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  1. Unexpected long query latency of a database system can cause domino effects on all the upstream services and se- verely degrade end users’ experience with unpredicted long waits, resulting in an increasing number of users disengaged with the services and thus leading to a high user disengage- ment ratio (UDR). A high UDR usually translates to reduced revenue for service providers. This paper proposes UTSLO, a UDR-oriented SLO guaranteed system, which enables a database system to support multi-tenant UDR targets in a cost-effective fashion through UDR-oriented capacity plan- ning and dynamic UDR target enforcement. The former aims to estimate the feasibility of UDR targets while the latter dynamically tracks and regulates per-connection query la- tency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully ex- ploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee. 
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    Free, publicly-accessible full text available November 1, 2024
  2. A primary design objective for Data-intensive User- facing (DU) services for cloud and edge computing is to maximize query throughput, while meeting query tail latency Service Level Objectives (SLOs) for individual queries. Unfortunately, the existing solutions fall short of achieving this design objective, which we argue, is largely attributed to the fact that they fail to take the query fanout explicitly into account. In this paper, we propose TailGuard based on a Tail-latency-SLO-and- Fanout-aware Earliest-Deadline-First Queuing policy (TF-EDFQ) for task queuing at individual task servers the query tasks are fanned out to. With the task queuing deadline for each task being derived based on both query tail latency SLO and query fanout, TailGuard takes an important first step towards achieving the design objective. TailGuard is evaluated against First-In-First-Out (FIFO) task queuing, task PRIority Queuing (PRIQ) and Tail-latency-SLO-aware EDFQ (T-EDFQ) policies by simulation. It is driven by three types of applications in the Tailbench benchmark suite. The results demonstrate that TailGuard can improve resource utilization by up to 80%, while meeting the targeted tail latency SLOs, as compared with the other three policies. TailGuard is also implemented and tested in a highly heterogeneous Sensing-as-a-Service (SaS) testbed for a data sensing service, with test results in line with the other ones. 
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    Free, publicly-accessible full text available July 1, 2024