Feedback performance control in software services T.F. Abdelzaher, J.A. Stankovic, C. Lu, R. Zhang,...
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Transcript of Feedback performance control in software services T.F. Abdelzaher, J.A. Stankovic, C. Lu, R. Zhang,...
Feedback performance control in software services
T.F. Abdelzaher, J.A. Stankovic, C. Lu, R. Zhang, and Y. Lu, Feedback Performance Control in Software Services, IEEE Control Systems, 23(3): 74-90, June 2003.
Overview
SW systems become larger and bigger Performance guarantee required, e.g.,
in web-based e-commerce Control theory
Promising theoretical foundation for perf control in complex SW applications, e.g., real-time scheduling, web servers, multimedia control, storage mangers, power management, routing in computer networks, …
Overview
Software performance assurance problems -> Feedback control problems focused on web server performance guarantee problems
SW performance control
Less rigorous guarantees on perf and quality
Most SW eng. research deals with the development of functionally correct SW
Functional correctness is not enough! Timeliness in embedded systems
Correct but delayed action can be disastrous Non-fucntional QoS attributes, e.g.,
timeliness, security, availability, …
Traditional approaches for perf guarantees Worst case estimates of load &
resource availability Recall EDF, RM, DM, Priority Ceiling
Protocol, …
New demand for performance assurance QoS guarantees required in a broader scope
of applications run in open, unpredictable environments Global communication networks enabling online
banking, trading, distance learning, … Points of massive aggregation suffering
unpredictable loads, potential bottlenecks, DoS attacks, …
-> Precise workload/system model unknown a priori Failure to meet QoS requirements -> loss of
customers or financial damages Worst case analysis/overdeisgn could be overly
pessimistic or wasteful Solid analytic framework for cost-effective perf
assurance required
Challenges
How to model SW architecture? How to map a specific QoS problem into
a feedback control system? How to choose proper SW sensors and
actuators to monitor and adjust perf and workloads/resource allocation?
How to design controllers for servers?-> This paper focuses on web servers
QoS metrics
Delay metrics Proportional to time: queuing delays,
execution latencies, service response time Rate metrics
Inversely proportional to time Connection bandwidth, throughput, packet
rate
Time-related perf attributes
Can be controlled by adjusting resource allocation Queuing theory can predict perf given a
particular resource allocation or vice versa Queuing theory only works for Poisson
arrival patterns Queuing theory can only predict average perf
even if this assumption holds Arrival patterns in web applications follow
heavy-tailed distribution -> Bursty arrival patterns
Service architecture
Fig. 1 Server architecture: (a) computing model (b) control-orientedrepresentation
Liquid task model
Liquid task model
Ci << Di
Takes Ci units of time to serve request i Di is the max tolerable response time Tolerable response time is finite Service times are infinitesimal
Progress of requests through the server queues ≈ Fluid flow
Service rate at stage k = dNk(t)/dt where Nk is #requests processed by stage k
Liquid task model
Volume at time T≈ #requests queued at stage k = ∫T(Fin – Fk) Fk: service rate at stage k Fin: request arrival rate to this stage
Valves: points of control, i.e., manipulated variables such as the queue length
Liquid model does not describe how individual requests are prioritized
Control theory can be combined with queuing theory or real-time scheduling
Server modeling
Difference equation to model web servers y(k): perf, e.g., delay or throughput, measured
at the kth sampling period U(k): control input at the kth sampling period ARMA (AutoreRressive Moving Average) model
y(k) = a1y(k-1) + a2y(k-2) + … + any(k-n)
+ b1u(k-1) + b2u(k-2) + … + bnu(k-n) Transfer function can be derived
Web proxy cache model [4] TCP dynamics [5]
Resource allocation for QoS guarantees Allocate more/less resource =
open/close a valve Need actuators to control resource
allocation or QoS provided by the system
SW system actuators
Input flow actuators Admission control Control queue length, server utilization, … Reject some requests under overload
SW system actuators
Quality adaptation actuators Change processing requirements to
increase server rate under overload E.g., Return abbreviated web page under
overload Tradeoff btwn delay & quality Service level m in a range [0, M] where 0 is
rejection
Resource reallocation actuator Alter the amount of allocated resources Usually applicable to multiple classes of
clients, e.g., dynamically reallocate disk space to support the service delay ratio 1:2 between two service classes [4,7]
QoS Mapping
Convert common resource management & SW perf assurance problems to FC problems
Absolute convergence guarantee Relative guarantee Resource reservation guarantee Prioritization guarantee Statistical multiplexing guarantee Utility optimization guarantee
Absolute convergence guarantee
Convergence to the specified problem Overshoot: Maximum deviation Settling time: Time taken to recover the
desired perf
Absolute convergence guarantee
Rate & queue length control Result in linear FC (Flow) rate can be directly controlled by
actuators Queue length can be linearly controlled by
controlling the flow E.g., server utilization control loop
Absolute convergence guarantee
Delay control More difficult Delay is inversely proportional to flow
Queuing delay d = Q/r where Q is queue length & r is service rate
Nonlinear
Relative guarantee
For example, fix the delays of two traffic classes at a ratio 3:1
Hi: measured perf of class i Ci: weight of class i Relative guarantee specifies H1:H2 = 1:3 Set point = 1/3 Error e = 1/3 – H1/H2
Controlled variable: relative delay ratio Manipulated variable: #allocated processes
per class to control connection delay HTTP protocol summary
A client, e.g., web browser establishes a TCP connection with a server process
The client submits an HTTP request to the sever over the TCP connection
The server sends the response back to the client Keep open the TCP connection for the Keep Alive interval,
e.g., 15s-> Claim connection delay dominates service response time -> Scheduling can also significantly relative delay ratio, but
it is not considered
Relative guarantee in Apache web server
Relative guarantee in Apache web server System identification based on the
ARMA model Randomly change per class process allocations Measure response time
Relative guarantee in Apache web server Perf settings
4 Linux machines run the Surge web workload generator
1 Linux machine runs the Apache web server
Suddenly increase #premium clients by 100 at time 870s
Related work
ControlWare CPU scheduling Storage management Network routers Power/heat management RTDB
Conclusions
Feedback control is applicable to managing performance in SW systems
Future work Adaptive/robust control Predictive control Apply to other computational systems such
as embedded systems
Adptive Control: Self-Tuning Regulator Dynamically estimate a model of the system
via the Recursive Least Square method Controller will accordingly set the actuators to
support the desired perf.
References (HP Storage Systems Lab)
Designing controllable computer systems, Christos Karamanolis, Magnus Karlsson and Xiaoyun Zhu. USENIX Workshop on Hot Topics in Operating Systems (HotOS), June 2005, pp. 49-54, Santa Fe, NM.
Dynamic black-box performance model estimation for self-tuning regulators, Magnus Karlsson and Michele Covell. International Conference on Autonomic Computing (ICAC), pp. 172-182, June 2005, Seattle, WA.
IBM Autonomic Computing Lab http://www.research.ibm.com/autonomi
c/index.html General, broader research issues
regarding self-tuning, self-managing systems
Also, visit Joe Hellerstein’s Adaptive Systems Department
Some University Labs
Tarek Abdelzaher: http://www.cs.uiuc.edu/homes/zaher/
Chenyang Lu: http://www.cse.wustl.edu/~lu/