IEEE 2015: Bracer: A Distributed Broadcast Protocol In Multi-Hop Cognitive Radio Ad Hoc Networks With Collision Avoidance

Networking

Abstract: Broadcast is an important operation in wireless ad hoc networks where control information is usually propagated as broadcasts for the realization of most networking protocols. In traditional ad hoc networks, since the spectrum availability is uniform, broadcasts are delivered via a common channel which can be heard by all users in a network. However, in cognitive radio (CR) ad hoc networks, different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. In this paper, a fully-distributed Broadcast protocol in multi-hop Cognitive Radio ad hoc networks with collision avoidance, BRACER, is proposed. In our design, we consider practical scenarios that each unlicensed user is not assumed to be aware of the global network topology, the spectrum availability information of other users, and time synchronization information. By intelligently downsizing the original available channel set and designing the broadcasting sequences and scheduling schemes, our proposed broadcast protocol can provide very high successful broadcast ratio while achieving very short average broadcast delay. It can also avoid broadcast collisions. To the best of our knowledge, this is the first work that addresses the unique broadcasting challenges in multi-hop CR ad hoc networks with collision avoidance.

IEEE 2015: Deploying Large-Scale Datasets on-Demand in the Cloud: Treats and Tricks on Data Distribution

Big Data

Abstract: Public clouds have democratised the access to analytics for virtually any institution in the world. Virtual machines (VMs) can be provisioned on demand to crunch data after uploading into the VMs. While this task is trivial for a few tens of VMs, it becomes increasingly complex and time consuming when the scale grows to hundreds or thousands of VMs crunching tens or hundreds of TB. Moreover, the elapsed time comes at a price: the cost of provisioning VMs in the cloud and keeping them waiting to load the data. In this paper we present a big data provisioning service that incorporates hierarchical and peer-to-peer data distribution techniques to speed-up data loading into the VMs used for data processing. The system dynamically mutates the sources of the data for the VMs to speed-up data loading. We tested this solution with 1000 VMs and 100 TB of data, reducing time by at least 30 percent over current state of the art techniques. This dynamic topology mechanism is tightly coupled with classic declarative machine configuration techniques (the system takes a single high-level declarative configuration file and configures both software and data loading). Together, these two techniques simplify the deployment of big data in the cloud for end users who may not be experts in infrastructure management.