The shortest-path algorithm is one of the most important algorithms in geographical information systems. Bellman’s principle of optimization (BPO) is implicit in the shortest-path problem; that is, any involved node must be located in the simple paths between source and destination nodes. Unfortunately, BPO has never been explicitly used to exclude irrelevant nodes in existing methods, potentially leading to unnecessary searches among irrelevant nodes. To address this problem, we propose a BPO-based shortest-path acceleration algorithm (BSPA). In BSPA, a high-level graph is built to locate the necessary nodes and is used to partition the graph and divide a given task into independent subtasks. This allows the speed of any existing method to be improved using parallel computing. In a test using random graphs, on average, at most only 1.209% of the nodes need to be involved in the calculation. When compared with existing algorithms in real-world road networks, the BSPA shows faster preprocessing and query times, being respectively 118 and 463 times faster in the best case. In the worst case, they remain slightly faster.

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

Summary We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.