1. Bandwidth Allocation in a Self-Managing Multimedia File Server Vijay Sundaram and Prashant Shenoy Department of Computer Science University of Massachusetts ACM Multimedia 2001

2. Outline Introduction Introduction Workload Monitoring Module Workload Monitoring Module Adaptive Bandwidth Manager Adaptive Bandwidth Manager Estimating Bandwidth Requirement based on Disk Utilizations Estimating Bandwidth Requirement based on Disk Utilizations Estimating Bandwidth Requirement based on the Arrival Rate Estimating Bandwidth Requirement based on the Arrival Rate Computing the Reservations of Each Class Computing the Reservations of Each Class Self-Managing Bandwidth Allocation in a Multi-disk Server Self-Managing Bandwidth Allocation in a Multi-disk Server Experimental Result Experimental Result Conclusion Conclusion

3. Introduction Best-effort service: simple interference  starvation occurs when bursty workload Mutually-exclusive storage: no interference  static partition of storage space automatic partition is very hard performance is very bad Reservations: no interference  each class reserves a certain fraction of the bandwidth (i.e., R be = 1 – R rt ) performance is good (i.e., meet the deadlines of real-time requests while providing low average response time for best-effort requests) Best-effort class (e.g. Textual data, image file) needs low average response time. Real-time class (e.g. Streaming Media, video) needs to meet the deadlines of requests.

4. Workload Monitoring Module W: the window size I: the measurement interval Workload Monitoring Module tracks various aspects of resource usage.

5. Workload Monitoring Module Request Arrival Rate: Request number: N be, N rt Request size: S be, S rt Request Waiting Times: Queue length: q be, q rt Disk Utilizations: View as bandwidth Time spent in servicing requests: Disk Utilization:

6. Adaptive Bandwidth Manager Estimating Bandwidth Requirement based on Disk Utilizations Estimating Bandwidth Requirement based on Disk Utilizations Estimating Bandwidth Requirement based on the Arrival Rate Estimating Bandwidth Requirement based on the Arrival Rate Computing the Reservations of Each Class Computing the Reservations of Each Class

7. Adaptive Bandwidth Manager U rt U be high percentile of U rt Perc( U rt ) median of U be s Median( U be )

8. Adaptive Bandwidth Manager Estimating Bandwidth Requirement based on Disk Utilizations Estimating Bandwidth Requirement based on Disk Utilizations Best-effort class uses the median of the utilization distribution, denoted by Best-effort class uses the median of the utilization distribution, denoted by Real-time class uses a high percentile of the utilization distribution, denoted by Real-time class uses a high percentile of the utilization distribution, denoted by Perc α ( U rt ) Median α ( U be ) Perc( U rt ) Median( U be )

9. Adaptive Bandwidth Manager Estimating Bandwidth Requirement based on Arrival Rate Estimating Bandwidth Requirement based on Arrival Rate The total disk utilization is always 100% during a overload and no longer reflects the relative needs of each class. The total disk utilization is always 100% during a overload and no longer reflects the relative needs of each class. No allocation can actually satisfy the total bandwidth needs of two classes, the goal of the bandwidth manager should be to ensure stable overload behavior and ensure that the allocations reflect the relative needs of the two classes. No allocation can actually satisfy the total bandwidth needs of two classes, the goal of the bandwidth manager should be to ensure stable overload behavior and ensure that the allocations reflect the relative needs of the two classes. The bandwidth needs of the best-effort class are computed as The bandwidth needs of the best-effort class are computed as The bandwidth needs of the real-time class are computed as The bandwidth needs of the real-time class are computed as

10. Adaptive Bandwidth Manager Computing the Reservations of Each Class Computing the Reservations of Each Class Case 1: Case 1: neither class utilizes its entire allocation neither class utilizes its entire allocation when and when and the allocations of the two classes remains unchanged the allocations of the two classes remains unchanged Case 2: Case 2: the best-effort class utilizes its entire allocation the best-effort class utilizes its entire allocation when and when and increase the allocation of best-effort class increase the allocation of best-effort class and correspondingly decrease the allocation of real-time class Case 3: Case 3: the real-time class utilizes its entire allocation the real-time class utilizes its entire allocation when and when and increase the allocation of real-time class increase the allocation of real-time class and correspondingly decrease the allocation of best-effort class Case 4: Case 4: overload overload when and ; when or when and ; when or based on arrival rate, allocations are computed as and based on arrival rate, allocations are computed as and User specified bounds and User specified bounds and

11. Self-Managing Bandwidth Allocation in a Multi-disk Server Compute the allocations on individual disks Compute the allocations on individual disks Compute the average and maximum allocation of each class Compute the average and maximum allocation of each class the average allocation of the best-effort class across all disks the average allocation of the best-effort class across all disks the maximum allocation of the best-effort class across all disks the maximum allocation of the best-effort class across all disks the average allocation of the real-time class across all disks the average allocation of the real-time class across all disks the maximum allocation of the real-time class across all disks the maximum allocation of the real-time class across all disks Compute the allocation as a linear combination of the average and the maximum load Compute the allocation as a linear combination of the average and the maximum load The final allocation is normalized as follows: The final allocation is normalized as follows:

12. Experimental Result

13. Conclusion Automating bandwidth allocation techniques Automating bandwidth allocation techniques consist of two key components: consist of two key components: the workload monitoring module that efficiently monitors the load in each application class the workload monitoring module that efficiently monitors the load in each application class the adaptive bandwidth manager that uses these workload statistics to dynamically determine the allocation of each class. the adaptive bandwidth manager that uses these workload statistics to dynamically determine the allocation of each class. provide control over the time-scale of allocation via tunable parameters. provide control over the time-scale of allocation via tunable parameters. have stable behavior during transient overload. have stable behavior during transient overload. exploit the semantics of each class while determining their allocations. exploit the semantics of each class while determining their allocations. provide significant advantages over static bandwidth allocation. provide significant advantages over static bandwidth allocation.