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In IaaS Cloud, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers. The existing VM scheduling schemes propose optimize PMs or network resources utilization, but few of them attempt to improve the energy effciency of these two kinds of resources simultaneously. This paper proposes a VM scheduling scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption. Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem. Accordingly, we design a two-stage heuristic algorithm to solve the issue, and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.

It is important for mobile robot to match features from scenes under wide baseline views. Wide baseline matching is one of the difficult problems in feature matching because large variation between the image appearances occurs due to illumination and viewpoints, especially on non-planar and less-textured scenes. In this paper we propose a method of feature matching under large separate views, which is based on the multiple hypotheses about the local transformation and multiple routes propagation. Given a single initial correspondence and the local affine transformation between the neighborhoods around them, the proposed method can supply more stable and accurate matches under wide baseline views. The matches can be propagated adaptively into the regions where the local transformations differ from the initial one. The experiments with real data show that the proposed method achieves the state-of-the art performance among match propagation algorithm.

Ant colony algorithm is an efficient intelligent algorithm to solve NP hard problem. This paper presents a parallel computing solution based on General Purpose GPU (GPGPU) to solve Traveling Salesman Problem (TSP) with Max-Min Ant System (MMAS). The experimental result shows it is more efficient than pure CPU computing.