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Federated computing, including federated learning and federated analytics, needs to meet certain task Service Level Objective (SLO) in terms of various performance metrics, e.g., mean task response time and task tail latency. The lack of control and access to client activities requires a carefully crafted client selection process for each round of task processing to meet a designated task SLO. To achieve this, one must be able to predict task performance metrics for a given client selection per round of task execution. In this paper, we develop, FedSLO, a general framework that allows task performance in terms of a wide range of performance metrics of practical interest to be predicted for synchronous federated computing systems, in line with the Google federated learning system architecture. Specifically, with each task performance metric expressed as a cost function of the task response time, a relationship between the task performance measure - the mean cost and task/subtask response time distributions is established, allowing for unified task performance prediction algorithms to be developed. Practical issues concerning the computational complexity, measurement cost and implementation of FedSLO are also addressed. Finally, we propose preliminary ideas on how to apply FedSLO to the client selection process to enable task SLO guarantee. 

Unexpected long query latency of a database system can cause domino effects on all the upstream services and severely 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 disengagement 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 planning 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 latency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully exploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee. 

Unexpected long query latency of a database system can cause domino effects on all the upstream services and severely 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 disengagement 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 planning 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 latency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully exploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee. 

Unexpected long query latency of a database system can cause domino effects on all the upstream services and severely 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 disengagement 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 planning 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 latency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully exploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee.