Logs are one of the most valuable data to describe the running state of services. Failure diagnosis through logs is crucial for service reliability and security. The current automatic log failure diagnosis methods cannot fully use the multiple fields of logs, which fail to capture the relation between them. In this article, we propose LogKG, a new framework for diagnosing failures based on knowledge

In cloud-edge computing paradigms, the integration of edge servers and task offloading mechanisms has posed new challenges to developing task scheduling strategies. This paper proposes an efficient convergent firefly algorithm (ECFA) for scheduling security-critical tasks onto edge servers and the cloud datacenter. The proposed ECFA uses a probability-based mapping operator to convert an individual

Crowdsourcing for healthcare, which is an application of crowd intelligence, has become a novel and important auxiliary way for traditional healthcare, showing a huge application perspective. In a crowdsourcing platform for healthcare, patients can act as requesters who recruit workers, such as doctors, to provide professional advice by posting a task. However, privacy concerns pose a significant obstacle

The recent expansion of mobile IoT devices (MIoTDs) along with the exposure of many compute-intensive and latency-critical applications, have given a step rise to the mobile edge computing (MEC) platform to process computational microservices at the edge. The paramount importance of designing an effective incentive mechanism is a very important topic for such systems to get a fair amount of resources

Based on sensor networks within a dynamic and real-time environment, a novel parking-lot navigation method is proposed based on diffusion equation and Poisson equation to achieve convenient and efficient navigation process with edge computing mind to aid information query and navigation. From the perspective of theoretical proof, is presented parallel method mainly for ordinary differential equations

Microservice-based applications may include multiple instances of microservices running on containerised infrastructures. These infrastructures pose challenges to digital investigations of security incidents because digital evidence can be destroyed when containers are terminated. Observability techniques are used to facilitate the investigation of incidents in microservice systems. However, existing

Running a typical Bitcoin client (also called full node) needs more than 444 GB of disk space, considerable time, and computational resources to synchronize the entire blockchain, which is infeasible for resource-constrained devices. To address such concerns, the lightweight Bitcoin client proposed by Satoshi outsources most of computational and storage burdens to full nodes. Unfortunately, interacting

Electronic Medical Records (EMRs) sharing enhances healthcare and biomedical discoveries but faces challenges: data provider centralization and limited interoperability. Blockchain can address these issues, but existing systems struggle with malicious EMR propagation due to challenges concerning the authenticity, non-repudiation, and integrity of the digital signatures they employ. Universal Designated

Edge computing and container technologies offer more possibilities for the development of Internet of Vehicles (IOV). However, many studies have neglected the dependencies among containers and the mobility of users. In this article, we propose a container migration strategy based on trajectory prediction, with the consideration of dependencies among containers. Given a set of containers with dependencies

The primary concern of numerous online social media network (SMN) platforms is how to provide users with effective and personalized web services. To achieve this goal, SMN platforms typically begin by collecting user preferences based on user behaviors (e.g., browsing history, posts) or user profiles. However, the effective information about a specific user on a single SMN platform is limited and monotonous

Streaming logs provide valuable information for complex systems in diagnosing system faults or conducting security analysis. Although the log sequence anomaly detection has drawn more and more attention and achieved a satisfactory performance, it remains an extremely difficult task because of several intrinsic challenges including new event occurrences in a continuously evolving environment, making

Spatial keyword queries have attracted much attention over the past decade due to the popularity of location-based services and social networks, which brings great economic benefits. Geo-textual data are encrypted-and-delegated to public clouds for efficient management and utilization while preventing potential data leakage. However, it is still challenging to solve secure ra nge s patial k eyword

The rapid growth in the number and diversity of Web service, coupled with the myriad of similar Web service in functionality, makes it challenging to find most suitable Web service for users to accelerate and accomplish Mashup development. Therefore, this article proposes a Web service recommendation method via integrating heterogeneous graph attention network representation and FiBiNET (Feature Importance

Recently, releasing data to a third party for secondary analysis has become a trend of service computing. However, data owners are concerned that such a move may expose individuals’ records, which is in violation of regulations such as the European Union's General Data Protection Regulation. Differential privacy has been proposed as a possible solution to the aforementioned problem. The privacy budget

While recommender systems have been ubiquitously used in digital marketing and online business development, the conversions of online advertising for mobile apps installation and activation sometimes are far from satisfactory, due to the lack of feedback from App-related activities, leading to a poor record of Return on Investment (RoI). Though the advertisers, e.g., App operators and App Store, are

Most cloud providers offer Deep Learning as a Service (DLaaS) for different business, science and engineering domains. However, it is known that deep neural networks (DNNs) are vulnerable to adversarial examples, which can cause well-trained DNN models to misbehave by injecting human-imperceptible perturbations to the query input data. Securing deep learning as a service becomes a critical challenge

Scheduling workflows in cloud environments is an important issue that many types of research have been conducted in this field. However, these approaches often focus on single workflow scheduling while the need for scheduling multiple workflows is growing. This study aims at presenting a cloud broker for executing Deadline-constrained Periodic scientific Workflows (BDPW). BDPW acts as a Workflow as

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity

Traditional insurance policies pay the insurer according to the actual loss or damage to a tangible object that is insured. Due to the numerous parties involved, the settlement procedure is usually highly drawn out. Comparatively, parametric insurance protects a policyholder from the occurrence of a particular event by disbursing a predetermined sum dependent on the severity of the event. In the transportation

Ethereum leverages ECDSA as the digital signature scheme to validate transactions. From the provable security standpoint, ECDSA built on an 80-bit security Elliptic Curve group can achieve at most 50-bit concrete security, rather than 80-bit security, due to its reduction loss for $2^{30}$ signature queries in security analysis. The state-of-the-art ECDSA scheme comes with no de facto formal security

Next location recommendation services play a pivotal role in Location-Based Social Networks (LBSNs) due to their ability to provide personalized recommendations of attractive destinations, resulting in substantial benefits for both users and service providers. Recent research indicates that these services are influenced by both sequential and geographical factors. However, we argue that most of these

Detecting anomalies in logs is crucial for service and system management, since logs are widely used to record the runtime status, and are often the only data available for postmortem analysis. Since anomalies are usually rare in real-world services and systems, a common and feasible practice is to mine or learn normal patterns from logs, and deem those violating the normal patterns as anomalies. As

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation

Threshold attribute-based credentials are suitable for decentralized systems such as blockchains as such systems generally assume that authenticity, confidentiality, and availability can still be guaranteed in the presence of a threshold number of dishonest or faulty nodes. Coconut (NDSS’19) was the first selective disclosure attribute-based credentials scheme supporting threshold issuance. However

In today's complex Internet platform, online users need help to protect their online identity. Only sometimes, websites are very transparent about how user data will be collected, stored and processed by them. Sometimes Internet entities collect more online user information than required. These entities often share user identity-related data with third parties without consent. Existing traditional

Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VFL, which builds an ensemble of weak learners (typically decision trees) to achieve promising prediction performance. Recently there have been growing interests

Serverless Function-as-a-Service (FaaS) is a rapidly growing computing paradigm in the cloud era. To provide rapid service response and save network bandwidth, traditional cloud-based FaaS platforms have been extended to the edge. However, launching functions in a heterogeneous computing continuum (HCC) that includes the cloud, fog, and the edge brings new challenges: determining where functions should

The Social Internet of Things (SIoT) is a novel network that integrates social relations among objects and facilitates the interconnection between humans and smart devices through the Internet of Things (IoT). However, with the growing number of users and their devices in SIoT, ensuring high quality of service (QoS) and trust relations among users poses significant challenges. Therefore, this article

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location

Patient-centered data management and sharing of personal health records (PHRs) are difficult to be realized as data is controlled by doctors/hospitals. In addition, security and privacy, oppressive costs, search and tracing unreliability, and complicated access authorization caused by traditional encryption severely hinder the widespread adoption of PHRs. To overcome these challenges, we propose a

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications’ power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model

Automated log analysis can facilitate failure diagnosis for developers and operators using a large volume of logs. Log parsing is a prerequisite step for automated log analysis, which parses semi-structured logs into structured logs. However, existing parsers are difficult to apply to software-intensive systems, due to their unstable parsing accuracy on various software. Although neural network-based

Cloud storage services improve the efficiency and popularity of data sharing. These services allow groups of participants to jointly maintain the shared data. As important security properties of cloud data sharing, the integrity and the reliability of the shared data have been studied recently. However, the existing research cannot sufficiently solve the issue of participant identity anonymity in the

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing

User interaction history with items is used to infer user preferences in conventional recommendation systems. Among these, conversational recommendation systems (CRSs), which provide effective recommendations based on a framework combining recommendations and conversations with users, have been proposed. However, existing CRS model still have some shortcomings, such as the lack of using dialogue records

Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering

Authorization-related security requirements are of great significance in workflow management systems. Existing studies are restricted in their scopes of research. There is no unified principle for their implementation. In this paper, we focus on the unification of authorization-related security requirements using Petri nets (PNs). These security requirements are expressed by constraints, being imposed

Usage-based pricing or Pay-as-You-Go is a relatively new SaaS business model that may provide customers the option to pay for only what they use. Yet, it is more challenging to implement than traditional Pay-before-Use subscriptions considering that it need not only realize financial payment on consumption-based behaviors, but also regulate the rights and obligations among the providers, consumers

Software defined networking (SDN), a highly regarded architecture, enhances the programmability and manageability of the network by decoupling the data plane and the control plane. It has emerged to bring more possibilities to the Internet, but at the same time, its inherent shortcomings have become a pool for malicious attackers. Low-rate denial of service (LDoS) attacks, a variant of denial of service

Metric learning as a service (MLaaS) represents one of the main learning streams to handle complex datasets in service computing research communities and industries. A common approach for dealing with high-dimensional and complex datasets is employing a feature embedding algorithm to compress data through dimension reduction while optimizing intra-class distance. To create generalizable MLaaS for high-performance

When dealing with distributed applications in Edge or Fog computing environments, the service latency that the user experiences at a given node can be considered an indicator of how much the node itself is loaded with respect to the others. Indeed, only considering the average CPU time or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters

Currently, various encryption techniques have been employed to protect the documents in cloud storage. In particular, attribute-based keyword search (ABKS) is a practical encryption primitive that can realize fine-grained access control and keyword based searching over encrypted documents. However, the search time in most of the existing ABKS schemes increases linearly with the size of document collection

The rapid proliferation of new web services over the last decade has led to an increase in functionally identical services, making the service selection system more challenging. In this work, we propose a web service selection model consisting of two layers, which we call CPSky. The upper layer, called the Prefilter layer, filters and allows only potent services to participate in the selection process

The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the

Secure queries are fundamental to data security, particularly in cloud databases. In data analytics, one of the common and practical queries is the iceberg query that can find aggregate values above a specified threshold. However, existing secure aggregate query schemes: 1) are unable to support secure iceberg queries equipped with the HAVING clause; 2) only consider additive aggregate functions; and

Reliable data storage is crucial to the production, transmission, transaction, consumption, and analysis of an Energy Internet (EI). Whereas mainstream distributed data storage seems to be a plausible solution, the existing methods suffer from a tradeoff between the storage overhead (incurred by the replicas of data encodings for lossless recovery) and the communication latency (due to the spiking

In process-aware information systems (PAISs), anomalies are ubiquitous, having a number of different underlying causes, such as software malfunctions or operator errors. The presence of anomalies not only has an enormous impact on the economic well-being of the business, but also interferes with our ability to mine useful information from event logs. In this article, we propose GRASPED, a GR U- A E

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users’ data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not

With the widespread deployment of secure outsourcing computation, the resource-constrained client can delegate intensive computation tasks to powerful servers. Matrix determinant computation is a fundamental mathematical operation that has been widely used in IoT applications. This operation is computationally expensive. Nevertheless, the existing secure outsourcing protocols for matrix determinant

The replication-Based Outsourced Computation (RBOC) mechanism allows a client to outsource the same computing job to multiple contractors and the honest contractors will get paid in the incentivized system based on the fact that a majority of contractors will honestly perform the computation. As self-executing contracts, smart contracts are utilized in the decentralized blockchain networks to execute

With the development of Industrial Internet of Things (IIoT) and 5G, massive data are easily collected and transmitted in cloud. Therefore, it is critical to guarantee the security of data sharing. In IIoT applications, the users of a group are in hierarchical structure and they intend to access data by external groups, which requires fine-grained access control, data authenticity and data retrieval

As the footing stone of artificial intelligence (AI), ubiquitous computing resource is beginning to receive interest. With this trend, a new form of edge-cloud service market dedicated to collecting, trading, and scheduling computing resources is rising. The computing participants in the service market, as providers and demanders of computing resources, are becoming more diversified and open. As such

With the rapid development of manufacturing and cloud computing, more and more emerged cloud services provide a promising way to perform complex requirements efficiently. Workflow offers an effective way to assemble disparate services and interacts with them to construct business logic for user requests. Meanwhile, workflow engines are responsible for the control of workflow execution. However, existing

The Internet of Things (IoT) boom has enabled Internet Service Providers (ISPs) to collect an enormous amount of high-dimensional data. Performing range queries on such data can effectively reuse them to help ISPs offer better services. Owing to the low cost and high resource utilization of cloud computing, an increasing number of ISPs are inclined to outsource data and services to it. However, as

With the popularity of social networks and the increase of cloud platform applications, service computing has also developed. Therefore, the protection of information even privacy uploaded to the cloud server has become critical. Recently, some researchers have proposed encryption schemes of visual meaningful image by using compressive sensing. However, these schemes generally cannot hide the large-size

Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (QoE). We allocate transcoding tasks to edge-servers in a multiple-access edge-computing (MEC) architecture, taking into account server capacity, wireless network coverage, and the cost

With the increasing popularity and wide application of the Internet, the users (such as managers and data consumers) in the Industrial Internet of Things (IIoT) can remotely analyze and control real-time data collected by various smart sensor devices. However, there are many security and privacy issues in the process of transmitting collected data through public channels in IIoT environment. In order

Real-time social interactions and multi-streaming are two critical features of live streaming services . In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ) , that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove

D ynamic W orkflow S cheduling in F og C omputing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings