MiCA: Real-Time Mixed Compression Scheme for Large-Scale Distributed Monitoring

Real-time monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The scalability of fine-grained monitoring faces more and more severe challenges with scaling up distributed systems. The real-time compression which suppresses remote information update to reduce continuous monitoring cost is a promising approach to address the scalability problem. In this paper, we present the Linear Compression Algorithm (LCA) which is the application of the linear filter to real-time monitoring. To our best knowledge, existing work and LCA only explores the correlations of values of each single metric at various times. We present a novel lightweight REal-time Compression Algorithm (ReCA) which employs discovery methods of the correlation among metrics to suppress remote information update in distributed monitoring. The compression algorithms mentioned above have limited compression power because they only explore either the correlations of values of each single metric at various times or that among metrics. Therefore, we propose the Mixed Compression Algorithm (MiCA) which explores both of the correlations to achieve higher compression ratio. We implement our algorithms and an existing compression algorithm denoted by CCA in a distributed monitoring system Ganglia and conduct extensive experiments. The experimental results show that LCA and ReCA have comparable compression ratios with CCA, that MiCA achieves up to 38.2%, 27% and 44.5% higher compression ratios than CCA, LCA and ReCA with negligible overhead, respectively, and that LCA, and ReCA can both increase the scalability of Ganglia about 1.5 times and MiCA can increase about 2.33 times under a mixed-load circumstance.