Prompt tuning for large-scale pretrained language models (PLMs) has shown remarkable potential, especially in low-resource scenarios such as few-shot learning. Moreover, derivative-free optimisation (DFO) techniques make it possible to tune prompts for a black-box PLM to better fit downstream tasks. However, there are usually preconditions to apply existing DFO-based prompt tuning methods, e.g. the backbone PLM needs to provide extra APIs so that hidden states (and/or embedding vectors) can be injected into it as continuous prompts, or carefully designed (discrete) manual prompts need to be available beforehand, serving as the initial states of the tuning algorithm. To waive such preconditions and make DFO-based prompt tuning ready for general use, this paper introduces a novel genetic algorithm (GA) that evolves from empty prompts, and uses the predictive probabilities derived from the backbone PLM(s) on the basis of a (few-shot) training set to guide the token selection process during prompt mutations. Experimental results on diverse benchmark datasets show that the proposed precondition-free method significantly outperforms the existing DFO-style counterparts that require preconditions, including black-box tuning, genetic prompt search and gradient-free instructional prompt search.

In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

Recently, increasing investments in satellite-related technologies make the low earth orbit (LEO) satellite constellation a strong complement to terrestrial networks. To mitigate the limitations of the traditional satellite constellation “bent-pipe” architecture, satellite edge computing (SEC) has been proposed by placing computing resources at the LEO satellite constellation. Most existing works focus on space-air-ground integrated network architecture and SEC computing framework. Beyond these works, we are the first to investigate how to efficiently deploy services on the SEC nodes to realize robustness aware service coverage with constrained resources. Facing the challenges of spatial-temporal system dynamics and service coverage-robustness conflict, we propose a novel online service placement algorithm with a theoretical performance guarantee by leveraging Lyapunov optimization and Gibbs sampling. Extensive simulation results show that our algorithm can improve the service coverage by$4.3\times $compared with the baseline.

In mobile edge computing, popular mobile applications, such as augmented reality, usually offload their tasks to resource-rich edge servers. The user experience can be considerably affected when many mobile users compete for the limited communication and computation resources. The key technical challenge in task offloading is to guarantee the Quality of Service (QoS) for such applications. Existing work on task offloading focus on deterministic QoS (delay) guarantee, which means that tasks have to complete before the given deadline with 100 percent. However, it is impractical to impose a deterministic QoS guarantee for tasks due to the high dynamics of the wireless environment when offloading to edge servers. In this paper, we focus on task offloading with statistical QoS guarantee (tasks are allowed to complete before a given deadline with a probability above the given threshold), which can further save more energy by loosing the QoS requirement. Specially, we first propose a statistical computation model and a statistical transmission model to quantify the correlation between the statistical QoS guarantee and task offloading strategy. Then, we formulate the task offloading problem as a mixed integer non-Linear programming problem with the statistical delay constraint. We transform the statistical delay constraint into the constraints on CPU cycle numbers and the delay exponent respectively. We propose an algorithm to provide the statistical QoS guarantee for tasks using convex optimization theory and Gibbs sampling method. Experiment results show that the proposed algorithm outperforms the three baselines.

3D object detection has attracted a significant amount of attention and interest from both academia and industry due to its indispensable role in understanding 3D environments. By fusing the camera and LiDAR sensors, it is expected to improve both the accuracy and robustness of 3D object detection. However, existing fusion approaches are either limited by cascading processing, or easy to be influenced by the interference information in multi-sensors. To this end, this paper incorporates meta learning to fuse the camera and LiDAR data. Specifically, we first extract meta knowledge from images, and then apply it to generate the parameter weights of a set of convolution kernels, which are further exploited for feature extraction on LiDAR point clouds. Furthermore, we propose a meta fusion network (MF -Net), enabling accurate and robust 3D object detection. The superiority and effectiveness of MF -Net have been demonstrated by extensive experiments on KITTI 3D object detection dataset.

Artificial intelligence-empowered smart things (e.g., robots, autonomous vehicles, and unmanned aerial vehicles) have been transforming the world. The “brains” of smart things can be abstracted as the agents or cybertwins residing on end devices and edge servers. The next-generation communication networks (i.e., 6G) will become the nervous system for these agents and natively support multiagent cooperation. By sharing local observations and intentions via communication channels, the agents could better understand the environments and make right decisions. Due to the limited channel bandwidth, the communication is considered as a bottleneck of multiagent cooperation. In this article, we propose a graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck. Specifically, a variational information bottleneck is used to encode the observations and intentions compactly. Furthermore, a graph information bottleneck with the attention-based neighbor sampling mechanism is utilized to improve the effectiveness and robustness of the multiround communication process. The experimental results show that GraphComm can improve the effectiveness, robustness, and efficiency of communication in multiagent cooperative tasks as compared to baseline methods.

It is widely recognized that connected vehicles have the potential to further improve the road safety, transportation intelligence and enhance the in-vehicle entertainment. By leveraging the 5G enabled Vehicular Ad hoc NETworks (VANET) technology, which is referred to as 5G-VANET, a flexible software-defined communication can be achieved with ultra-high reliability, low latency, and high capacity. Many enabling applications in 5G-VANET rely on sharing mobile data among vehicles, which is still a challenging issue due to the extremely large data volume and the prohibitive cost of transmitting such data using 5G cellular networks. This article focuses on efficient cooperative data sharing in edge computing assisted 5G-VANET. First, to enable efficient cooperation between cellular communication and Dedicated Short-Range Communication (DSRC), we first propose a software-defined cooperative data sharing architecture in 5G-VANET. The cellular link allows the communications between OpenFlow enabled vehicles and the Controller to collect contextual information, while the DSRC serves as the data plane, enabling cooperative data sharing among adjacent vehicles. Second, we propose a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph. Specifically, considering the continuous data sharing, we propose a balanced greedy algorithm, which can make the content distribution more balanced. Furthermore, due to the fixed amount of computing resources allocated to this software-defined cooperative data sharing service, we propose an integer linear programming based decomposition algorithm to make full use of the computing resources. Extensive simulations in NS3 and SUMO demonstrate the superiority and scalability of the proposed software-defined architecture and cooperative data sharing algorithms.

Vehicular edge computing (VEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this article, we study the problem of task scheduling in VEC, where multiple computation-intensive vehicular applications can be offloaded to roadside units (RSUs) and each application can be further divided into multiple tasks with task dependency. The tasks can be scheduled to different mobile-edge computing servers on RSUs for execution to minimize the average completion time of multiple applications. Considering the completion time constraint of each application and the processing dependency of multiple tasks belonging to the same application, we formulate the multiple tasks scheduling problem as an optimization problem that is NP-hard. To solve the optimization problem, we develop an efficient task scheduling algorithm. The basic idea is to prioritize multiple applications and prioritize multiple tasks so as to guarantee the completion time constraints of applications and the processing dependency requirements of tasks. The numerical results demonstrate that our proposed algorithm can significantly reduce the average completion time of multiple applications compared with benchmark algorithms.

This article investigates the coded cooperative data exchange (CCDE) problem, where a set of nodes initially hold a subset of packets and wish to retrieve all desired packets via direct wireless communication with neighbors. The CCDE problem in multihop wired networks has seen significant research recently and is proved to be NP-hard. The CCDE problem in multihop wireless networks (MWNs) must additionally consider half-duplex constraint, interference constraint, and channel constraint. Channel assignment brings new challenges to the CCDE problem in MWN, since it is also a well-known NP-hard problem even with one channel. In this article, we study the CCDE problem in MWN with multiple channels. We first construct a path network to evaluate the priority for each possible transmission and then construct a conflict graph (CG). This graph depicts the half-duplex, interference, and channel constraints. After that, a greedy channel assignment algorithm is proposed to obtain the nonconflict transmissions based on the constructed CG and assign a channel for each selected transmission. Finally, based on the selected transmissions, we construct a single-source multicast network exploiting the time expanded network, with which the network encoding and decoding schemes can be computed within polynomial time. Extensive simulations are conducted to demonstrate the efficiency of the proposed algorithm.

Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

Guiding vehicles to their destination is an essential service. Nowadays navigation systems are mainly relying on the traffic conditions of road network, and other influence factors are not taken into account accurately, which is easy to lead to imbalance between the supply and demand of roads, resulting in congestion. In this paper, we introduce an online guiding approach via value iteration network on road graphs, R-VIN for short, which is an end-to-end planning model. In R-VIN, a large-scale real GPS trajectories are mapped via map-matching based road topology, which enables R-VIN to catch the experienced driving knowledge. Then we propose a conversion method from irregular road graphs to regular grid images to formalize the learning model. For a global optimum, ConvLSTM is used to predict the future traffic situation to form prediction reward of R-VIN. Combining with current reward, a double rewarded VIN is used to solve the plan-involved function. Lastly, we train and evaluate R-VIN on planning problem in road networks, showing that R-VIN can achieve segment-based autonomous navigation with high top-k accuracy and less commuting time.

Crowd intelligence tries to gather, process, infer, and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things. A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors, or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First, we use a reward-penalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers (ES) of the mobile edge computing network, which together function as a trustless hybrid human-machine crowd-intelligence platform. As ES are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved ES and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method, respectively.

In vehicular sensor networks, vehicles can act as mobile sensors to monitor the dynamic features of the physical world such as traffic flow, air quality, and temperature. However, the conventional full-coverage sensing approach is neither realizable nor cost-effective since the sensor-equipped vehicles are unevenly distributed and the environmental data are spatio-temporally correlated. To this end, we propose a cost-effective urban environment sensing solution (CESense), that exploits the sensing data correlations to improve the sensing accuracy and efficiency. CESense gathers data only at some specific areas of the whole sensing space and reliably infers the status of unsensed areas. Particularly, CESense uses a probabilistic matrix factorization model to reveal the latent features that impact the environmental status. Then, an appropriate set of sensing areas can be selected by fully taking advantage of these latent features and the sensing resource distribution patterns. In addition, to be adaptive to the dynamic environment, a checkpoint mechanism is designed to supervise the data gathering progress. Extensive experiments, which are based on the real taxicab mobility traces and air quality data collected in Beijing city, demonstrate that CESense can significantly improve the accuracy and efficiency of vehicular sensing.

Crowdsourcing is a promising platform, whereby massive tasks are broadcasted to a crowd of semi-skilled workers by the requester for reliable solutions. In this paper, we consider four key evaluation indices of a crowdsourcing community (i.e., quality, cost, latency, and platform improvement), and demonstrate that these indices involve the interests of the three stakeholders, namely the requester, worker, and crowdsourcing platform. Since the incentives among these three stakeholders always conflict with each other, to elevate the long-term development of the crowdsourcing community, we take the perspective of the whole crowdsourcing community, and design a crowdsourcing mechanism to align incentives of stakeholders together. Specifically, we give workers reward or penalty according to their reporting solutions instead of only nonnegative payment. Furthermore, we find a series of proper reward-penalty function pairs and compute workers personal order values, which can provide different amounts of reward and penalty according to both the workers reporting beliefs and their individual history performances, and keep the incentive of workers at the same time. The proposed mechanism can help latency control, promote quality and platform evolution of crowdsourcing community, and improve the aforementioned four key evaluation indices. Theoretical analysis and experimental results are provided to validate and evaluate the proposed mechanism, respectively.

Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.

Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. Many existing methods can be used for short-term or long-term traffic prediction, but they focus more on regions than on road segments. The lack of fine-grained traffic predicting approach hinders the development of ITS. Therefore, MapLSTM, a spatio-temporal long short-term memory network preluded by map-matching, is proposed in this paper to predict fine-grained traffic conditions. MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future. Breaking the single-index forecasting, MapLSTM can predict the vehicle speed, traffic volume, and the travel time in different directions of road segments simultaneously. Experiments confirmed MapLSTM can not only achieve prediction for road segments based a large scale of GPS trajectories effectively but also have higher predicting accuracy than GPR and ConvLSTM. Moreover, we demonstrate that MapLSTM can serve various applications in a lightweight way, such as cognizing driving preferences, learning navigation, and inferring traffic emissions.

Mobile Edge Computing (MEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this paper, to reduce the latency of the computation offloading of vehicles, we study multiple vehicles computation offloading problem in vehicular edge networks. We formulate the problem as a multi-user computation offloading game problem, prove the existence of Nash equilibrium (NE) of the game and propose a distributed computation offloading algorithm to compute the equilibrium. We analyze the price of anarchy of the game algorithm and evaluate the performance of the game algorithm using extensive simulations. Numerical results show that the proposed algorithm can greatly reduce the computation overhead of vehicles.

With the development of mobile Internet, mobile service is emerging one after another, and the problem of information overload is becoming ever more serious. As an important tool to alleviate information overload, mobile service recommendation has attracted more and more attention. However, traditional recommendation algorithms always recommend popular services to users, which result into a rich-get-richer problem and become a barrier for the unpopular services to startup and growth. In order to promote the healthy development of the service ecosystem, it is necessary to guarantee the fairness of unpopular services. To address this problem, this paper proposes a fairness-aware mobile service recommendation method (FMSR), which gives a relatively fair recommendation opportunity for unpopular services. FMSR makes a tradeoff between recommendation accuracy and fairness, and can recommend popular services and unpopular services respectively. For unpopular services, we design a fair efficiency function and use combinatorial optimization techniques to achieve recommendations. For popular services, bias matrix factorization is utilized to implement recommendations. Experimental results based on real-world demonstrate that FMSR significantly improve the fairness of mobile service recommendation in the evolving mobile service ecosystem.

Electricity consumption of diverse facilities can be recorded hourly or minutely due to the development of smart grid and smart home technologies. As a result, the traditional relationship analysis between electricity consumption and other external factors should be improved and conducted based on fine-grained rather than coarse-grained time series data. In that case, canonical correlation analysis (CCA) is an appropriate method to process two or more datasets containing multiple variables. However, the result of CCA is not unique, which leads to the challenge for batch-oriented data analysis. To solve this problem, we propose an optimal result selection mechanism for CCA and kernel CCA algorithms based on accuracy validation of canonical weights and components. An additional clustering is also provided to optimize the approach in terms of time complexity and accuracy performance. The approach is implemented on three multivariable datasets, referring to 960 non-residential electricity consumers in 60 towns or cities of the same district, to find the canonical correlations among electricity consumption, gas consumption and climate change for every consumer. The experimental results indicate that the proposed approach outperforms other related methods. We also find out three typical patterns of canonical correlation curves, which are relative stability, cyclic change and seasonal change.

Choosing an appropriate route is a critical problem in urban navigation. Being familiar with roads topology and other vehicles' routes, experienced drivers could usually find a near optimal route. However, the nowadays navigation applications hardly catch the domain knowledge and drivers' interaction in this scenario. In fact, they only recommend several routes and leave the most difficult decision to driver. Hence the route is often congested by many vehicles whose drivers make a similar choose. To intelligently make right decision on navigation and improve traffic efficiency for each vehicle, we propose a neural network structure which learns to plan coarse-grained route in complex urban areas.Focused on learning to navigate, this paper first formalizes urban map and vehicle route model. The city map is segmented into grids and each vehicle's route is mapped to grids. Based on grid-world model, we solicit both global traffic status and driving actions from large-scale taxicab GPS data. The learn-to-plan problem is therefore to find a policy function from a global status representation to an experienced driver's action under that status. Traditional neural network is difficult to learn to this plan-involved function, so we leverage and modify value iteration network (VIN), which explicitly takes long-term plan into consideration. Finally we evaluate the performance of proposed network on real map and trajectory data in Beijing, China. The results show that VIN can achieve human driver performance in most cases, with high success rate and less commuting time.

Cloud data center traffic is experiencing a rapid growth as more and more data-intensive applications are required to process big data in a cloud data center. Although fat-tree networks own rich path multiplicity, and have been widely adopted as network topologies in cloud data center networks to transmit vast bisection bandwidth, they lead to bandwidth-related bottlenecks. To address this issue, in this paper, we propose a traffic-aware virtual cluster reallocation approach via biogeography-based optimization to allocate some reallocated virtual machines (VMs) as compact as possible with those VMs in the same virtual clusters. In order to validate our approach, we build a system model to perform a thorough evaluation of its performance. Experimental results show that our proposed approach outperforms six existed approaches in term of total transmission cost, total processing time, and total network resource consumption.

The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

The electricity load data recorded by smart meters contain plenty of knowledge that contributes to obtaining load patterns and consumer categories. Generally, the daily load curves are clustered first in order to obtain load patterns of each consumer. However, due to the volume and high dimensions of load curves, existing clustering algorithms are not appropriate in this situation. Thus, a fused load curve clustering algorithm based on wavelet transform (FCCWT) is proposed to solve this problem. The algorithm includes two main phases. First, FCCWT applies multilevel discrete wavelet transform (DWT) to convert the daily load curves for dimensionality reduction. Second, it detects clusters at two outputs of the first phase, and then fuses two groups of clusters with a sub-algorithm named cluster fusion to achieve the optimized clusters. FCCWT is implemented on datasets of both China and United States. Their clustering performances are evaluated by diverse validity indices comparing with four typical clustering methods. The experimental results show that FCCWT outperforms other comparison methods. Additionally, case analysis of two datasets are also provided to discuss the significance of load patterns.

The performance of a vehicular ad hoc network (VANET) largely depends on the underlying medium access control (MAC), as it determines the schedule utility of physical resources. However, in existing time-division multiple access (TDMA)-based MAC protocols, a node usually acquires slots based on what each node senses, which is typically the coupling of the control and data plane. This coupling makes the TDMA protocols unable to rapidly and agilely deal with the challenges in VANETs, such as high mobility and dynamic network densities. Inspired by the software-defined network (SDN), we propose a novel SDN-based MAC protocol, named sdnMAC, to handle these challenges. A novel roadside openflow switch (ROFS) is designed as the roadside unit, controlled by the openflow controller. The sdnMAC can be divided into two tiers, the management of ROFSes (MA-ROFS) by the controller and the management of vehicles (MA-VEH) by ROFSes. In MA-ROFS, the controller schedules the cooperative sharing of time slot information among ROFSes. In MA-VEH, each ROFS allocates slots based on this shared information, thus decoupling of the control and data plane. This decoupling provides great rapidness and agility to sdnMAC, thus handling the rapid mobility and varying vehicle densities. Extensive simulations using network simulator NS3 and traffic simulator SUMO are performed. It is shown that the sdnMAC protocol can better meet the requirements of cooperative safety in VANETs.

Federated‐cloud has been widely deployed due to the growing popularity of real‐time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated‐cloud. Specifically, the network resources are deployed and selected according to k‐means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.

Group sensing is a kind of crowdsensing service where HD map producers motivate private cars in a local region to collect data from real world. Group sensing needs vehicles to communicate physically and drivers to collaborate strategically in a mobile or edge‐assisted environment. First, we consider collaboration module that motivates drivers to be participants; centralized and distributed motivating methods are discussed. Secondly, we consider communication module; two VANET‐based methods are proposed to achieve message relaying in edge infrastructure. To accomplish participants’ selection, three combinations of two modules are proposed and simulated based on a flexible framework. The results show that centralized selection could motivate collaboration at a low price but brings heavy communication overhead. Clustered selection requires more incentives and less communication overhead than centralized selection. Distributed selection is usually the first class choice because of its fine performances on both communicating and motivating.

In order to build highly reliable composite service via Service Oriented Architecture (SOA) in the Mobile Fog Computing environment, various fault tolerance strategies have been widely studied and got notable achievements. In this paper, we provide a comprehensive overview of key fault tolerance strategies. Firstly, fault tolerance strategies are categorized into static and dynamic fault tolerance according to the phase of their adoption. Secondly, we review various static fault tolerance strategies. Then, dynamic fault tolerance implementation mechanisms are analyzed. Finally, main challenges confronted by fault tolerance for composite service are reviewed.

The proliferation of real-time applications, such as online gaming, video chatting, brings unprecedented pressure for multi-cloud application providers to secure the good quality of experience. These applications are designed for cooperative group users/members/clients and are expected to have low data transmission latency due to frequent interactions. We use total data transmission latency (TDTL) to represent the group latency in this paper. However, existing approaches perform poorly since they often ignore the feature of the cooperative group scenario and cannot provide a flexible rental cost model for application providers. Minimizing TDTL is challenging due to the difficulties in 1) Finding an effective method to transmit data through multi-cloud datacenters, 2) Taking the cooperative group scenario into consideration, 3) Providing a flexible transmission rental cost model. To this end, we propose CMIP, an innovative approach that aims to minimize the TDTL by renting virtual machines of well-connected datacenters as the proxy for the cooperative group scenario in multi-cloud providers. First, a weighted graph is constructed to illustrate the multi-cloud network. Second, we define a path latency model and a rental cost model for the cooperative group. After that, Yen's algorithm and mixed integer programming are adopted to optimize the TDTL with rental cost constraint. To study the performance of CMIP, we conduct extensive evaluations using multiple real data traces and compare it with related approaches. The comprehensive evaluation analysis shows that the CMIP achieves much lower total latency and is more flexible than other approaches.

It is challenging to conduct quality of experience (QoE) evaluations of web-based streaming video services effectively and efficiently. Aiming to overcome this challenge, we have created QoECenter, a web-based visual platform that innovatively facilitates comprehensive QoE evaluations of the streaming video services. QoECenter offers a holistic approach to conducting the QoE evaluations via an integrated set of technologies for source video classification, QoS realization of video encoding and network transmission, and context-aware user experience data gathering and analysis. From a QoECenter consumer's viewpoint, three kinds of data are required for an end-to-end streaming video QoE evaluation: video source level data, system process level data, and end user level data. QoECenter provides visual interfaces for parameter setting and data acquisition for each data level, and supports both objective and subjective datadriven QoE analyses. A QoECenter consumer can easily conduct comparative QoE evaluations like running easy-to-use visual applications. The effectiveness and efficiency design objectives of QoECenter have been validated by various real experiments.

Mobility profiles of users play a crucial role in a wide range of context-aware computing and services. Travel mode choice, as a representative feature of mobility profiles, is one of the important components in travel demand and future planning of transportation systems. Transportation mode choice has been widely studied based on the random utility model and decision making methods which haven't considered the correlation among features influencing transportation mode choice. This paper presents a data driven model to analyze transportation mode choice given transportation information. The contributions of this paper lie in the following two aspects. On one hand, we propose a travel mode choice model considering the correlation among influencing features of mode. And the relevant features related to the mode choice are redefined and considered to improve the final efficiency and effectiveness. On the other hand, we propose a directed-graph-guided fused lasso method to depict the correlation rules among features. The lasso method can reduce the redundant information to improve the speed of convergence and accuracy of analysis. Three different models namely standard lasso, graph-guided fused lasso and spatio-functionally weighted regression based models, are compared with our model and tested with the GPS trajectory data in Beijing. As a result, we achieved better performance than other compared models.

We investigate the problem of physical layer security in a wireless cooperative network, where communication is assisted by the untrusted relay. It is impossible for the amplify-and-forward protocol to convey a confidential message from the source to the destination without the help of other nodes, where only an untrusted relay is available. Nonetheless, our results are quite optimistic when there are multiple untrusted relays, which amplify the received signal and forward it to the destination. We treat the untrusted relay nodes as eavesdroppers, despite the fact that they are the enablers of communication. First of all, we prove that a positive secrecy rate can always be ensured regardless of the transmit power and the channel condition of the untrusted relays, as long as there exists a sufficient number of untrusted relays. Furthermore, a closed-form solution to the upper bound of secrecy rate is obtained. Finally, a practical approach is proposed to acquire a positive secrecy rate, which brings nearly no extra communication cost. Simulations are conducted to demonstrate the validity of the proposed approach.

Most state-of-the-art service selection approaches assume user preferences can be provided by the target user with sufficient precision and ignore historical service usage data for all users. It is desirable for ordinary users to possess a new service selection approach that can recommend satisfactory services to them even when their service selection preferences are specified imprecisely in terms of vagueness, inaccuracy, and incompleteness. This paper proposes a novel service selection approach that resolves the imprecise characteristics of user preferences and can recommend satisfactory services for users with varying cognitive levels in terms of service experience. The proposed service selection approach is comprised of four major tasks: 1) employ user-friendly linguistic variables to collect apparent user preferences (AUP) and convert the linguistic variables to standardized fuzzy weights as AUP weights; 2) evaluate all users' respective cognitive levels for the target service type and obtain the cognitive level threshold for that type of services; 3) adjust the AUP weights based on the calculated cognitive levels and the threshold, and supplement the potential user preferences weights; and 4) prioritize candidate services per a user satisfaction maximization objective. In-depth comparative experimental evaluations were performed using two real-world datasets. The results show that our service selection model outperforms three other representative ones and could provide a stable and reliable selection of services for the users with low service cognitive levels.

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, Web usage mining, bioinformatics, etc. Given a database, where each transaction contains a set of items, FIM tries to find itemsets that occur in transactions more frequently than a given threshold. Despite valuable insights the discovery of frequent itemsets can potentially provide, if the data is sensitive (e.g., web browsing history and medical records), releasing the discovered frequent itemsets might pose considerable threats to individual privacy.

Given a service composition, it is challenging but important to have a runtime adaptation, due to the complicated execution environment and evolving feature of Web service. In this paper, we present a runtime adaptive service composition approach, taking execution time minimization and revenue maximization into consideration. Based on dynamic programming, we deduce the optimal policy. Through this policy, orchestrator selects one concrete service for per task on runtime. The experimental results show that the proposed approach outperforms previous approach.

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.

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.

Many factors affect the time cost of Cloud computing tasks. One of the most serious factors is data transmission latency, which reduces the efficiency of Cloud computing. Existing notable schemes ignore the communication cost among virtual machines (VMs) in the MapReduce environment. In this paper, we propose a VM placement approach to reduce data transmission latency with the communication cost among VMs. We first construct bipartite graph and classify VMs as two groups according to their transmission latency with data nodes. Then we propose two VM placement optimization algorithms to minimize the total data transmission latency (TDTL) and the maximum data transmission latency (MDTL) in the MapReduce environment. Finally, we place VMs for Reduce phase. The evaluation results show that our approach reduces the average data transmission latency by 26.3% compared with other approaches.

In vehicular sensor networks, probe vehicles can act as mobile sensors to monitor physical world and report to an urban sensing center. However, the distribution of probe vehicles is uneven over space and time. Data redundancy and vacancy are common phenomena for different spatiotemporal positions, which seriously degrade sensing efficiency and accuracy. To address this issue, we propose an adaptive and compressive data gathering scheme (AC-Sense) based on matrix completion theory. The scheme adaptively determines the locations where to obtain samples from so that the principal features of physical world can be captured with a reduced number of probe vehicles. The spatio-temporal correlation between sensor data is exploited to estimate the un-sampled data. Furthermore, we introduce a feedback mechanism to stabilize sensing performance according to the evaluation of data error. We perform extensive experiments based on real taxicab mobility traces and air quality data in Beijing. The experimental results show that the proposed scheme largely improves sensing efficiency while ensuring required data quality.

Fast deployment of mobile Internet makes Web services often consumed under a multi-dimensional spatiotemporal model, wherein a specific service client could keep active while its location is changing. Recommending Web services for such clients must be able to predict unknown QoS values with the target client's service requesting time and location taken into account, e.g., Performing the prediction via a set of measured multi-dimensional QoS data. Most QoS prediction methods focus on the QoS characteristics for one specific dimension, e.g., Time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient service recommendation for Web service based mobile clients via tensor decomposition and reconstruction optimization algorithms. Comparative experimental evaluation results show that the proposed QoS prediction approach could result in much better accuracy in recommending Web services than several other representative ones.

Exiting schemes rarely take into account coordinated problem among multiple virtual machines (VMs) which collectively complete a task, since if these VMs are uncoordinated, the execution results of a task are not correct. To solve the problem, we first exploit a proactive prediction scheme to predict the VM's status. Then, if VM's status is deteriorating, coordinated checkpoint is adopted to suspend the current task to search an optimal target host. Finally, we introduce an efficient heuristic algorithm to solve the optimal target host selection problem. The experimental results demonstrate the efficiency and effectiveness of our proposed approach.

Abstract Mashups, which use multiple sources to create a new service, emerged as an evolution of Web 2.0. However, scalable access control for mashups is difficult. To enable a mashup to gather data from legacy applications and services, users must obey as the mashup host orders. These orders are created without any standard or limits about the privacy protection. This authorization approach violated the principle of least privilege and leaves users vulnerable to misuse of their private information by malicious mashups. To overcome the limitations, we introduce the privacy‐preserving authorization method for mashups, which encapsulates the data of backend services with different private sensitivity degrees before the authorization process executes. We also give the data–user relationship model to make standard for backend services when defining private sensitivity degrees of users' data. In this progress, standard encapsulation file and authorization file are created successively. In the end, the authorization steps, which could be set stored for regular use of the mashups, are created based on the authorization mechanism and authorization file. The proposed method mainly focuses on the users and backend services, which are the real data owners. Through this method, users have the ability to observe and control the data involved in the mashup, and the backend services can take the responsibility of their users' private information protecting. In the end of the paper, the application example and a series of experimental study are given to demonstrate the feasibility and efficiency of this method. Copyright © 2015 John Wiley & Sons, Ltd.

Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.

Web service recommendation systems can help service users to locate the right service from the large number of available web services. Avoiding recommending dishonest or unsatisfactory services is a fundamental research problem in the design of web service recommendation systems. Reputation of web services is a widely-employed metric that determines whether the service should be recommended to a user. The service reputation score is usually calculated using feedback ratings provided by users. Although the reputation measurement of web service has been studied in the recent literature, existing malicious and subjective user feedback ratings often lead to a bias that degrades the performance of the service recommendation system. In this paper, we propose a novel reputation measurement approach for web service recommendations. We first detect malicious feedback ratings by adopting the cumulative sum control chart, and then we reduce the effect of subjective user feedback preferences employing the Pearson Correlation Coefficient. Moreover, in order to defend malicious feedback ratings, we propose a malicious feedback rating prevention scheme employing Bloom filtering to enhance the recommendation performance. Extensive experiments are conducted by employing a real feedback rating data set with 1.5 million web service invocation records. The experimental results show that our proposed measurement approach can reduce the deviation of the reputation measurement and enhance the success ratio of the web service recommendation.

How to select the optimal composited service from a set of functionally equivalent services but different QoS attributes has become a hot research in service computing. However existing approaches are inefficient as they search all solution spaces. More importantly, they neglect the QoS inherently uncertainty due to the dynamic network environment. In this paper, we propose a fast and reliable Web service selection approach that attempts to select the best reliable composited service on the basis of filtering low reliable Web services according to the uncertainty of QoS. The approach first employs information theory and variance theory to abandon high QoS uncertainty services and downsize the solution spaces. A reliability fitness function is then designed to select the best reliable service for composited services. We experimented with real-world and synthetic datasets and compared our approach with other approaches. Our results show that our approach is not only fast, but also find more reliable composited services.

In the Web services environment, RBAC (role-based access control) model is widely accepted as an efficient approach to manage the access control. By defining the authorization relationship between subject roles and object roles in the RBAC, authorization policies are utilized to simplify the authorization management on different Web services. But the scalability and complexity of composite Web services may cause authorization policy conflict. A new authorization policy added to the system may conflict with existing ones and result in authorization chaos and authorization leaking. And when implemented in the composite Web services, policy conflict detection would be of high cost with manually checking. That makes automatic policy conflict detection important to ensure the security of authorizations in the composited Web services. This paper analyzes the features of the authorization policy in the CWS-RBAC (RBAC for composite Web services) and presents methods of detecting policy conflict including subject role propagation conflict, object role composition conflict and context conflict. The experiment designed is to validate the efficiency of each conflict detection method.

As cloud computing becomes widely deployed, more and more cloud services are offered to end users in a pay-as-you-go manner. Today’s increasing number of end user-oriented cloud services are generally operated by Software as a Service (SaaS) providers using rental virtual resources from third-party infrastructure vendors. As far as SaaS providers are concerned, how to process the dynamic user service requests more cost-effectively without any SLA violation is an intractable problem. To deal with this challenge, we first establish a cloud service request model with SLA constraints, and then present a cost-aware service request scheduling approach based on genetic algorithm. According to the personalized features of user requests and the current system load, our approach can not only lease and reuse virtual resources on demand to achieve optimal scheduling of dynamic cloud service requests in reasonable time, but can also minimize the rental cost of the overall infrastructure for maximizing SaaS providers ’ profits while meeting SLA constraints. The comparison of simulation experiments indicates that our proposed approach outperforms other revenue-aware algorithms in terms of virtual resource utilization, rate of return on investment and operation profit and provides a cost-effective solution for service request scheduling in cloud computing environments.

Abstract Denial‐of‐Service attacks continue to plague the Internet. Tracing an individual attack packet to its origin is an important step in defending against these attacks. For this reason, researchers have proposed several approaches for single‐packet IP traceback. Packet logging is a generic technique in these methods, which results in the high overhead at routers and low traceback accuracy. In this paper, we propose a novel path‐based approach for single‐packet IP traceback. Our approach makes use of the routing paths to set up traceback paths, instead of packet logging, so as to improve single‐packet IP traceback in several dimensions: (i) our storage overhead is only related to the number of routing paths, no matter how many packets traverse on them; (ii) the number of queried routers during the traceback process is only related to the number of hops in the attack path; (iii) the false positives in attack‐path construction can be negligible. We perform extensive mathematical analysis and simulations to evaluate our approach. The results show that our approach represents a step forward in preciseness and efficiency compared with the previous work. Copyright © 2013 John Wiley & Sons, Ltd.

Blocking attack flows to protect the threatened resources is a necessary step in defending against the Distributed Denial-of-Service DDoS attacks. Two kinds of reactive packet filtering technologies have been proposed as close to victim-end filtering and close to source-ends filtering. The first scheme only involves a single Active Filtering Routers AFRs but damages the whole network bandwidth resource; another extreme scheme requires millions of AFRs and thus degrades the network transmission performance, but it has the best defense effect. A feasible scheme should use a certain quantity of AFRs to filter attack flows between the victim end and the source ends. Going one step further, in this paper, we make the first effort on studying the filtering location to maximize the protected network bandwidth while not permitting any attack flow to reach the victim. We formulate this problem to an integer linear programming problem and design an efficient heuristic filtering location algorithm. We evaluate our algorithm through integrating it into the existing filtering architecture and implementing this integration scheme on the emulated DDoS scenarios based on real-world Internet topology. Our evaluation results show that compared to the state-of-the-art source-ends filtering scheme Active Internet Traffic Filtering, this integration scheme only uses 20% of its AFRs to achieve more than 70% of its protection effect. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In recent years, large amounts of graph‐structured data have been outsourced to the commercial public cloud. It is a crucial requirement to enable substructure similarity query for effective data retrieval. However, for protecting data privacy, sensitive data have to be encrypted before outsourcing, which impedes the traditional similarity query schemes from being supported in cloud. Most existing works on encrypted cloud data retrieval pay little attention to this problem. Additionally, considering the huge amounts of encrypted data graphs, the complicated similarity computation and privacy requirements, it is particularly challenging to solve this problem effectively. In this paper, for the first time, we investigate the problem of privacy‐assured substructure similarity query over encrypted graph‐structured data in cloud computing. Our solution explores a secure framework and a series of secure algorithms to efficiently perform the substructure similarity query without privacy breaches. The proposed solution first builds a secure feature‐graph index to represent the feature‐related information about each encrypted data graph based on privacy homomorphism and obscuration methods and then calculates the similarity between the query graph and each data graph by the difference of feature frequency in a privacy‐preserving manner. Thorough analysis is given to investigate effectiveness and privacy guarantees, and the experiments with real dataset further demonstrate the validity and efficiency of the proposed solution. Copyright © 2013 John Wiley & Sons, Ltd.

To maximize the profit of cloud service provider, we present a dynamic virtual resource renting method under SLA (service level agreement) constraints. According to the price distribution and current task emergency, the method attempts to adjust acceptable price of each virtual resource type at different price interval. If there is price acceptable resource, we choose to rent the most profitable one. Otherwise, if SLA permits, we even suspend the task and restart it when price falls. Partial experimental result in simulation environment is also presented.

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.

With the rapid development of Cloud computing, more and more service providers could provide cloud services (applications) to users. Faced with mass Cloud services, trust and reputation mechanisms offer a promising way to solve the trust evaluation of Cloud services. Hence, trust and reputation play an important role in evaluating of Cloud services. In this paper, we propose a lightweight reputation measurement approach for Cloud services based on (user) feedback ratings. The proposed approach first adopts cloud model to obtain the trust vector of each cloud service by exploiting feedback ratings. The trust vector consists of Expected value, Entropy value and Hyper-Entropy value. Then we use fuzzy set theory to calculate the reputation scores of Cloud services. Simulation results show that the proposed approach is significantly effective for unstable feedback ratings.

With an increasing number of cloud service providers (CSP) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing methods are difficult to solve the problem because they do not make full use of temporal price differences. This paper introduces a dynamic virtual resource renting method that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. We first pretreat the historical price series and adopt the outlier detection technique to filter the extreme price. Then, considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We provide an extensive evaluation of our method using Amazon EC2 spot price dataset and normally distributed price dataset. The results demonstrate the effectiveness of our method.

Most single-packet IP trace back approaches that have been proposed demand routers to log the packet digests to trace back, which lead to the linear growth of the storage overhead as the forwarded packets are increasing. This paper proposes a novel single-packet IP trace back approach based on the routing path to alleviate the burden of routers. Our approach introduces the relevant theories of label switching path in Multi-Protocol Label Switching (MPLS) and makes use of routing path to set up a Trace back Path (TP). During the trace back process, we can reconstruct the attack path on the basis of label switching mechanism. We use mathematical analysis and simulations to evaluate our approach. Our evaluation results show that compared to HIT and SPIE, two state-of-art single-packet trace back approaches, the storage overhead in our approach is only related to the number of routing paths, no matter how many packets traverse on the paths, and the number of queried routers during the trace back process is only related to the number of hops in an attack path.

ABSTRACT SYN flooding attacks are a common type of distributed denial‐of‐service attacks. Up to now, many defense schemes have been proposed against SYN flooding attacks. Traditional defense schemes rely on passively sniffing an attacking signature and are inaccurate in the early stages of an attack. These schemes are effective only at the later stages when attacking signatures are obvious. In this paper, we propose a detection approach that makes use of SYN traffic prediction to determine whether SYN flooding attacks happen at the early stage. We firstly adopt grey prediction model to predict SYN traffic, and then, we employ cumulative sum algorithm to detect SYN flooding attack traffic among forecasted SYN traffic. Trace‐driven simulation results demonstrate that our proposed detection approach can detect SYN flooding attacks effectively. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Trusted quality of Web service (QoWS) issue is critical for e‐commerce applications. However, many existing studies have little work in situations that have insufficient or no historical information regarding QoWS data. In this study, we propose a trusted QoWS metric approach, that is, Bayesian Approach with Maximum Entropy Principle. The key of our proposed approach is to extract QoWS prior distribution of Web service by using Maximum Entropy Principle and then to infer QoWS posterior distribution of Web service by using Bayesian Approach. On the basis of the obtained QoWS posterior distribution, trusted QoWS can be measured. We conduct extensive simulations to evaluate our proposed approach. The simulation results demonstrate that our proposed approach can obtain trusted QoWS effectively. Copyright © 2012 John Wiley & Sons, Ltd.

In order to obtain trustworthy QoS (Quality of Service) in situations which have little or no information regarding a service's QoS, we propose a QoS measure approach, namely Bayesian Approach with Maximum-Entropy Principle (BA-MEP). BA-MEP firstly extracts the QoS prior distribution from the objective data (such as historical statistics data) and subjective data (such as the service providers and QoS experts) by Maximum Entropy Principle, and then Bayesian Approach is used to infer the QoS posterior distribution, finally, trustworthy QoS can be obtained from the QoS model. In addition, we also propose a trustworthy expert algorithm (TEA) and analysis three reasons that the QoS data of Web services are not always true. Some experiments are illustrated to show the effectiveness of BA-MEP.

In ubiquitous network environment, network and resources change frequently, the capabilities of devices are quite different. All these features lead to local characteristics of QoS (quality of service). In addition, the centralized service registry and service selection methods cannot adapt to ubiquitous network environment. Therefore, this paper presents a decentralized Web services selection algorithm based on multi-attribute group decision making (MAGDM) theory (DWSSA_MAGDM). Firstly the definitions of the heterogeneous user's and service's context ontology are given. Then a service optimal selection algorithm is given. The algorithm includes standardization of the heterogeneous decision-making matrix, aggregation of the QoS evaluation and synthetically evaluation. Experimental results show that the DWSSA_MAGDM works well for ubiquitous network environment.

This paper presents a novel Web service composition algorithm based on TOPSIS (WSC_TOPSIS) to solve the service composition difficulties with multiple decision-makers and heterogeneous QoS for the first time. It includes three main steps: normalizing decision matrix, evaluating alternatives synthetically and evaluating group alternatives synthetically. Experimental results show that the proposed algorithm can better support Web service composition with heterogeneous QoS data and multiple decision-makers.

Free-riding phenomenon is overwhelming in nowadays P2P network which causes researchers to investigate and develop many approaches to combat it. However, almost all the studies neglect the role of relative contribution of peers, namely willingness of contribution (WoC) in our paper. The ignorant to the ratio of peer's actually contribution to its physical capability would undoubtedly lead to unfairly resource allocation and discourage many peers, as well as loose their support in long term. Therefore, in this paper we present a novel and effective approach Cowtra to address such problem. Our algorithm guarantees that the bandwidth of a source node is distributed properly according to the absolute contribution of the competing peers. Then we adjust the amount of competing peers' received bandwidth by utilizing the WoC in the second phase, such that peers with higher WoC obtain more resource while peers with lower WoC otherwise. At last, simulation results demonstrate the superiority of our algorithm in terms of fairness and efficiency.

As it is known, service is the key element of the new generation telecommunication network. Though network technology develops so quickly, the technology that uses in the application remains in a low degree. In other hand, current service model is also a problem that prevent telecom application from developing.Meanwhile, people demand for more personal experiences, such as context aware demand. In this paper, the author will analyze current problems in service platform and then propose a new platform with social factors and ubiquitous feature. And an example application will set up to validate the framework.

Quality of Service (QoS) characterizes the non-functional aspects of Web service, which is the key factor for satisfying service users and has to be carefully considered in service oriented architecture. There has been a lot of works on QoS description and service selection by QoS information. However, two issues have not been substantially explored in QoS management-how to collect QoS information from running services, and how to dynamically adapt service provisioning with run-time QoS information. Moreover, current monitoring approaches are focused on monitoring the service interaction in a single domain, while cross-domain monitoring has not been studied yet. In this paper, we present a policy-driven monitoring framework for collecting QoS information and adapting service provisioning for cross-domain service interaction. The monitor is built on our distributed QoS registry-Q-Peer. The monitor is user-centric, metric-oriented, feedback-enabled and loosely-coupled with service providers. Services are observed by capturing interaction messages, so the monitoring approach has no impact to service providers. The framework supports dynamic configuration of monitoring metrics for both single service and cross-domain composite service. It can be used as a reliable third-party QoS monitor for Web services.

This paper compares disease with network vulnerability by their definitions and characteristics. A mapping between disease and vulnerability is built based on their similarities. We put forward a novel model of vulnerabilities in computer networks by simulating the reverse of cause-result of disease. Based on the model, a quantitative metric for vulnerabilities of computer networks is presented. The complexity of the algorithm for computing the metric is O(|V|2X|S|), where V and S stand for set of vulnerabilities and set of network states. By analyzing different structures of the vulnerability model, we found that the value reflecting vulnerability decreases when the model is more linear.

Fault tolerance (FT) is a feature of top importance in the long-life real-time systems. The service control point (SCP) in the intelligent network (IN) is just one of this type of systems. Its dependability affects directly the quality of service (QoS) of the whole network. We give an overview on the software fault tolerance (SFT) at first, and decide to adopt the time-redundancy approach to implement the SFT of the SCP according to the special features of the IN application. Based on the processing mechanism of IN calls and with the consideration of the load-balance, software fault-tolerance and software upgrading on-line, we present a two-level concurrency model of the SCP software, and give our implementation of this model in the multi-task environment. Furthermore, we discuss in detail the issues of the SFT based on this model, and give the corresponding strategies and algorithms to achieve the specified whole FT targets. Finally, we discuss the system effectiveness of the SCP software, and provide a method of computing the effectiveness of the SCP software based on the Markov model.