  The last decade has witnessed an unprecedented growth in the demand for
data-driven real-time services. These services are fueled by emerging
applications that require rapidly injecting data streams and computing updated
analytics results in real-time. In many of such applications, the computing
resources are often shared for processing both updates from information sources
and queries from end users. This requires joint scheduling of updates and
queries because the service provider needs to make a critical decision upon
receiving a user query: either it responds immediately with currently available
but possibly stale information, or it first processes new updates and then
responds with fresher information. Hence, the tradeoff between service
performance and information freshness naturally arises in this context. To that
end, we propose a simple single-server two-queue model that captures the
coupled scheduling of updates and queries and aim to design scheduling policies
that can properly address the important tradeoff between performance and
freshness. Specifically, we consider the response time as a performance metric
and the Age of Information (AoI) as a freshness metric. After demonstrating the
limitations of the simplest FCFS policy, we propose two threshold-based
policies: the Query-k policy that prioritizes queries and the Update-k policy
that prioritizes updates. Then, we rigorously analyze both the response time
and the Peak AoI (PAoI) of the threshold-based policies. Further, we propose
the Joint-(M,N) policy, which allows flexibly prioritizing updates or queries
through choosing different values of two thresholds M and N. Finally, we
conduct simulations to evaluate the response time and the PAoI of the proposed
policies. The results show that our proposed threshold-based policies can
effectively control the balance between performance and freshness.
