With the development of high-performance computing and the expansion of large-scale multiprocessor systems, it is significant to study the reliability of systems. Probabilistic fault diagnosis is of practical value to the reliability analysis of multiprocessor systems. In this paper, we design a linear time diagnosis algorithm with the multiprocessor system whose threshold is set to 3, where the probability that any node is correctly diagnosed in the discrete state can be calculated. Furthermore, we give the probabilities that all nodes of a d-regular and d-connected graph can be correctly diagnosed in the continuous state under the Weibull fault distribution and the Chi-square fault distribution. We prove that they approach to 1, which implies that our diagnosis algorithm can correctly diagnose almost all nodes of the graph.

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network edge. However, current computation offloading studies in the heterogeneous edge environment face multifaceted challenges: Dependencies among computational tasks, resource competition among multiple users, and diverse long-term objectives. Mobile applications typically consist of several functionalities, and one huge category of the applications can be viewed as a series of sequential tasks. In this study, we first proposed a novel multiuser computation offloading framework for long-term sequential tasks. Then, we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading problem. Moreover, we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve them. We further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

BCube is one kind of important data center networks. Hamiltonicity and Hamiltonian connectivity have significant applications in communication networks. So far, there have been many results concerning fault-tolerant Hamiltonicity and fault-tolerant Hamiltonian connectivity in some data center networks. However, these results only consider faulty edges and faulty servers. In this paper, we study the fault-tolerant Hamiltonicity and the fault-tolerant Hamiltonian connectivity of BCube(n,k) under considering faulty servers, faulty links/edges, and faulty switches. For any integers n ≥ 2 and k ≥ 0, let BC_n,k be the logic structure of BCube(n,k) and F be the union of faulty elements of BC_n,k. Let f_v, f_e, and f_s be the number of faulty servers, faulty edges, and faulty switches of BCube(n,k), respectively. We show that BC_n,k − F is fault-tolerant Hamiltonian if f_v +f_e + (n − 1)f_s ≤ (n − 1)(k + 1) − 2 and BC_n,k −F is fault-tolerant Hamiltonian-connected if f_v + f_e + (n − 1)f_s ≤ (n − 1)(k + 1) − 3. To the best of our knowledge, this paper is the first work which takes faulty switches into account to study the fault-tolerant Hamiltonicity and the fault-tolerant Hamiltonian connectivity in data center networks.

As users increasingly befriend others and interact online via their social media accounts, online social networks (OSNs) are expanding rapidly. Confronted with the big data generated by users, it is imperative that data storage be distributed, scalable, and cost-efficient. Yet one of the most significant challenges about this topic is determining how to minimize the cost without deteriorating system performance. Although many storage systems use the distributed key value store, it cannot be directly applied to OSN storage systems. And because users’ data are highly correlated, hash storage leads to frequent inter-server communications, and the high inter-server traffic costs decrease the OSN storage system’s scalability. Previous studies proposed conducting network partitioning and data replication based on social graphs. However, data replication increases storage costs and impacts traffic costs. Here, we consider how to minimize costs from the perspective of data storage, by combining partitioning and replication. Our cost-efficient data storage approach supports scalable OSN storage systems. The proposed approach co-locates frequently interactive users together by conducting partitioning and replication simultaneously while meeting load-balancing constraints. Extensive experiments are undertaken on two realworld traces, and the results show that our approach achieves lower cost compared with state-of-the-art approaches. Thus we conclude that our approach enables economic and scalable OSN data storage.