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This article presents a report on APNOMS 2008, which was held October 22–24, 2008 in Beijing, China. The theme of APNOMS 2008 was “Challenges for Next Generation Network Operations and Service Management.” [ABSTRACT FROM AUTHOR]

Location spoof detection is a major component of location proofing mechanisms in internet of things (IoT), and it is significant for the system to assess the trustworthiness of the location data associated with the user. Unlike the work that employs physical layer features, we interest in building the infrastructure for a solution to establish location spoofing detection capabilities in blockchain-based IoT systems. In detail, at the node and the mobile trajectory level, we create an IoT system for evaluating the trustworthiness of location proofs with blockchain location system features. A blockchain-based multilayer fuzzy hierarchical analysis process (AHP) evaluation method is contemplated to detect location spoofing in the IoT system. Simulation results indicate the proposed method has a superior performance and provides a basis for the trustworthiness assessment of location proofs. [ABSTRACT FROM AUTHOR]

SUMMARY Coalition is an essential mechanism in the multi-agent systems in the research of task-oriented area. Self-interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering-based coalition formation and self-adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX-MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self-repairing autonomously. The stimulus-response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We address the problem of loss rates inference from end-to-end unicast measurements. Like other network tomography problems, it requires solving a system of equations that involve measurement values and the loss rates of links. However, the equations do not have a unique solution in general. One kind of method imposes unrealistic assumption on the system, e.g. the uniform prior probability of a link being congested. Other methods use multiple probe measurements to acquire more information about the system that may generate many additional overhead costs. In this paper, we demonstrate that a considerable portion (more than 95 %) of links could be uniquely identified by current measurements directly. Then we utilize the information of these determined links to acquire the global distribution of the system that can help to infer the rest loss rates. Moreover, we derive an upper bound on the accuracy of a congestion localization problem using the Bayesian network that provides a necessary condition for achieving the 0- error diagnosis. Finally, we evaluate our new method and a former representative method by both the simulation and the real implementation in the PlanetLab network. The results show that our method not only makes a great improvement on the accuracy, but also reduces the probe costs and the running time to an extremely low level. Furthermore, our method can also perform well in large and more congested networks. [ABSTRACT FROM AUTHOR]

SUMMARY As the computer network increasingly grows larger and more complex, fault diagnosis has become a challenging task. Active probing is an efficient tool for fault localization. By implementing some test programs and analyzing the results, active-probing-based techniques can perform diagnosis efficiently and adaptively. Because probes may generate additional traffic overhead, it is important to appropriately select small number of probes to reach the desired diagnostic capability. However, the computation of probe selection problem in such environment is extremely expensive. Most of the past works purchase the speed at the cost of diagnostic accuracy. In this paper, we first verify that probe selection problem satisfies the property of submodularity. Then we take the use of the property and develop a submodularity-based selection algorithm with following novel features: (i) it is cost effective, failure resistant and more accurate; (ii) it could deal with the uncertainties about the network structures and the observations; and (iii) it can select the required probes in near-linear time. Finally, we implement submodularity-based selection algorithm and other two representative probe selection algorithms (bounded path enumeration approximation algorithm and greedy search algorithm) on different settings of networks. The results have shown how the new algorithm outperforms the former two algorithms. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement. [ABSTRACT FROM AUTHOR]

In-network computing indicates the convergence of computing and network, which can be further considered as computing-based networking and network-enabled computing, from which comes the edge in-network computing paradigm. In this newly proposed paradigm, the computing, caching, and communication resources are deployed on in-transit edge nodes, such as routers or switches; hence, services are provided with less delay and energy consumption by the deep aggregation of edge-side resources. However, open challenges still exist in dealing with heterogeneous resource sharing, scheduling, and ensuring credibility of service processing. Given this background, this article aims to address the edge-side heterogeneous resource trust management in multi-domain scenarios. A multi-domain heterogeneous resource trust management architecture is first constructed to achieve trusted resource sharing and transaction. Then, under the proposed architecture, a smart-contract-based resource sharing mechanism is formulated, including an incentive mechanism based on end-side node contribution. Furthermore, a differential-evolution-enabled containerized microservice orchestration algorithm is finally designed to minimize operation delay and load imbalance during the container deployment. The application of this platform in a highly elastic power grid scenario has been tested as an example. [ABSTRACT FROM AUTHOR]

With the rapid development of smart city and 5C, user demand for Internet services has increased exponentially. Through collaborative content sharing, the storage limitation of a single edge server (ES) can be broken. However, when mobile users need to download the whole content through multiple regions, independently deciding the caching content for ESs in different regions may result in redundant caching. Furthermore, frequent switching of communication connection during user movement also causes retransmission delay. As a revolutionary approach in the artificial intelligence field, deep reinforcement learning (DRL) has earned great success in solving high-dimensional and network resource management related problems. Therefore, we integrate collaborative caching and DRL to build an intelligent edge caching framework, so as to realize collaborative caching between cloud and ESs. In this caching framework, a fed-erated-machine-learning-based user behavior prediction model is first designed to characterize the content preference and movement trajectory of mobile users. Next, to achieve efficient resource aggregation of ESs, a user-behavior-aware dynamic collaborative caching domain (DCCD) construction and management mechanism is devised to divide ESs into clusters, select cluster heads, and set the re-clustering rules. Then a DRL-based content caching and delivery algorithm is presented to decide the caching content of ESs in a DCCD from a global perspective and plan the transmission path for users, which reduces redundant content and transmission delay. Especially when a user request cannot be satisfied by the current DCCD, a cross-domain content delivery strategy is presented to allow ESs in other DCCDs to provide and forward content to the user, avoiding the traffic pressure and delay caused by requesting services from cloud. The simulation results show that the proposed collaborative caching framework can improve user satisfaction in terms of content hit rate and service delay. [ABSTRACT FROM AUTHOR]

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. In this work, we propose a correlated failure prediction approach (CFPA) to predict correlated failures of computing elements in DCSs. The approach models correlated-failure patterns using the concept of probabilistic shared risk groups and makes a prediction for correlated failures by exploiting an association rule mining approach in a parallel way. We conduct extensive experiments to evaluate the feasibility and effectiveness of CFPA using both failure traces from Los Alamos National Lab and simulated datasets. The experimental results show that the proposed approach outperforms other approaches in both the failure prediction performance and the execution time, and can potentially provide better prediction performance in a larger system. [ABSTRACT FROM AUTHOR]

To adapt the 5G communication, the cloud radio access network is a paradigm introduced by operators which aggregates all base stations computational resources into a cloud BBU pool. The interaction between RRH and BBU or resource schedule among BBUs in cloud have become more frequent and complex with the development of system scale and user requirement. It can promote the networking demand among RRHs and BBUs, and force to form elastic optical fiber switching and networking. In such network, multiple stratum resources of radio, optical and BBU processing unit have interweaved with each other. In this paper, we propose a novel multiple stratum optimization (MSO) architecture for cloud-based radio over optical fiber networks (C-RoFN) with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. MSO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation and path provisioning latency. [ABSTRACT FROM AUTHOR]

In wireless sensor network, data loss is inevitable due to its inherent characteristics. This phenomenon is even serious in some situation which brings a big challenge to the applications of sensor data. However, the traditional data estimation methods can not be directly used in wireless sensor network and existing estimation algorithms fail to provide a satisfactory accuracy or have high complexity. To address this problem, Temporal and Spatial Correlation Algorithm (TSCA) is proposed to estimate missing data as accurately as possible in this paper. Firstly, it saves all the data sensed at the same time as a time series, and the most relevant series are selected as the analysis sample, which improves efficiency and accuracy of the algorithm significantly. Secondly, it estimates missing values from temporal and spatial dimensions. Different weights are assigned to these two dimensions. Thirdly, there are two strategies to deal with severe data loss, which improves the applicability of the algorithm. Simulation results on different sensor datasets verify that the proposed approach outperforms existing solutions in terms of estimation accuracy. [ABSTRACT FROM AUTHOR]

Wireless sensor network based on service-oriented architecture has provided important support for Internet of Things in smart city; therefore, multi-requests can be satisfied through service composition. However, as the number and complexity of requests increase, the dynamic and low energy of sensors in wireless sensor network gradually limit the development of the service-oriented architecture in Internet of Things; therefore, optimizing energy under the condition that multi-requests can be satisfied in the dynamic wireless sensor network becomes an important problem. To address this challenge, first, this paper analyzes the mobility of sensors to predict the path stability and manage the information and capabilities on them so that we can find the satisfactory service provider candidates. Then, this article proposes a service mapping pre-process to achieve the work flow reduction in a service layer to save energy in advance and a service mapping process to find the optimal sensors in a network layer that can satisfy the requests. Simulation results show that our strategies can achieve the goal of energy conservation, load balancing, and network lifetime extension under the premise of guaranteeing requests function and non-function attributes and have better performance than traditional ones. [ABSTRACT FROM AUTHOR]