PhD Defense - Meeting Tail Latency SLOs in Shared Networked Storage

Today I went to Timothy Zhu's PhD thesis defense.  His work is on achieving better sharing of data center resources to improve performance, and particularly to reduce tail latency.  He also TA'd for me last fall.

Workloads are generally bursty, and their characteristics are different.  Furthermore, they may have service level objectives (SLOs), and the system needs to meet these different objectives.  And the system contains a variety of resources that must be shared in some form.  It is not sufficient to just divide the bandwidth.  Nor can the system measure the latency and try reacting, particularly as bursty workloads do not give sufficient time to react.  While each workload has deadlines, it would be too complex to tag request packets with the deadlines for queuing and routing.  However, the deadlines can be used to generate priorities for requests.

The system is architected to have storage and network enforcement components to ensure QoS.  There is also a controller that receives an initial trace to characterize each workload, and that workload's SLOs.  The controller works through a sequence of analyses to successfully place each workload into the overall system.

Effectively, each workload is assigned a "bucket" of tokens, where the bucket size provides the ability to handle bursts and the rate that tokens are added covers the request rate for the workload.  Shorter burstier workloads receive large buckets and low rates, while constant workloads with little bursts have high rates and small buckets.  In both cases, only when the bucket is empty, is the workload rate-limited in its requests, and these requests receive the lowest priority.  Deterministic Network Calculus (DNC) to model the worst-case queue scenarios.  This plots two curves: the requesting flow and the service curve, both plotted as tokens by function of window size (dt).  The maximum distance between the curves is the maximum latency.

Using three traces: DisplayAds, MSN, and LiveMaps, they tested three approaches: Cake (reactive approach), earliest deadline first, and Timothy's scheme (PriorityMeister).  His scheme did significantly better than the others at meeting the SLOs.  However, the DNC analysis was based on achieving 100% and not the SLO's 99% (or other percentile success).  Depending on the characteristics, there can be significant differences between these guarantees.  To model the latency percentiles, Stochastic Network Calculus (SNC) can achieve this; however, the math is significantly more complex.  And the math had not previously been applied to this problem.  DNC is still better when assuming that bursts are correlated or the system is in an adversarial setting.  Reducing these assumptions (uncorrelated workloads), the SNC-based analysis permitted the system to admit 3x workloads versus the DNC analysis.

Workloads have a curve of satisfying bucket sizes and token rate pairs.  Many systems require the user to provide its rate limit.  Other systems use simple heuristics to either find the "knee of the curve" or select a rate limit as a multiple of the average rate.  However, for an individual workload, all pairs are satisfying, it is only when workloads are combined in a system do the different pairs matter.  The configurations of the set of workloads on the system can be solved for using a system of linear equations.  Therefore, when placing new workloads, the controlling architecture can find successful placements, while potentially reconfiguring the workloads assigned.

One extension would be addressing failure modes.  Currently, the system is assumed to be at degraded performance when components have failed.