IntP: Quantifying cross-application interference via system-level instrumentation

Large-scale container datacenters host tens of thousands of diverse container-wrapped applications each day improving resource usage and maintenance costs. However, resource contention-related interference between co-located applications can severely degrade performance, affecting the quality of service at the user level and compromising experience. Understanding the sources of noise that generates this interference and better managing how to consolidate applications to physical hosts can significantly improve resource usage and overall performance reducing costs for providers and users. This paper presents IntP-an open-source system-level monitoring tool, which analyses selected architectural counters and operating systems data structures to estimate the stress an application puts on each hardware's subsystem and consequently infer the potential interference it could generate in other applications hosted in the same physical machine. Different from state-of-the-art tools that apply a more high-level approach using micro benchmarks and application metrics, IntPs low level instrumentation enables a more accurate prediction of the performance degradation that results from contention on shared resources, with less monitoring overhead. This information can be used to optimize scheduling strategies, which will make datacenter more resource-efficient and cost-effective. To show examples on how to use this tool and validate its results we present three cases studies that applied IntP in their interference-aware methodologies to improve resource utilization in distributed architectures that were able to achieve an increase up to 35% in resource efficiency and up to 25% in user level performance.