Cloud computing, a novel distributed paradigm to provide powerful computing capabilities, is usually adopted by developers and researchers to execute complicated IoT applications such as complex workflows. In this scenario, it is fundamentally important to make an effective and efficient workflow application scheduling and execution by fully utilizing the advantages of the cloud (as virtualization and elastic services). However, in the current stage, there is relatively few research for workflow scheduling in cloud environment, where they usually just bring the traditional methods directly into cloud. Without considering the features of cloud, it may raise two kinds of problems: (1) The traditional methods mainly focus on static resource provision, which will cause the waste of resources; (2) They usually ignore the performance fluctuation of virtual machines on the physical machines, therefore it will lead to the estimation error of task execution time. To address these problems, a novel mechanism which can estimate the probability distribution of subtask execution time based on background VM load series over physical machines is proposed. An elastic performance fluctuations-aware stochastic scheduling algorithm is introduced in this paper. The experiments show that our proposed algorithm can outperform the existing algorithms in several metrics and can relieve the influence of performance fluctuations brought by the dynamic nature of cloud.

Provisioning multiple paths can improve fault tolerance and transport capability of multi-routing in wireless networks. Disjoint paths can improve the diversity of paths and further reduce the risk of simultaneous link failure and network congestion. In this paper we first address a many-to-one disjoint-path problem (MOND) for multi-path routing in a multi-hop wireless network. The objective of this problem is to maximize the minimum number of disjoint paths of every source to the destination. We prove that it is NP-hard to obtain k disjoint paths for every source when k ≥ 3. To solve this problem efficiently, we propose a heuristic algorithm called TOMAN based on network flow theory. Experimental results demonstrate that it outperforms three related algorithms.