HaaS: Cloud-Based Real-Time Data Analytics with Heterogeneity-Aware Scheduling

Real-time data analytics has become increasingly important in modern times as many organizations and companies are generating and analyzing high volume of data constantly. Despite of the impressive technical development, it remains a challenging job to analyze the stream data effectively and efficiently because traditional hardware and software lack specific designs and optimizations for those emerging requirements. In this paper, we discuss our experience on real-time data analytics, with our in-house processing framework HaaS. HaaS is designed to exploit existing data analytics tools and libraries as well as distributed computing technologies to embrace heterogeneous computation resources in the cloud. HaaS utilizes hierarchical clustering to partition physical topology of clusters weighted with task topology information into densely connected sub-graphs. HaaS is also equipped with a heterogeneity-aware scheduling algorithm to facilitate holistic optimization over multiple running tasks with various service level agreements. To the best of our knowledge, HaaS is the first ever streaming analytical framework providing users with flexible and optimized usage with CPUs, GPUs and FPGAs in the cloud. Users with stream processing tasks can easily enjoy remarkable advantages of CPUs, GPUs and FPGAs in throughput, power consumption and monetary cost over others. In our empirical evaluations with highly diversified workloads, HaaS saves over 18% on power consumption and 24% on monetary cost over existing system design architecture, while the overall throughput of HaaS remains no lower than 90% of the theoretical limit.