing state-of-the-art parallel methods, GPUSGD performs much better in accelerating the matrix factorization. The authors also claim that the proposed algorithm is the first work of developing a parallel SGD method to improve the matrix factorization on the GPU. Correlation analysis is both popular and useful in a number of social networking research, particularly in the exploratory data analysis. In the fifth paper, ‘Using Spearman's correlation coefficients for exploratory data analysis on big dataset’ 6, by Chengwei Xiao, Jiaqi Ye, Rui Máximo Esteves, and Chunming Rong, three well-known and often-used correlation coefficients – Pearson product-moment correlation coefficient and Spearman and Kendall rank correlation coefficients – are compared from definition to application domain. Based on the characteristics of the pump's vibration dataset, the nonparametric and distribution-free Spearman rank correlation coefficient is introduced to analyze the relationship between the pump's state and each of the 207 880 variables. The percentage of variables and exact variables' tables with high Spearman's correlation coefficients for state 1 and state 2, state 1 and state 3, state 2 and state 3, and 3 states in different files are obtained respectively, which has important valuation for the future research of the unsupervised machine learning system. Alongside the rapid development of e-commerce, purchase prediction has become an increasingly important consideration for a wide variety of retail platforms. Along with the development of social networks, much attention has been given to the influence of the social networks on users' purchase. The sixth paper, ‘Purchase prediction using tmall-specific features’ 7, by Yang Zhao, Liang Yao, and Yin Zhang, proposes a framework which combines machine learning methods with a threshold-moving approach to predict sets of pairs (user ID and brand ID) in terms of whether a certain brand is purchased by a specified user according to his or her historical activity records. Three specific feature groups are extracted: click features, purchase features, and collect-and-cart features using a dataset from Tmall, a Chinese business-to-consumer online retail platform. Next, seven user purchase prediction experiments with different combinations of the three feature groups are conducted, and the purchase prediction performance is observed. The results show that a combination of all three feature groups, with 27 features in total, provides valuable purchase prediction contributions. It is identified that the last-day shopping cart count, from the collect-and-cart feature group, is a valuable feature capable of markedly affecting prediction performance. In addition, the purchase feature group is also shown to have a greater impact on purchase prediction. Social network has become a very popular way by which Internet users communicate and interact online. Effective user interest prediction is significant for service providers in a set of application scenarios such as user behavior analysis, resource recommendation, etc. In the seventh paper, ‘Interest prediction in social networks based on Markov chain modeling on clustered users’ 8, submitted by Xianghan Zheng, Dongyun An, and Wenzhong Guo, user interest prediction method based on the Markov chain modeling on clustered users is proposed with the following procedure: collecting dataset from 4613 users and more than 16 million messages from Sina Weibo, obtaining each user's interest eigenvalue sequence and establishing single-Markov chain model, and implementing user clustering algorithm for the multi-Markov chain construction in order to divide users into a set of predefined interest categories. The proposed solution is capable of predicting both long-term and short-term user interests based on a suitable selection of the initial state distribution, λ. The proposed solution also proves that short-term interests are consistent with long-term interests if the influences of social or user-related events that cause interruptions (e.g., earthquake, birthday, etc.) are not considered. Furthermore, the experiments show that the proposed solution is feasible and efficient and can achieve a higher accuracy of prediction than that of the other approaches such as support vector machine and K-means. The flourishing social networks have greatly enriched the ways of communications and thus brought people in the world much closer than ever. However, critical contexts of the traditional face-to-face communications, for example, body gestures, could be missing during the online communication, hampering the user experiences. The eighth paper, ‘AAH: accurate activity recognition of human beings using WiFi signals’ 9, by Yu Gu, Lianghu Quan, and Fuji Ren, tries to fill in the blank by presenting a passive and device-free activity recognition system through harvesting fingerprints of different activities from ubiquitous WiFi signals. The proposed system can be integrated into any existing wireless local area networks without additional hardware supports. Also, it does not need the subjects to be cooperative during the recognition process. A prototype system is built and evaluated via extensive real-world experiments. By comparing with three state-of-the art solutions, that is, K-nearest neighbor, naive Bayes, and bagging, the superiority of the proposed method is shown in terms of accuracy and complexity. For analyzing the social network, it is important to classify the network traffic and identify the applications running in the network. With the rapid development of smart phones, recent years have witnessed an exponential growth of the number of mobile apps. Considering the security and management issues, network operators need to have a clear visibility into the apps running in the network. The ninth paper, ‘Automatically identifying apps in mobile traffic’ 10, by Lingjun She, Jianhua Sun, Hao Chen, Wenyong Zhong, Cheng Chang, Zhiwen Chen, Wentao Li, and Shuna Yao, presents a novel approach to generating the fingerprints for mobile apps from network traffic. The fingerprints that characterize the unique behaviors of specific mobile apps can be used to identify mobile apps from the real network traffic. In order to handle the large volume of traffic efficiently, the authors use non-negative matrix factorization to perform traffic analysis to cluster similar network traffic into groups. Then, access patterns of individual apps that are extracted from each group can be used as fingerprints, distinguishing apps from others uniquely. The experimental evaluations show that the proposed approach can identify the mobile apps from random and mixed network traffic with high precision. The articles presented in this special issue provide recent advances in some fields related to social network analysis and applications. In particular, the manuscripts undertake research on different topics, including networking, infrastructures, algorithms, applications, and miscellaneous. We hope that the readers can benefit from the perspectives presented in this special issue and will contribute to these strategically important, exciting, and fast-growing research areas. In closing, we would like to thank all the authors who have submitted their research work to this special issue. We would also like to acknowledge the contribution of many experts in the field who have participated in the review process and provided helpful suggestions to the authors on improving the content and presentation of the papers. We would also like to express our gratitude to the editor-in-chief, Prof. Geoffrey C. Fox, for his support and help in bringing forward this special issue. We hope you will enjoy the papers in this collection.

Abstract Radio Frequency Identification (RFID) technology provides a seamless link between physical world and the information system in cyber space. However, the emerging Internet of Things and cloud systems are full of security holes, which introduce new challenging security problems in both tag identification and data privacy. In this paper, we present efficient methods for identity authentication and data encryption to enhance RFID privacy. The proposed techniques aim to ensure no adversary interacting with the tags and the reader, to infer any information on a tag's identity from the communication. The main idea, a symmetric cryptography technique, termed as Advanced Encryption Standard, was applied to implement both mutual authentication and data encryption between the front and back ends of RFID systems. The Advanced Encryption Standard cryptographic algorithm was adopted because of its low hardware complexity and high security strength. In addition, the proposed key management protocol is proved resistant to several known RFID attacks such as Man‐in‐the‐Middle, Denial‐of‐Service, replay, clone attack, and backward/forward traceability. The computational time complexity of the proposed scheme through the entire identity authentication and data encryption phases is 3Tencrypt + 1Tnonce + 4Txor, which is superior to most existing approaches. The proposed scheme was also proved to be able to provide higher data encryption performance than other symmetric cryptographic algorithms with regard to the same level of security strength. Copyright © 2016 John Wiley & Sons, Ltd.

An increasing number of companies are beginning to deploy services/applications in the cloud computing environment. Enhancing the reliability of cloud service has become a critical and challenging research problem. In the cloud computing environment, all resources are commercialized. Therefore, a reliability enhancement approach should not consume too much resource. However, existing approaches cannot achieve the optimal effect because of checkpoint image-sharing neglect, and checkpoint image inaccessibility caused by node crashing. To address this problem, we propose a cloud service reliability enhancement approach for minimizing network and storage resource usage in a cloud data center. In our proposed approach, the identical parts of all virtual machines that provide the same service are checkpointed once as the service checkpoint image, which can be shared by those virtual machines to reduce the storage resource consumption. Then, the remaining checkpoint images only save the modified page. To persistently store the checkpoint image, the checkpoint image storage problem is modeled as an optimization problem. Finally, we present an efficient heuristic algorithm to solve the problem. The algorithm exploits the data center network architecture characteristics and the node failure predicator to minimize network resource usage. To verify the effectiveness of the proposed approach, we extend the renowned cloud simulator Cloudsim and conduct experiments on it. Experimental results based on the extended Cloudsim show that the proposed approach not only guarantees cloud service reliability, but also consumes fewer network and storage resources than other approaches.

Reputation plays an important role for users in choosing or paying for multimedia applications or services. Some efficient multimedia reputation-measurement approaches have been proposed to achieve accurate reputation measurement based on feedback ratings that users give to a multimedia service after invoking. However, the implementation of these approaches suffers from the problems of wide abuse and low utilization of user context. In this article, we study the relationship between user context and feedback ratings according to which one user often gives different feedback ratings to the same multimedia service in different user contexts. We further propose an enhanced user context-aware reputation-measurement approach for multimedia services that is accurate in two senses: (1) Each multimedia service has three reputation values with three different user context levels when its feedback ratings are sufficient and (2) the reputation of a multimedia service with different user context levels is found using user context sensitivity and user similarity when its feedback ratings are limited or not available. Experimental results based on a real-world dataset show that our approach outperforms other approaches in terms of accuracy.

Cloud computing is an innovative service platform to offer diverse resources such as infrastructure, platform and software as services. However, one challenging aspect of such a service is the impatient user threat, which directly leads to numerous negative impacts such as poor throughput, unpredictable workload and waste of resources. In this paper, the problems of conducting system controls in a cost-effective way and simultaneously satisfying performance guarantees are first studied. System losses are analyzed according to the related performance factors and waiting buffer sizes. A cost model is developed to address a performances/cost tradeoff issue in which the user balking, reneging, system blocking and resources provisioning are all taken into account. The relationship between system controls and throughput variations in a multi-servers system with a finite buffer is demonstrated. A proposed policy combined with a heuristic algorithm allows cloud providers to control the service rate and buffer size within a system loss guarantee by solving constrained optimization problems. Simulation results show that more cost-saving and system throughput enhancement can be verified as compared to a system without applying our policy.

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.

With the need of data storage increases tremendously nowadays, distributed file system becomes the most important data storage system in cloud computing. In distributed file system development, there are many researchers work hard to refine the architecture to provide scalability and reliability. In our work, we propose a distributed metaserver system including metaserver scale-out, metadata replication, metaserver recovery, and metaserver management recovery mechanisms. In our experiments, the proposed system can increase the capacity of metadata and increase the reliability by fault tolerance mechanism. The overhead of read/write data is very little in the proposed system as well.

Cloud computing is a new paradigm for delivering remote computing resources through a network. However, achieving an energy-efficiency control and simultaneously satisfying a performance guarantee have become critical issues for cloud providers. In this paper, three power-saving policies are implemented in cloud systems to mitigate server idle power. The challenges of controlling service rates and applying the N-policy to optimize operational cost within a performance guarantee are first studied. A cost function has been developed in which the costs of power consumption, system congestion and server startup are all taken into consideration. The effect of energy-efficiency controls on response times, operating modes and incurred costs are all demonstrated. Our objectives are to find the optimal service rate and mode-switching restriction, so as to minimize cost within a response time guarantee under varying arrival rates. An efficient green control (EGC) algorithm is first proposed for solving constrained optimization problems and making costs/performances tradeoffs in systems with different power-saving policies. Simulation results show that the benefits of reducing operational costs and improving response times can be verified by applying the power-saving policies combined with the proposed algorithm as compared to a typical system under a same performance guarantee.

This paper introduces a prototype of Taiwan UniCloud, a community-driven hybrid cloud platform for academics in Taiwan. The goal is to leverage resources in multiple clouds among different organizations. Each self-managing cloud can join the UniCloud platform to share its resources and simultaneously benefit from other clouds with scale-out capabilities. Accordingly, resources are elastic and sharable with each other such as to afford unexpected resource demands to each cloud. The proposed platform provides a web portal to operate each cloud via a uniform user interface. The construction of virtual clusters with multi-core VMs is supplied for parallel and distributed processing models. An object-based storage system is also delivered to federate different storage providers. This paper not only presents the architectural design of Taiwan UniCloud, but also evaluates the performance to demonstrate the possibility of current implementation. Experimental results show the feasibility of the proposed platform as well as the benefit from the cloud federation.

Data aggregation in wireless sensor networks is widely used to collect data in an energy efficient manner to eliminate redundant data transmission so that prolong the network lifetime. To meet the data aggregation needs in wireless sensor networks, this paper proposes a novel multi-path routing algorithm, called EAD, to process in-network data aggregation. For each sensor on the routing paths, EAD evaluates its neighbors based on the residual energy, deviation angle and distance, and selects the k neighbors with the minimal evaluation costs as its forwarding nodes in order to balance energy consumption of the wireless sensor network on the premise of ensuring the reliability and performance. Simulation results show that EAD can effectively prolong network lifetime, reduce latency and ensure the reliability by adjusting the weight of each influencing factor.

The articles in this special issue focuses on principles, paradigms, and applications of utility computing and its practical realization, especially in the context of cloud computing.

Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is dependent on whichever node is last to complete a task. This problem is exacerbated by heterogeneous environments. In this paper we propose a method to improve MapReduce execution in heterogeneous environments. This is done by dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in order to maximize resource utilization.

With the ever-increasing popularity of the cloud platform, cloud backup scheme attracts more attention from both industry and academia. For cloud providers, profit evaluation of server farms is an important issue of cloud computing economics. Optimal logistic policy should be considered to be an indispensible design simultaneously for providing qualified service to cloud users while the whole cloud center is under construction. To maintain contract-based service quality, the administrator of server farm should adopt the necessary redundancy strategy to provide backup servers when some servers fail. To this aim, focusing upon exploring the optimal profit, two decision parameters of spares and repairmen in the system are considered to maintain regulated service quality for the cloud users. The basic point of our approach is that a novel design approach is developed for evaluating the profit patterns using the finite-source queuing theory. A comprehensive mathematical analysis on profit pattern has been made in detail. Numerical simulation has also been conducted to validate the proposed optimization model. The design illustration is presented to demonstrate engineering application scenario in cloud computing environment, hence the proposed approach indeed provides a feasibly cost-effective design framework to meet logistic economy.

In this paper, we use PCI pass-through technology and make the virtual machines in a virtual environment are able to use the NVIDIA graphics card, which uses the CUDA parallel programming. It makes the virtual machine have not only the virtual CPU but also the real GPU for computing. The performance of virtual machine is predicted to increase dramatically. This paper will measure the performance differences between virtual machines and physical machines by using CUDA; and how virtual machines would verify CPU numbers under influence of CUDA performance. At last, we compare two open source virtualization environment hypervisor, whether it is after PCI pass-through CUDA performance differences or not. Through the experiment, we will be able to know which environment will reach the best efficiency in a virtual environment by using CUDA.

Most previous workflow scheduling research focused on scheduling a single workflow on parallel systems. Recent researches show that utilizing idle time slots between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Stavrinides and Karatza proposed a list scheduling approach to efficient utilization of the idle time slots through bin packing techniques. In this paper, we elaborate on this direction and develop a new approach to further improve multiple workflow scheduling performance through two techniques. The first, in contrast with the list scheduling approach, is clustering the tasks within workflows into groups before allocation. This can reduce inter-task communication cost and thus improve workflow execution performance. The second technique tries to make a balance between tasks' start time and the fitness of idle time slots when allocating task groups. The proposed approach has been evaluated with a series of simulation experiments and compared to the previous method. The results show that our approach outperforms the previous method significantly, up to 51% performance improvement in terms of average makespan.

SUMMARY The auto‐design of electronic circuits for the next generation Information Technology (IT) computing environments is currently one of the most extensively studied issues in the field of evolvable hardware (EHW) architectures. It aims to improve the reliability and fault‐tolerance of hardware systems using embedded techniques. As the scalability of logic circuits becomes larger and more complex nowadays, its auto‐design is more and more difficult. In order to improve the efficiency and the capability of digital circuit auto‐design, in this paper, a multi‐objective simulated annealing (MSA)‐based increasable evolution approach is proposed in an embedded system. First, an extended matrix encoding method is used to indicate the potential performance of a circuit. Therefore, the risk of deleting a circuit with a good developing potential during evolution can be reduced. Second, we consider each output of a digital circuit as an objective, and MSA is designed for digital logic circuits with gradual evolution scheme. In the process of evolution, each objective is evolved in parallel with adaptive mechanism of neighborhood and a performance evaluation. Finally, a framework of online evolution with macro‐blocks is employed to implement MSA on a field‐programmable gate array efficiently and securely. In our experiments, six arithmetic circuits are designed to assess the performance of MSA with gate‐level and function‐level approaches comparing to other algorithms. The comparison results show that our method is very efficient in the auto‐design of EHW. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, more and more researches and applications exploit grid computing systems to deal with high performance computing. However, the mass data transmissions across different grid sites affect overall computing performance. Therefore, grid systems start to integrate with the P2P technology to support the high performance distributed computing. The new distributed computing system is named the P2P Grid computing system. Although the P2P Grid computing system combines the advantages of the grid computing system and the P2P technology, some issues are still needed to be solved. For example, the highly variable resource usage and the heterogeneity of resources could intensely affect the P2P Grid system performance. In this case, the computing performance depends on the resource management policy. Therefore, this study proposes a distributed dynamic load balance policy to manage resources more effectively and to further improve the resource utilization. The prototype is implemented on the sites of the Taiwan Uni Grid, and the P2P grid sites exchange information by JXTA advertisements. Experimental results show that the proposed algorithm could efficiently distribute the workload for execution, that is, it not only can minimize the job execution time, but also maximize the resource utilization.

Task consolidation is a way of maximizing cloud computing resource, which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy usage. Many literature show that energy consumption and resource utilization in clouds are highly coupled. Some research works aim to decrease resource utilization for saving energy while some try to find the balance between resource utilization and energy consumption. In this paper, an energy-aware task consolidation (ETC) technique is presented aims to optimize energy consumption of virtual clusters in cloud data center. Conforming most cloud systems, a 70% principle of CPU utilization is proposed to manage task consolidation among virtual clusters. The simulation results show that ETC can significantly reduce power consumption in managing task consolidation for cloud systems. Up to 17% improvement as compare to a recent work in [10] that aims to maximize resource utilization can be obtained.

Virtualization is a common strategy to improve the existing computing resources, particularly in cloud computing field. Hadoop, one of Apache projects, is designed to scale up from single servers to thousands of machines, and each offer local computation and storage. However, how to guarantee stability and reliability have become great study topics. In this article, we use current open-source based on software and platform to reach our goal. For instance, Xen-Hyper visor virtualization technology, Open Nebula virtual machines management tool, and so on. After extending component capabilities, we developed a mechanism to support our idea and reached Hadoop High Availability which called Virtualization Fault Tolerance (VFT). We consider a practical problem that occurs frequently in our system, and the results in this paper also confirm the downtime time can be shortened if failure occurred. In this case, it is not only for the Hadoop applications, but also extended to more areas of cluster-based systems.

The robust availability of resources in distributed computing environments is a very important issue. In general, the resources are distributed among geographical distributed sites resulting in the higher failure probability. Therefore, this paper proposes a fault tolerance policy on dynamic load balancing in P2P grids to improve the dynamic availability of resources. This proposed policy duplicates jobs in different computing nodes to avoid job or hardware failure. In the meantime, the proposed fault tolerance policy also considers the load balancing among different computing nodes while keeping the stable job turnaround time. Therefore, the proposed policy could improve the system performance in a varying environment. Experimental results show that the proposed policy could achieve a better job completion rate as the failure rate increases.

This special issue includes four extended versions of selected papers originally presented at the IEEE Asia Pacific Services Computing Conference held in Yilan, Taiwan. The papers selected for this issue not only contribute valuable insights and results, but also have particular relevance to services composition, services management, services query, and scheduling for cloud, grid, and pervasive systems. They all present high quality results for tackling problems arising from the ever-growing fields of services and cloud computing. These are briefly reviewed.

The worldwide Digital Signage market has been getting increasingly popular in recent years. Nevertheless, for service providers, the Digital Signage business is still not easy to manage and time-consuming to operate. In this paper, based on the GPS and wireless infrastructure, we present a mobile location-aware digital signage system (LDSS). Through the centralized system architecture, the applications for the Digital Signage can be expanded to various advertising vehicles including buses and other mobile advertising vehicles. Also, we present an advertisement recommendation algorithm, by which the advertisement can be broadcasted in the right markets more effectively. Evidenced by simulated experiments and analysis, compared with traditional region trigger advertising, sequential advertising and random advertising, our advertisement recommendation algorithm can effectively reach to the key target audiences in planned regions, while show excellent ability to differentiate markets in unplanned regions reaching to the target consumer groups as well.

Abstract Data Grids enable the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for content needed by large‐scale data‐intensive applications such as high‐energy physics, bioinformatics, and virtual astrophysical observatories. In Data Grids, co‐allocation architectures were developed to enable parallel downloads of data sets from selected replica servers. As Internet is usually the underlying network of a grid, network bandwidth plays as the main factor affecting file transfers between clients and servers. In this paradigm, there are still some challenges that need to be solved, such as to reduce differences in finish times between selected replica servers, to avoid traffic congestion resulting from transferring the same blocks in different links among servers and clients, and to manage network performance variations among parallel transfers. In this paper, we propose theAnticipative Recursively Adjusting Mechanism(ARAM) scheme to adjust the workloads on selected replica servers and handle unpredictable variations in network performance by those servers. Our algorithm is based on using the finish rates for previously assigned transfers to anticipate the bandwidth status for the next section to adjust workloads, and to reduce file transfer times in grid environments. Our approach is useful in grid environments with unstable network link. It not only reduces idle time wasted waiting for the slowest server, but also decreases file transfer completion times. Copyright © 2010 John Wiley & Sons, Ltd.

The large sized data sets are replicated in more than one site for the better availability to the nodes in a grid. Downloading the dataset from these replicated locations have practical difficulties, due to network traffic, congestion, frequent change-in performance of the servers, etc. In order to speed up the download, complex server selection techniques, network and server loads are used. However, consistent performance is not guaranteed due to the shared nature of network links of the load on them, which can vary unpredictably. In this paper, we present a bandwidth sensitive co-allocation scheme for parallel downloading in grid economics. Objective of the proposed technique aims to service grid applications efficiently and economically in data grids. With the consideration of cost factor, we present a novel mechanism for server selection, dynamic file decomposition and co-allocation. Under considerations in costs, our mechanism for selections of servers with various techniques combined is able to significantly attenuate economic costs. We compared our scheme with the existing schemes and the preliminary results show notable improvement in overall completion time of data transfer.

The problem of redundant RFID reader elimination has instigated researchers to propose different optimization heuristics due to the rapid advance of technologies in large scale RFID systems. In this paper, we present a layered elimination optimization (LEO) which is an algorithm independent technique aims to detect maximum amount of redundant readers could be safely removed or turned off with preserving original RFID network coverage. A significant improvement of the LEO scheme is that number of "write-to-tag" operations could be largely reduced during the redundant reader identification phase. Moreover, LEO is a distributed scheme which does not need to collect global information for centralizing control, leading no communications and synchronizations among RFID readers. To evaluate the performance of the proposed techniques, we have implemented the LEO technique along with another redundant reader identification algorithm and other hybrid schemes. In experimental results, the LEO is shown to be effective and provides superior performance in terms of larger number of redundant reader could be detected and with lower algorithm overheads.

In recent years, grid technology, which is frequently used to solve the scientific problem, has ripened gradually. A large number of storage resources and computational power are combined to form a data grid network to deal with massive data of scientific experiments. Data replication generates huge data which are distributed in wide-area for researches around the globe. The files of grid environments can be modified by grid users might bring a critical problem of maintaining data consistency among the several replicas distributed in different machines. For that reason how to maintain the consistency of those files is a great challenge. In this paper, we propose a Oneway Replica Consistency model in data grid environments, which is used for consistency maintenance issue. Furthermore, we anticipate striking a balance between improving data access performance and replica consistency in data grids. This work can find out the more efficient ways of utilizing storage space is an important point.

This paper explores the tracers deployment problem for IP traceback methods how many and where the tracers should be deployed in the network to be effective for locating the attack origins. The minimizing the number of tracers deployment problems depended on locating the attack origins are defined. The problem is proved to be NP-complete. A heuristic method which can guarantee that the distance between any attack origin and its first met tracer be within an assigned distance is proposed. The upper bound for the probability of an undetected attack node can be calculated in advance and used to evaluate the number of tracers needed for the proposed heuristic method. Extended simulations are performed to study the performance of the tracers deployment.

Dynamic data redistribution is used to enhance data locality and algorithm performance by reducing interprocessor communication in many parallel scientific applications on distributed memory multicomputers. Since the redistribution is performed at runtime, there is a performance tradeoff between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a processor replacement scheme to minimize the cost of interprocessor data exchange during runtime. The main idea of the proposed technique is to develop a replacement function for reordering logical processors in the destination phase. Based on the replacement function, a realigned sequence of destination processors can be derived and is then used to perform data decomposition in the receiving phase. Together with local matrix and compressed CRS vectors transposition schemes, the interprocessor communication can be eliminated during runtime. A significant improvement of this approach is that the realignment of data can be performed without interprocessor communication for special cases. The second contribution of the present technique is that the complicated communication sets generation could be simplified by applying local matrix transposition. Consequently, the indexing cost could be reduced significantly. The proposed techniques can be applied in both dense and sparse applications. A generalized symmetric redistribution algorithm is also presented in this work. To analyze the efficiency of the proposed technique, the theoretical analysis proves that up to (p-1)/p data transmission cost can be saved. For general cases, the symmetric redistribution algorithm saves 1/p communication overheads compared with the traditional method. Experimental results also show that the proposed techniques provide superior performance in most data redistribution instances.

In many scientific applications, array redistribution is usually required to enhance data locality and reduce remote memory access in many parallel programs on distributed memory multicomputers. Since the redistribution is performed at runtime, there is a performance trade-off between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a generalized processor mapping technique to minimize the amount of data exchange for BLOCK-CYCLIC(kr) to BLOCK-CYCLIC(r) array redistribution and vice versa. The main idea of the generalized processor mapping technique is first to develop mapping functions for computing a new rank of each destination processor. Based on the mapping functions, a new logical sequence of destination processors can be derived. The new logical processor sequence is then used to minimize the amount of data exchange in a redistribution. The generalized processor mapping technique can handle array redistribution with arbitrary source and destination processor sets and can be applied to multidimensional array redistribution. We present a theoretical model to analyze the performance improvement of the generalized processor mapping technique. To evaluate the performance of the proposed technique, we have implemented the generalized processor mapping technique on an IBM SP2 parallel machine. The experimental results show that the generalized processor mapping technique can provide performance improvement over a wide range of redistribution problems.

In many scientific applications, dynamic array redistribution is usually required to enhance the performance of an algorithm. In this paper, we present a generalized basic-cycle calculation (GBCC) method to efficiently perform a BLOCK-CYCLIC(s) over P processors to BLOCK-CYCLIC(t) over Q processors array redistribution. In the GBCC method, a processor first computes the source/destination processor/data sets of array elements in the first generalized basic-cycle of the local array it owns. A generalized basic-cycle is defined as lcm(sP, tQ)/(gcd(s,t)/spl times/P) in the source distribution and lcm(sP, tQ)/(gcd(s,t)/spl times/Q) in the destination distribution. From the source/destination processor/data sets of array elements in the first generalized basic-cycle, we can construct packing/unpacking pattern tables to minimize the data-movement operations. Since each generalized basic-cycle has the same communication pattern, based on the packing/unpacking pattern tables, a processor can pack/unpack array elements efficiently. To evaluate the performance of the GBCC method, we have implemented this method on an IBM SP2 parallel machine, along with the PITFALLS method and the ScaLAPACK method. The cost models for these three methods are also presented. The experimental results show that the GBCC method outperforms the PITFALLS method and the ScaLAPACK method for all test samples. A brief description of the extension of the GBCC method to multidimensional array redistributions is also presented.

In many scientific applications, dynamic array redistribution is usually required to enhance the performance of an algorithm. We present a generalized basic cycle calculation (GBCC) method to efficiently perform a BLOCK-CYCLIC(s) over P processors to BLOCK-CYCLIC(t) over Q processors array redistribution. In the GBCC method, a processor first computes the source/destination processor/data sets of array elements in the first generalized basic cycle of the local array it owns. A generalized basic cycle is defined as lcm(sP,tQ)/(gcd(s,t)/spl times/P) in the source distribution and lcm(sP,tQ)/(gcd(s,t)/spl times/Q) in the destination distribution. From the source/destination processor/data sets of array elements in the first generalized basic cycle, we can construct packing/unpacking pattern tables. Based on the packing/unpacking pattern tables, a processor can pack/unpack array elements efficiently. To evaluate the performance of the GBCC method, we have implemented this method on an IBM SP2 parallel machine, along with the PITFALLS method and the ScaLAPACK method. The cost models for these three methods are also presented. The experimental results show that the GBCC method outperforms the PITFALLS method and the ScaLAPACK method for all test samples. A brief description of the extension of the GBCC method to multi dimensional array redistributions is also presented.

Array redistribution is usually required to enhance algorithm performance in many parallel programs on distributed memory multicomputers. Since it is performed at run-time, there is a performance trade-off between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a basic-cycle calculation technique to efficiently perform BLOCK-CYCLIC(S) to BLOCK-CYCLIC(t) redistribution. The main idea of the basic-cycle calculation technique is, first, to develop closed forms for computing source/destination processors of some specific array elements in a basic-cycle, which is defined as icm(s,t)/gcd(s,t). These closed forms are then used to efficiently determine the communication sets of a basic-cycle. From the source/destination processor/data sets of a basic-cycle, we can efficiently perform a BLOCK-CYCLIC(s) to BLOCK-CYCLIC(t) redistribution. To evaluate the performance of the basic-cycle calculation technique, we have implemented this technique on an IBM SP2 parallel machine, along with the PITFALLS method and the multiphase method. The cost models for these three methods are also presented. The experimental results show that the basic-cycle calculation technique outperforms the PITFALLS method and the multiphase method for most test samples.

Array redistribution is usually required to enhance algorithm performance in many parallel programs on distributed memory multicomputers. Since it is performed at run-time, there is performance tradeoff between the efficiency of new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. We present efficient algorithms for array redistribution. The most significant improvement of our algorithms is that a processor does not need to construct the send/receive data sets for a redistribution. Based on the packing/unpacking information that derived from the BLOCK-CYCLIC(kr) to BLOCK-CYCLIC(r) redistribution (or vice versa), a processor can pack/unpack array elements into (from) messages directly. To evaluate the performance of our methods, we have implemented our methods along with Thakur's (1994) methods on an IBM SP2 parallel machine. The results show that the execution time of our algorithms is approximately 5% to 27% faster than that of Thakur's methods.

In this paper, we present message encoding techniques to improve the performance of BLOCK-CYCLIC(kr) to BLOCK-CYCLIC(r) (and vice versa) array redistribution algorithms. The message encoding techniques are machine independent and could be used with different algorithms. By incorporating the techniques in array redistribution algorithms, one can reduce the computation overheads and improve the overall performance of array redistribution algorithms. To evaluate the performance of the techniques, we have implemented the message encoding techniques into some array redistribution algorithms on an IBM SP2 parallel machine. The experimental results show that the execution time of array redistribution algorithms with the message encoding techniques is 3% to 22% faster than those without the message encoding techniques.