Millions of sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the Internet Things (IoT). However, IoT devices are limited computational power, including storage, processing, communication resources, effectively perform compute-intensive tasks locally. Edge computing resolves resource limitation problems by bringing computation closer edge devices. Providing distributed nodes across network reduces stress centralized overcomes...

Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in Internet Things are offloaded to (ECDs) for implementation. However, ECDs often overloaded or underloaded with disproportionate resource requests. In addition, during process task offloading, transmitted information is vulnerable, which can result data incompleteness. view this challenge, a blockchain-enabled computation offloading method, named BeCome, proposed article....

Complementary to the fancy big data applications, networking for is an indispensable supporting platform these applications in practice. This emerging research branch has gained extensive attention from both academia and industry recent years. In this new territory, researchers are facing many unprecedented theoretical practical challenges. We therefore motivated solicit latest works area, aiming pave a comprehensive solid starting ground interested readers. first clarify definition of based...

To maximize the economic benefits, a cloud service provider needs to recommend its services as many users possible based on historical user-service quality data. However, when platform (e.g., Amazon) intends make recommendation decision, considering only own data is insufficient, because user may invoke from multiple distributed platforms Amazon and IBM). In this situation, it promising for collaborate with other IBM) utilize integrated improve accuracy. two challenges are present in...

Fog computing is emerging as a powerful and popular paradigm to perform IoT (Internet of Things) applications, which an extension the cloud make it possible execute applications in network edge. The could choose fog or nodes for responding resource requirements, load balancing one key factors achieve efficiency avoid bottlenecks, overload, low load. However, still challenge realize balance environment during execution applications. In view this challenge, dynamic allocation method, named...

The increasing number of web APIs registered in service sharing communities (e.g., http://ProgrammableWeb.com that provides a platform benefiting the social interactions between different software developers) has provided promising way to quickly build various apps with diverse functions. Generally, an app developer can manually discover, select, and compose set appropriate new satisfying developer's functional nonfunctional business requirements, economically conveniently. However, above...

Using Web APIs registered in service sharing communities for mobile APP development can not only reduce period and cost, but also fully reuse state-of-the-art research outcomes broad domain so as to ensure up-to-date applications. However, the big volume of available well their differences make it difficult selection considering compatibility, preferred partial expected functions which are often high variety. Accordingly, how recommend a set functional-satisfactory compatibility-optimal...

The development of the Internet vehicles (IoV) has spawned a series driving assistance services (e.g., collision warning), which improves safety and intelligence transportation. In IoV, need to be met in time due rapid speed vehicles. By introducing edge computing into insufficiency local computation resources is improved, providing high quality for users. Nevertheless, provided by servers are often limited, fail meet all needs users IoV simultaneously. Thereby, how minimize tasks processing...

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands processing various contents (e.g., videos). Generally, efficient delivery, the from providers are processed on edge servers (ESs), as computing offers vehicular low-latency services. However, due to reusability same required by different distributed users, copies repeatedly in an server leads a waste resources storage, computation, and bandwidth) ESs. Therefore, it is...

Currently, the world is experiencing rapid spread of Coronavirus Disease 2019 (COVID-19). Since epidemic continues to take a devastating impact on society, economy, and healthcare, real-time detection COVID-19 essential for fast cost-effective diagnosis services. Fortunately, deep learning (DL), as promising technology, enables services chest X-ray (CXR) images. The training task DL model generally implemented at centralized cloud. However, due geo-distributed data sources transmission large...

Web3, an emerging blockchain-based decentralized network, grants users ownership and enhances the collaboration among devices under monitoring. Benefiting from decentralization in-memory computing, vehicular edge networks can process tasks such as road object detection distributedly without being attacked. Recently, to provide intelligent service for Web3 users, artificial intelligence applications have been booming, thus generating enormous deep learning models. These models are supposed be...

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending new known as the user cold-start problem. Some approaches incorporate social networks deduce preferences based circles of users solve problem sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in behaviors, leading inaccuracies predicting preferences. To address aforementioned issues, we...

The world of industrial automation technology is at the outset a new era innovation with hype Industry 4.0. Global modern system converges power machines, computing, analytics, connectivity, cyber-physical systems, Internet things, automation, cloud and data exchange. 4.0 revolution towards digital factories smart products. Big an integration multi-disciplinary technologies facilitates customer by bringing incredible services to click. things connected machines adding communication...

Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, amount of customers, services and online information has grown rapidly, yielding big data analysis problem service systems. Consequently, traditional often suffer from scalability inefficiency problems when processing or analysing such large-scale data. Moreover, most existing present same ratings rankings different users without considering diverse users'...

Crowdsourcing is a booming technique that enables participants to exchange data directly, thus making it possible answer latency-sensitive service requests and relieve the burden of core networks. With some incentives, providers compete furnish requests, pledging quality experience (QoE) for requestors. However, decentralized communication in crowdsourcing increases probability information tapering. Furthermore, providers' arbitrary selection poses great threat efficient profitable provision...

Scientific workflows are often deployed across multiple cloud computing platforms due to their large-scale characteristic. This can be technically achieved by expanding a platform. However, it is still challenge conduct scientific workflow executions in an energy-aware fashion or even inside platform, since the platform expansion will make energy consumption big concern. In this paper, we propose Energy-aware Resource Allocation method, named EnReal, address above challenge. Basically,...

Recommender systems are a promising way for users to quickly find the valuable information that they interested in from massive data. Concretely, by capturing user's personalized preferences, recommender system can return list of recommended items best match user preferences using collaborative filtering. However, big data environment, heavily fragmented distribution QoS (Quality Services) recommendation decision- making presents large challenge when integrating different platforms while...

Cloud computing promises a scalable infrastructure for processing big data applications such as medical analysis. Cross-cloud service composition provides concrete approach capable large-scale processing. However, the complexity of potential compositions cloud services calls new and aggregation methods, especially when some private clouds refuse to disclose all details their transaction records due business privacy concerns in cross-cloud scenarios. Moreover, credibility cross-clouds on-line...

Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, services, with complex interdependent logics, are modeled workflows. When any nodes hosting workflows fail, all sorts consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering failed tasks well optimizing and load balance still critical challenge. To address this...

In big data applications, privacy is one of the most concerned issues because processing large-scale privacy-sensitive sets often requires computation resources provisioned by public cloud services. Sub-tree anonymization a widely adopted scheme to anonymize for preservation. Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways fulfill sub-tree anonymization. However, existing approaches fall short parallelization capability, thereby lacking scalability in handling...

With the increasing volume of web services in cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one most effective techniques to alleviate heavy burden on selection decisions a target user. However, bases, that is, historical usage data, are often distributed different platforms. Two challenges present such cross-cloud scenario. First, platform is not willing share its data other platforms due privacy concerns, which decreases feasibility severely....

Cloud computing provides promising scalable IT infrastructure to support various processing of a variety big data applications in sectors such as healthcare and business. Data sets like electronic health records often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared third-parties cloud. A practical widely-adopted technique for preservation anonymize via generalization satisfy given model. However, most existing...