With the benefit of virtualization technology and an ascending trend towards more cloud applications, virtual cluster (VC) allocation concerning different requirements of application deployment is vital to cloud datacenters and edge networks. Most of the state-of-the-art approaches for VC allocation problems are probability-based heuristics, and none of them absolutely outperforms any other approach in any case. Thus, it is necessary to collect and select effective and efficient approaches to jointly solve diverse and customizable VC allocation problems. This paper presents VCA-Solver, a VC allocation solver that can draw on collectively feasible schemes from multiple effective approaches. We describe the architecture of VCA-Solver and present a preliminary evaluation with a prototype implementation.

Mobile edge networks enhance cloud applications by offering reduced delay for mobile terminals. However, due to the cumulative increase of mobile terminals and applications, edge networks fail to accommodate all applications and are difficult to guarantee the end-to-end delay demand, which is an important metric of QoS. One potential solution to address this challenge is to place virtual clusters (VCs) in mobile micro-clouds (MMCs) to enhance the capabilities of edge networks and the cloud. Therefore, in this paper, we build the optimization model of VC placement in edges for maximizing the benefit function, with constraints of 1). the satisfaction for end-to-end delay demand of each application; 2). the resource capacity of each MMC no more than the whole demands of those VCs placed in. We use Stochastic Network Calculus (SNC) to build the delay model more accurately, considering stochastic arrivals of flows and stochastic services of service nodes. Moreover, we use Martingale Theory to enhance the solvability of the SNC-based delay model, achieving tighter delay bound reliability.

In this paper, we propose a system framework of Pub/Sub-based multimodal data transmission to mobile terminals. For various sensors, we classify the data in different topics, but transmit them in the uniform data channel, with different transmission mechanisms. In this case, mobile terminals receive data via different mechanisms, the push notification and request-response connection. The proposed framework makes the data transmission more efficient and flexible.