Abstract
In heterogeneous I/O workload environments, disk scheduling algorithms should support different QoS (Quality-of-Service) for each I/O request. For example, the algorithm should meet the deadlines of real-time requests and at the same time provide reasonable response time for best-effort requests. This paper presents a novel disk scheduling algorithm called G-SCAN (Grouping-SCAN) for handling heterogeneous I/O workloads. To find a schedule that satisfies the deadline constraints and seek time minimization simultaneously, G-SCAN maintains a series of candidate schedules and expands the schedules whenever a new request arrives. Maintaining these candidate schedules requires excessive spatial and temporal overhead, but G-SCAN reduces the overhead to a manageable level via pruning the state space using two heuristics. One is grouping that clusters adjacent best-effort requests into a single scheduling unit and the other is the branch-and-bound strategy that cuts off inefficient or impractical schedules. Experiments with various synthetic and real-world I/O workloads show that G-SCAN outperforms existing disk scheduling algorithms significantly in terms of the average response time, throughput, and QoS-guarantees for heterogeneous I/O workloads. We also show that the overhead of G-SCAN is reasonable for on-line execution.
Highlights
As an increasingly large variety of applications are developed and equipped in modern computer systems, there is a need to support heterogeneous performance requirements for each application simultaneously
We show that the space and time overhead of G-SCAN is reasonable for online execution
We presented a novel disk scheduling algorithm called G-SCAN that supports requests with different QoS requirements
Summary
As an increasingly large variety of applications are developed and equipped in modern computer systems, there is a need to support heterogeneous performance requirements for each application simultaneously. A deadlineguaranteed service is required for real-time applications (e.g., audio or video playback), while reasonable response time and high throughput are important for interactive best-effort applications (e.g., web navigation or file editing). Since these applications require different QoS- (Quality-of-Service-) guarantees, an efficient disk scheduling algorithm that can deal with heterogeneous I/O requests is needed. In order to find this optimal schedule, all possible request sequences need to be searched This is a complicated searching problem which is known as NP hard [1]. Finding an optimal schedule from this huge searching space is not feasible due to the excessive spatial and temporal overhead. Most practical scheduling algorithms use deterministic heuristic approaches instead of searching huge spaces
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