Search

Applications deployed on edge servers improve users experience, when compared to deployments on cloud servers. Existing works usually assume that a central scheduler helps in making decisions, but they are often inefficient, inaccurate, or time-consuming. In this paper, we present a proactive application deployment system, which consists of three modules (i.e., incentive, profit, and latency). Based on the architecture of a fully distributed edge network, our system includes SELL, a Spontaneous Edge depLoyment aLgorithm in the incentive module. SELL lets edge servers compete with each other in a two-stage Stackelberg game to win deployment rights, and the winners get paid for their deployment efforts. The other two modules help recursively adjust service prices and deployment intentions in view of their own profits. Simulations on the proactive edge application deployment system demonstrate that SELL can help an application provider find appropriate edge servers to deploy applications while maximizing the profits for both parties in a low latency.

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MULTISCALER, to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MULTISCALER is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MULTISCALER approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MULTISCALER produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

The rise of 5G technology has driven the development of edge computing. Computation offloading is the key and challenging point in edge computing, which investigates offloading resource-intensive computing tasks from the user side to the cloud or edge side for processing. More consideration needs to be given to load balancing, user variability, and the heterogeneity of edge facilities in relevant research. In addition, most of the research around edge collaboration also revolves around cloud-side collaboration, which pays relatively little attention to the collaboration process between edge nodes, and the incentive and trust issues of the collaboration process need to be addressed. In this paper, we consider the impact of the user demand variability and the edge facility heterogeneity, then propose a method based on Vickrey-Clarke-Groves (VCG) auction theory to accommodate the edge demand response (EDR) process where the number of users and service facilities do not match. The method makes users’ bidding rules satisfy the Nash equilibrium and weakly dominant strategy, which can improve the load balancing of edge nodes, has positive significance in improving the edge resource utilization and reducing the system energy consumption. In particular, combined with blockchain, we further optimize the incentive and trust mechanism of edge collaboration and consider three scenarios: no collaboration, internal collaboration, and incentive collaboration. We also consider the impact of the user task’s transmission distance on the quality of experience (QoE). In addition, we illustrate the possible forking attack of blockchain in collaborative edge computing and propose a solution. We test the performance of the proposed algorithm on a real-world dataset, and the experimental results verify the algorithm’s effectiveness and the edge collaboration’s necessity.

Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA$$^3$$ 3 D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user’s historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user’s next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user’s short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user’s check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user’s long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user’s long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods.

For cloud storage service vendors, balancing the client-perceived IO performance and the self-perceived space cost is always one of the standing challenges. When applying deduplication techniques for the cloud storage systems, the demand for optimizing such tradeoff becomes more pressing. Enabling deduplication decreases the storage space cost, whereas the IO performance will be somewhat affected due to extra processing overhead and data fragmentation. In this article, we address this challenge by proposing MUSE , a MU ti-tiered and S LA-driv E n deduplication framework for cloud storage systems. First, we propose a novel notation of Dedup-SLA (deduplication-oriented service level agreement). With different levels of quantified performance/space-cost combinations, the Dedup-SLA serves as a refined service quality protocol between service vendor and customer. Second, MUSE adopts multi-tiered deduplication that orchestrates several combinational forms of deduplication into multiple tiers with varied “deduplication strength”. Third, we implement a mechanism called dynamic deduplication regulation (DDR) to adjust the deduplication behavior during runtime. MUSE’s deduplication behavior is periodically switched between tiers according to the predefined Dedup-SLA and instant system status. We conduct comprehensive experiments to compare MUSE with several other types of deduplication schemes. The results demonstrate that MUSE significantly optimizes the IO-performance/space-cost balance compared to other schemes, hence delivering higher deduplication service quality for deduplication-enabled cloud storage systems.

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

Deep learning has gained tremendous traction from the developer and researcher communities. It plays an increasingly significant role in a number of application domains. Deep learning frameworks are proposed to help developers and researchers easily leverage deep learning technologies, and they attract a great number of discussions on popular platforms, i.e., Stack Overflow and GitHub. To understand and compare the insights from these two platforms, we mine the topics of interests from these two platforms. Specifically, we apply Latent Dirichlet Allocation (LDA) topic modeling techniques to derive the discussion topics related to three popular deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform, we compare the topics across the three deep learning frameworks. Moreover, we make a comparison of topics between the two platforms. Our observations include 1) a wide range of topics that are discussed about the three deep learning frameworks on both platforms, and the most popular workflow stages are Model Training and Preliminary Preparation. 2) the topic distributions at the workflow level and topic category level on Tensorflow and PyTorch are always similar while the topic distribution pattern on Theano is quite different. In addition, the topic trends at the workflow level and topic category level of the three deep learning frameworks are quite different. 3) the topics at the workflow level show different trends across the two platforms. e.g., the trend of the Preliminary Preparation stage topic on Stack Overflow comes to be relatively stable after 2016, while the trend of it on GitHub shows a stronger upward trend after 2016. Besides, the Model Training stage topic still achieves the highest impact scores across two platforms. Based on the findings, we also discuss implications for practitioners and researchers.

An edge computing system is an emergent architecture for providing computing, storage, control, and networking abilities, that is an important technology to realize Internet of Things and smart cities. In an edge computing environment, users can offload their computationally expensive tasks to offloading points, which may reduce the energy consumption or communication delay. There are a large number of offloading points and users in a system, and their tasks are structured. However, resources of offloading points are limited, and users have different preferences for energy consumption and communication delays. In this paper, we first establish a system model for the environment with multiple users, multiple offloading points, and structured tasks. Then, we formalize an offloading decision problem in such an environment as a cost-minimization problem, which is a NP-hard problem. Thus, we design a method based on backtracking to obtain its exact solution; the method’s time complexity is, unfortunately, exponential with the number of offloading points. To reduce the complexity, a method based on an improved genetic algorithm and a method based on a greedy strategy are designed. Finally, we validate and compare three methods in terms of the total cost of all users, resource utilization of offloading points and execution time. The simulation results show that the last method performs the best.

With the rapid development of mobile computing technology, more and more complex tasks are now able to be fulfilled on users’ mobile devices with an increasing number of novel services. However, the development of mobile computing is limited by the latency brought by unstable wireless network and the computation failure caused by the constrained resources of mobile devices. Therefore, people turn to establish a service provisioning system based on mobile edge computing (MEC) model to solve this problem. With the help of services deployed on edge servers, the latency can be reduced and the computation can be offloaded. Though the edge servers have more available resources than mobile devices, they are still resource-constrained, so they must carefully choose the services for deployment. In this paper, we focus on improving performance of the service provisioning system by deploying and replacing services on edge servers. Firstly, we design and implement a prototype of service provisioning system that simulates the behaviors between users and servers. Secondly, we propose an approach to deploy services on edge servers before the launching of these servers, and propose an approach to replace services on edge servers dynamically. Finally, we conduct a series of experiments to evaluate the performance of our approaches. The result shows that our approach can improve the performance of service provisioning systems.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

In the information age, the amount of data is huge which shows an exponential growth. In addition, most services of application need to be interdependent with data, cause that they can be executed under the driven data. In fact, such a data-intensive service deployment requires a good coordination among different edge servers. It is not easy to handle such issues while data transmission and load balancing conditions change constantly between edge servers and data-intensive services. Based on the above description, this paper proposes a Data-intensive Service Edge deployment scheme based on Genetic Algorithm (DSEGA). Firstly, a data-intensive edge service composition and an edge server model will be generated based on a graph theory algorithm, then five algorithms of Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), Ant Colony Algorithm (ACO), Optimized Ant Colony Algorithm (ACO_v) and Hill Climbing will be respectively used to obtain an optimal deployment scheme, so that the response time of the data-intensive edge service deployment reaches a minimum under storage constraints and load balancing conditions. The experimental results show that the DSEGA algorithm can get the shortest response time among the service, data components and edge servers.

Open source software has drawn more and more attention from researchers, developers and companies nowadays. Meanwhile, many Chinese technology companies are embracing open source and choosing to open source their projects. Nevertheless, most previous studies are concentrated on international companies such as Microsoft or Google, while the practical values of open source projects of Chinese technology companies remain unclear. To address this issue, we conduct a mixed-method study to investigate the landscape of projects open sourced by three large Chinese technology companies, namely Baidu, Alibaba, and Tencent (BAT). We study the categories and characteristics of open source projects, the developer's perceptions towards open sourcing effort for these companies, and the internationalization effort of their open source projects. We collected 1,000 open source projects that were open sourced by BAT in GitHub and performed an online survey that received 101 responses from developers of these projects. Some key findings include: 1) BAT prefer to open source frontend development projects, 2) 88\% of the respondents are positive towards open sourcing software projects in their respective companies, 3) 64\% of the respondents reveal that the most common motivations for BAT to open source their projects are the desire to gain fame, expand their influence and gain recruitment advantage, 4) respondents believe that the most common internationalization effort is "providing an English version of readme files", 5) projects with more internationalization effort (i.e., include an English readme file) are more popular. Our findings provide directions for software engineering researchers and provide practical suggestions to software developers and Chinese technology companies.

Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.

In this paper, we plan to predict a ranking on e-books by analyzing the implicit user behavior, and the goal of our work is to optimize the ranking results to be close to that of the ground truth ranking where e-books are ordered by their corresponding reader number. As far as we know, there exist little work on predicting the future e-book ranking. To this end, through analyzing various user behavior from a popular e-book reading mobile APP, we construct three groups of features that are related to e-book ranking, where some features are created based on the popular metrics from the e-commerce, e.g., conversion rates. Then, we firstly propose a baseline method by using the idea of learning to rank (L2R), where we train the ranking model for each e-book by taking all its past user feedback within a time interval into consideration. Then we further propose TDLR: a Time Decay based Learning to Rank method, where we separately train the ranking model on each day and combine these models by gradually decaying the importance of them over time. Through extensive experimental studies on the real-world dataset, our approach TDLR is proved to significantly improve the e-book ranking quality more than 10% when compared with the L2R method where no time decay is considered.

Deploying deduplication for distributed primary storage is a sophisticated and challenging task, considering that the demands of low read/write latency, stable read/write performance, and efficient space saving are all of paramount importance. Unfortunately, existing schemes cannot present a satisfactory solution for the aforementioned requirements simultaneously. In this article, we propose D $^{3}$ , a dynamic dual-phase deduplication framework for distributed primary storage. Several major innovations are established in D $^{3}$ . First, we formulate a deduplication-oriented taxonomy called Dedup-Type , to group data with similar deduplication-related characteristics into larger categories. It serves as coarse-grained filter and one of the prioritizing references in D $^{3}$ . Second, D $^{3}$ is a dual-phase framework—inline-phase and offline-phase deduplication processes work in concert with each other. Third, D $^{3}$ operates in a dynamic manner. We design two critical mechanisms: context-aware threshold adjustment (CTA) for local inline-phase deduplication, and deferred priority-based enforcement (DPE) for global offline-phase deduplication. The CTA mechanism enables selective deduplication under a periodically updated threshold. Data skipped during the inline phase is regarded as a candidate for offline phase, and is handled in a prioritized order under the governance of DPE mechanism. Evaluation results demonstrate that, compared with conventional inline and offline deduplication schemes, D $^{3}$ achieves more efficient and stabler read/write performance with competitive space saving.

Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

High reliability, efficient I/O performance and flexible consistency provided with low storage cost are all desirable properties of cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimum on all these properties is impractical. N-way Replication and Erasure Coding, two extensively-applied storage schemes with high reliability, adopt opposite and unbalanced strategies on the tradeoff among these properties, thus considerably restraining their effectiveness on wide range of workloads. To address the aforementioned obstacle, we propose a novel storage scheme called ASSER , an ASS embling chain of E rasure coding and R eplication. ASSER stores each object in two parts: a full copy and a certain amount of erasure-coded segments. We establish dedicated read/write protocols for ASSER leveraging the unique structural advantages. On the basis of elementary protocols, we implement sequential and PRAM (Pipeline-RAM) consistency to make ASSER feasible for various services with different performance/consistency requirements. Evaluation results demonstrate that under the same fault tolerance and consistency level, ASSER outperforms N-way replication and pure erasure coding in I/O throughput under diverse system and workload configurations with superior performance stability. More importantly, ASSER delivers stably efficient I/O performance at much lower storage cost than the other comparatives.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

The advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

Mobile edge computing (MEC) has been proposed to significantly reduce the response time of service invocations for end users. In MEC environment, a service provider can create multiple instances from a service and deploy them to different hired edge servers, where the deployed instances can be selected and invoked to decrease the network latency by nearby users. However, service instance selection in MEC is a challenging research problem from threefold aspects. First, the limitations of an edge server in terms of computation capacity and coverage range result in serving for only a certain number of users at the same time. Second, due to variable geographical locations from user mobility paths in MEC, the mobility of edge users is highly related to data transmission rate and affects the delay of service invocations. Furthermore, when many users in an edge server covered region request the same service instance at the same time, they interfere with each other and may reduce the experience of service invocations if there is no effective strategy to distribute these requests to appropriate instances deployed on different edge servers. To improve the user experience on service invocations with a lower response time, we take the above three factors into account and model the service instance selection problem (SISP) in MEC as an optimization problem, and propose a novel genetic algorithm-based approach with a response time-aware mutation operation with normalization for service instance selection called GASISMEC to find approximately optimal solution. Extensive experiments are conducted on two widely-used real-world datasets. The results demonstrate that our approach significantly outperforms the six baseline competing approaches.

Cloud computing has attracted increasing interest in industry and academia. However, due to the constantly expanding scale of cloud platforms, the monitoring of clouds encounters a number of critical challenges related to flexibility, scalability, efficiency, and performance. In this paper, we present JTang Cloud Monitoring System (JTangCMS), an efficient monitoring system for cloud platforms. Our contributions cover the collection, delivery, and processing of monitoring data. For data collection, a flexible and scalable agent is implemented with pluggable monitoring components to collect runtime information from different entities. For data delivery, an efficient and robust data dissemination framework is implemented to transmit the runtime information reliably with high throughput and low latency, based on the Data Distribution Service (DDS). For data processing, a cloud action platform is developed to support cloud management decision-making, based on complex event processing (CEP). Finally, detailed experimental evaluations show the feasibility and efficiency of JTangCMS.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Service computing offers an exciting paradigm for service provision and consumption. The field is now embracing new opportunities in the mobile Internet era, which is characterized by ubiquitous wireless connectivity and powerful smart devices that let users consume or provide services anytime and anywhere. In this article, the authors describe important issues related to service computing on mobile and IoT devices. They then propose application patterns and analyze the main challenges from the perspectives of both service provision and service consumption.

A new descriptor called LRP is proposed to describe subtle texture for 2D image.Texture enhancement is built to extend LRP to represent HSI.A supervised manifold learning method called DLPP is proposed to reduce dimension.The proposed framework outperforms other methods with experiments. Classification is a critical step to make full use of the hyperspectral data. The most current approaches perform well for analyzing the macro texture, but they often fail to deal with the micro texture. Thus, this study proposes a general framework for the material with micro texture based on Hyperspectral Image (HSI) technique. In this framework, Local Response Pattern (LRP) is firstly proposed to describe 2D image texture to preserve more structural information and keep less sensitive to image conditions. Then, LRP is extended to represent HSI with Texture Enhancement (TE) by considering opponent relationships between pairs of bands. After that, Discriminated Locality Preserving Projection (DLPP) is proposed to reduce data dimension in a linearizing nonlinear manifold way. Finally, experiments on the hyperspectral images of fresh and frozen-thawed fish fillets are conducted. The results demonstrate that the proposed framework is efficient in terms of both recognition rates and robustness.

Quality-of-Service (QoS) is a fundamental element in Service-Oriented Computing (SOC) domain. At the ongoing age of Web 2.0, predicting the missing QoS values becomes more and more important since it is an indispensable preprocess of numerous service-oriented applications. Previous research works on this task underestimate the importance of users' geographical information, which we argue would contribute to improving prediction accuracy in Web services invocation process. In this paper, we propose a novel collaborative location-based regularization framework (Colbar) to address the problem of personalized QoS prediction. We first leverage the personal geographical and QoS information to identify robust neighborhoods. And then, we collect the wisdom of crowds to construct two location-based regularization terms, which are integrated to build up an unified Matrix Factorization framework. Finally we make intermediate fusions to generate better prediction results. The experimental analysis on a large-scale real-world QoS dataset shows that the prediction accuracy of Colbar outperforms other state-of-the-art approaches in various criteria.

With the development of information technology, data on the Internet is growing even faster than Moore's Law. At the age of big data, more and more services are created to deal with big data, which are called data-intensive services. In most cases, multiple data-intensive services are assembled into a service composition to meet complicated requirements. Since the big-data transmission, which is occurred among component services as well as between a service and a data center, has great influence on the overall performance of a composition, deploying those services cannot be considered independently. This paper proposes an optimal deployment method based on a negative selection algorithm for a data-intensive service composition to reduce the cost of the data transmission. When making a deployment schedule, it considers not only the cost of data transmission among component services, but also the load balance of data centers where component services are deployed. It models the deployment problem as a combination optimization problem and extends a negative selection algorithm to get an optimal deployment plan. A series of experiments are carried out to evaluate the performance of the proposed method using different settings as well as to compare with other methods. The results show that the method outperforms others for the problem of data-intensive service composition deployment.

© 2016 IEEE. With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

This paper proposes a novel approach based on the planning-graph to solve the top-k QoS-aware automatic composition problem of semantic Web services. The approach includes three sequential stages: a forward search stage to generate a planning-graph to reduce the search space of the following two stages greatly, an optimal local QoS calculating stage to compute all the optimal local QoS values of services in the planning, and a backward search stage to find the top-K composed services with optimal QoS values according to the planning-graph and the optimal QoS value. In order to validate the approach, experiments are carried out based on the test sets offered by the WS-Challenge competition 2009. The results show that the approach can find the same optimal solution as the champion system from the competition but also can provide more alternative solutions with the optimal QoS for users.

The large-granularity Web services are a new form of Web services. In contrast to the traditional Web services, they often have more interfaces, encapsulate more complex business logics, and provide more functions etc.. However, the existing Web services composition approach cannot be fit for the kind of Web service. In order to solve the problem, we propose the notation of the inverted interface dependency and extend it to OWL-S firstly. A large-granularity Web service is regarded as a group of the inverted interface dependency. Then we propose an automatic composition approach based on the idea of proofs as programs. The main idea of the method is as follows. Specification of composition requirement is described as a proposition which can be proved by related theory. The proof can be viewed as a solution of composition specification. By the case study, the availability of the approach is approved. At last, we develop a prototype JTangComposition to implement our approach.

To discover services efficiently has been regarded as one of important issues in the area of Service Oriented Computing (SOC). This article carries out a survey on the issue and points out the problems for the current semantic-based service discovery approaches. After that, an information model for registered services is proposed. Based on the model, it brings forward a two-phase semantic-based service discovery method which supports both the operation matchmaking and operation-composition matchmaking. The authors import the bipartite graph matching to improve the efficiency of matchmaking. An implementation of the proposed method is presented. A series of experiments show that the method gains better performance on both discovery recall rate and precision than a traditional matchmaker and it also scales well with the number of services being accessed.

Dynamically composing services requires mechanisms to ensure component services compatible with each other both at all of the syntax, semantic and behavioral level. This paper focuses on the issue of behavioral compatibility in a service composition. It adopts the π-calculus to model service behaviors and interactions in a formal way. Based on the formalization, it proposes a method to automatically check the behavioral compatibility in a qualitative way. Furthermore, it presents an algorithm to compute the compatibility degree in a quantitative way. The algorithm is implemented in a prototype and its performance analysis is also carried out to show that it can help composing services on the fly and ensure the services compatible with each other to provide functions with newly-added values.

Middleware, operating systems, and databases have been developed to the point where they have become fundamental components in the modern software era. The JTang middleware platform was developed for the modern service industry, and it offers a range of basic techniques, methods, middleware software, and tools for the services computing technology. This chapter describes the background for the development of this platform and its main components.

SaaS(Software as a Service) has recently made a great impact on the software industry and changed the traditional lifetime of software. To obtain the benefit of SaaS, more and more software products have been converted into SaaS model, including business workflow management systems (BWfMS). Compared with the traditional BWfMSs, SaaS oriented BWfMSs introduce a new set of features which demand a new architectural design for SaaS oriented BWfMSs. Although several SaaS oriented BWfMSs have been developed, a study from an architectural perspective for SaaS oriented BWfMSs is still missing. The main contributions of this paper are: 1) according to the four SaaS maturity levels, it depicts four different scenarios where SaaS oriented BWfMSs work, 2) based on the four different scenarios, it proposes a reference model for SaaS oriented BWfMSs at each SaaS maturity level; 3) to validate the feasibility of the proposed reference model, it implements a specific BWfMS named WAAS.

SOA based software is typically based on dynamic reconfiguration, since it is the composition of services. Then ensuring the correctness of dynamic reconfiguration is an important challenge. But few work focuses on it, this paper gives a better answer to solve the problem. It uses services-behavioral type discipline extended the Martin-Lof’s Type Theory (for short, MLTT) which supports a type-theoretic formulation of services behavior structured patterns, so that services can be formally constructed by type services-behavioral type. Then, the type rules for subtype, duality, and correctness of dynamic reconfiguration are discussed. In our case study, the deductions are gave to show that how to verify the correctness of the dynamic reconfiguration. Finally, the implement of our approach is discussed.