A General Framework for Average-Case Performance Analysis of Shared Resources

Contemporary embedded systems are based on complex heterogeneous multi-core platforms to cater to the increasing number of applications, some of which have (soft) real-time requirements. To reduce cost, resources are shared using diverse arbitration mechanisms, such as Time-Division Multiplexing (TDM), Static-Priority (SP), and Round-Robin (RR), depending on application and resource requirements. However, resource sharing results in interference between sharing applications making it difficult to estimate if the average latency is sufficient to satisfy their real-time requirements. Existing work proposes isolated models that either fail to address the diversity of arbitration mechanisms or cannot capture the dynamic arrival and service processes of applications and resources in multi-core platforms. This paper addresses this problem by proposing a general framework for average-case performance analysis of shared resources in multi-core platforms. The two main contributions are: 1) a general model for resource sharing based on queuing theory that can be used with different arbiters and that captures architectural features of the shared resource, such as pipelining and arbitration delay, and 2) three arbiter models for TDM, SP, and RR, respectively that assume general distributions (G/G/1) and fits within the framework.