Getting your big data priorities straight: a demonstration of priority-based QoS using social-network-driven stock recommendation

As we come to terms with various big data challenges, one vital issue remains largely untouched. That is the optimal multiplexing and prioritization of different big data applications sharing the same underlying infrastructure, for example, a public cloud platform. Given these demanding applications and the necessary practice to avoid over-provisioning, resource contention between applications is inevitable. Priority must be given to important applications (or sub workloads in an application) in these circumstances. This demo highlights the compelling impact prioritization could make, using an example application that recommends promising combinations of stocks to purchase based on relevant Twitter sentiment. The application consists of a batch job and an interactive query, ran simultaneously. Our underlying solution provides a unique capability to identify and differentiate application workloads throughout a complex big data platform. Its current implementation is based on Apache Hadoop and the IBM GPFS distributed storage system. The demo showcases the superior interactive query performance achievable by prioritizing its workloads and thereby avoiding I/O bandwidth contention. The query time is 3.6 × better compared to no prioritization. Such a performance is within 0.3% of that of an idealistic system where the query runs without contention. The demo is conducted on around 3 months of Twitter data, pertinent to the S & P 100 index, with about 4 × 1012 potential stock combinations considered.