EISCambridge 2009MoN8 Exploring Markov models for gate-limited service and their application to network-based services Glenford Mapp and Dhawal Thakker.

Slides:



Advertisements
Similar presentations
Lecture 10 Queueing Theory. There are a few basic elements common to almost all queueing theory application. Customers arrive, they wait for service in.
Advertisements

Introduction to Queuing Theory
AE1APS Algorithmic Problem Solving John Drake
Closed Queuing Networks (Mean Value Analysis). Closed Queuing Networks Arise in two situations Arise in two situations When “source” of requests is explicitly.
Hadi Goudarzi and Massoud Pedram
1 Improving Direct-Mapped Cache Performance by the Addition of a Small Fully-Associative Cache and Prefetch Buffers By Sreemukha Kandlakunta Phani Shashank.
Copyright © 2005 Department of Computer Science CPSC 641 Winter PERFORMANCE EVALUATION Often in Computer Science you need to: – demonstrate that.
Discrete Time Markov Chains
MSN 2004 Network Memory Servers: An idea whose time has come Glenford Mapp David Silcott Dhawal Thakker.
TCOM 501: Networking Theory & Fundamentals
Planning under Uncertainty
Lecture 13 – Continuous-Time Markov Chains
Queueing Theory: Recap
A CHAT CLIENT-SERVER MODULE IN JAVA BY MAHTAB M HUSSAIN MAYANK MOHAN ISE 582 FALL 2003 PROJECT.
RAIDs Performance Prediction based on Fuzzy Queue Theory Carlos Campos Bracho ECE 510 Project Prof. Dr. Duncan Elliot.
OS Fall ’ 02 Performance Evaluation Operating Systems Fall 2002.
Data Communication and Networks Lecture 13 Performance December 9, 2004 Joseph Conron Computer Science Department New York University
1 PERFORMANCE EVALUATION H Often in Computer Science you need to: – demonstrate that a new concept, technique, or algorithm is feasible –demonstrate that.
1 Multiple class queueing networks Mean Value Analysis - Open queueing networks - Closed queueing networks.
1 TCOM 501: Networking Theory & Fundamentals Lectures 9 & 10 M/G/1 Queue Prof. Yannis A. Korilis.
PRASHANTHI NARAYAN NETTEM.
Fundamental Characteristics of Queues with Fluctuating Load (appeared in SIGMETRICS 2006) VARUN GUPTA Joint with: Mor Harchol-Balter Carnegie Mellon Univ.
Lecture 14 – Queuing Systems
Location Models For Airline Hubs Behaving as M/D/C Queues By: Shuxing Cheng Yi-Chieh Han Emile White.
Dimitrios Konstantas, Evangelos Grigoroudis, Vassilis S. Kouikoglou and Stratos Ioannidis Department of Production Engineering and Management Technical.
1.A file is organized logically as a sequence of records. 2. These records are mapped onto disk blocks. 3. Files are provided as a basic construct in operating.
Queuing Networks. Input source Queue Service mechanism arriving customers exiting customers Structure of Single Queuing Systems Note: 1.Customers need.
1 Real-Time Queueing Network Theory Presented by Akramul Azim Department of Electrical and Computer Engineering University of Waterloo, Canada John P.
Towers of Hanoi. Introduction This problem is discussed in many maths texts, And in computer science an AI as an illustration of recursion and problem.
Copyright warning. COMP5348 Lecture 6: Predicting Performance Adapted with permission from presentations by Alan Fekete.
A bit on Queueing Theory: M/M/1, M/G/1, GI/G/1 Yoni Nazarathy * EURANDOM, Eindhoven University of Technology, The Netherlands. (As of Dec 1: Swinburne.
Verification & Validation
1 Chapter 5 Flow Lines Types Issues in Design and Operation Models of Asynchronous Lines –Infinite or Finite Buffers Models of Synchronous (Indexing) Lines.
Scalable Web Server on Heterogeneous Cluster CHEN Ge.
NETE4631:Capacity Planning (2)- Lecture 10 Suronapee Phoomvuthisarn, Ph.D. /
Analysis of M/M/c/N Queuing System With Balking, Reneging and Synchronous Vacations Dequan Yue Department of Statistics, College of Sciences Yanshan University,
Network Design and Analysis-----Wang Wenjie Queueing System IV: 1 © Graduate University, Chinese academy of Sciences. Network Design and Analysis Wang.
Swapping to Remote Memory over InfiniBand: An Approach using a High Performance Network Block Device Shuang LiangRanjit NoronhaDhabaleswar K. Panda IEEE.
Queuing Theory Basic properties, Markovian models, Networks of queues, General service time distributions, Finite source models, Multiserver queues Chapter.
1 Elements of Queuing Theory The queuing model –Core components; –Notation; –Parameters and performance measures –Characteristics; Markov Process –Discrete-time.
+ Simulation Design. + Types event-advance and unit-time advance. Both these designs are event-based but utilize different ways of advancing the time.
Performance Prediction for Random Write Reductions: A Case Study in Modelling Shared Memory Programs Ruoming Jin Gagan Agrawal Department of Computer and.
Christoph F. Eick: Using EC to Solve Transportation Problems Transportation Problems.
Network Design and Analysis-----Wang Wenjie Queuing Theory III: 1 © Graduate University, Chinese academy of Sciences. Network Design and Performance Analysis.
Mobile Agent Migration Problem Yingyue Xu. Energy efficiency requirement of sensor networks Mobile agent computing paradigm Data fusion, distributed processing.
CPSC 404, Laks V.S. Lakshmanan1 External Sorting Chapter 13: Ramakrishnan & Gherke and Chapter 2.3: Garcia-Molina et al.
ECE 466/658: Performance Evaluation and Simulation Introduction Instructor: Christos Panayiotou.
State N 2.6 The M/M/1/N Queueing System: The Finite Buffer Case.
NP-COMPLETE PROBLEMS. Admin  Two more assignments…  No office hours on tomorrow.
1 Markov chains and processes: motivations Random walk One-dimensional walk You can only move one step right or left every time unit Two-dimensional walk.
Operational Research & ManagementOperations Scheduling Economic Lot Scheduling 1.Summary Machine Scheduling 2.ELSP (one item, multiple items) 3.Arbitrary.
NETE4631: Network Information System Capacity Planning (2) Suronapee Phoomvuthisarn, Ph.D. /
A Measurement Based Memory Performance Evaluation of Streaming Media Servers Garba Isa Yau and Abdul Waheed Department of Computer Engineering King Fahd.
Internet Applications: Performance Metrics and performance-related concepts E0397 – Lecture 2 10/8/2010.
Slide 1/29 Informed Prefetching in ROOT Leandro Franco 23 June 2006 ROOT Team Meeting CERN.
(C) J. M. Garrido1 Objects in a Simulation Model There are several objects in a simulation model The activate objects are instances of the classes that.
Queuing Theory.  Queuing Theory deals with systems of the following type:  Typically we are interested in how much queuing occurs or in the delays at.
Example 14.3 Queuing | 14.2 | 14.4 | 14.5 | 14.6 | 14.7 |14.8 | Background Information n County Bank has several.
© 2015 McGraw-Hill Education. All rights reserved. Chapter 17 Queueing Theory.
OPERATING SYSTEMS CS 3502 Fall 2017
Operating Systems (CS 340 D)
Queuing Theory Queuing Theory.
Virtual Memory Networks and Communication Department.
CSI 400/500 Operating Systems Spring 2009
Computer Systems Performance Evaluation
Sachin Jayaswal Department of Management Sicences
Qingbo Zhu, Asim Shankar and Yuanyuan Zhou
September 1, 2010 Dr. Itamar Arel College of Engineering
Location models for airline hubs behaving as M/D/c queues
Computer Systems Performance Evaluation
Presentation transcript:

EISCambridge 2009MoN8 Exploring Markov models for gate-limited service and their application to network-based services Glenford Mapp and Dhawal Thakker Networking Research Group School of Engineering and Information Sciences (EIS) Middlesex University, London

EISCambridge 2009MoN8 Outline of Talk Context Types of Service Exhaustive-Limited Service Gated-Limited Service An Approximate Solution Application to Network-Based Services Conclusion and Future Work

EISCambridge 2009MoN8 Context: Queuing Theory Customers being served in some way –Teller at bank, roving salesman, etc Servers do work on customers after which customers may leave the system or join another queue for further service. Servers may serve other queues Different ways of serving –Exhaustive –Gated

EISCambridge 2009MoN8 Types of Service Exhaustive – the server serves until the queue is empty. So it serves customers who arrive after service on the queue has started. Gated Service – the server only serves the customers in the queue at the start of service. Customers arriving after service has begun are served on subsequent server visits.

EISCambridge 2009MoN8 Limited Service Derivatives Exhaustive-Limited: The server serves a maximum of k customers. Other customers are serviced on subsequent visits. Gated-Limited: The server only serves a maximum of k customers that it finds in the queue when it arrives. Other customers are serviced in the same manner but on subsequent visits

EISCambridge 2009MoN8 Application Different applications may different service models We focus on gated-limited systems for a number of reasons –Transport: Large systems such as buses can be modelled as gated-limited servers. –Network Services can also be modelled as gated-limited service

EISCambridge 2009MoN8 Simple Example of a Network-Based System APPL 1 APPL 2 APPL 3 APPL 4 NETWORK SERVER Network Buffer (Multiple Requests) Client Service Thread

EISCambridge 2009MoN8 Key Observations Once the network buffer is sent off to the server, applications must wait before putting new requests in the buffer The buffer is finite, so there is a maximum number of requests, K, that can be serviced at the same time Gated-Limited Service

EISCambridge 2009MoN8 Motivation Everything is going to be network-based –The network is the computer New streaming applications are being used that require better than best-effort service. You-Tube; BBC iPlayer, etc Can use pre-fetching techniques – but we have to see about demand misses

EISCambridge 2009MoN8 Network-Based Storage Service to support pre-feteching

EISCambridge 2009MoN8 Whats already been done If you look at the literature, a lot of work has been done on exhaustive or exhaustive-limited systems Gated service also studied Gate-limited solutions exist but are generally too hard to calculate –Not back-of-the envelope stuff

EISCambridge 2009MoN8 Standard Solution: The Partial Bulk Service Model (PBM) Found in standard text-books: Each Markov state is defined by 2 parameters: n = the total number of customers in the system s = the number of customers currently being served Note: the maximum number of customer that can be served at the same time is given by K.

EISCambridge 2009MoN8 PBM Cont The PBM is exhaustive-limited service, but we need to understand the solution. If K = 1, we have normal MM1. The general solution is quite similar to the MM1 solution

EISCambridge 2009MoN8

EISCambridge 2009MoN8

EISCambridge 2009MoN8 Evaluation of PBM Exhaustive-limited not gated-limited However, at extremely high loads, the gated-limited waiting times will approach the exhaustive waiting times because there will almost always be K or more customers in the queue. We need to remember this!

EISCambridge 2009MoN8 Our approach Use Markov Models –Previous approaches have been very mathematical from the word go! Look at arrival and departure moments For the gated-limited model, the number of customers served in the next cycle is evaluated at the end of the current cycle.

EISCambridge 2009MoN8 Our Markov Model for Gate-Limited Service

EISCambridge 2009MoN8 Model is complicated because.. There are several chains where each chain represents the number of customers currently being served. –So Chain 1, represents 1 customer being served, etc. You can jump over several chains in one go depending on how many people are left in your queue at the end of the current service time.

EISCambridge 2009MoN8 Gated Limited Model for K=2

EISCambridge 2009MoN8 How do you solve this model? There are K chains; so if K is 2; there are 2 chains Our approach Try to express the probability of being in each state of each chain in terms of the probabilty of lowest element in the chain. For K = 2; we need to express Chain 1 in terms of P 11 and P 22 for Chain 2. – Then we concentrate on solving each chain

EISCambridge 2009MoN8

EISCambridge 2009MoN8

EISCambridge 2009MoN8 The Effect of this Approach By only expressing Markov state equations for Chain 2 in terms of other Markov states for Chain 2, we are saying that we can imagine that the states for Chain 2 in the gated-limited model to be part of an IMAGINARY PBM chain So we can solve for root r, between 0 and 1, just like in the PBM approach

EISCambridge 2009MoN8

EISCambridge 2009MoN8

EISCambridge 2009MoN8 Results for K = 2

EISCambridge 2009MoN8 Towards a general solution

EISCambridge 2009MoN8 Towards a general solution The most difficult part is to calculate p n,n Use the equations for p n,n and express these variables in terms of p k,k. Mathematics is complicated –Need to look at using matrix techniques to solve these equations. Still a lot of work to be done but the approach looks promising.

EISCambridge 2009MoN8 Application – Global Storage Server Project Aim –To develop a high performance globally accessible storage server Eliminate dependency on local storage –More green: less power, noise –Network Memory Server (NMS) Uses the memory of another machine Appears as a hard-disk to the client OS

EISCambridge 2009MoN8 Time to Fetch p blocks = L + Cp Clustering in the NMS

EISCambridge 2009MoN8 Approach To allow stream to run without jitter –Time to fetch < Time to process L + Cp < Tcpu * p Average waiting time expereince to satisfy Demand misses < the average waiting time on disk (Tdisk) L + (d * C) + Twait < Tdisk

EISCambridge 2009MoN8 Looking at Conservative Pre- fetching (PonD) Prefetch only when there is a demand miss Therefore, using PonD: – L + (p+d) * C < ( Tcpu * p) For demand misses, the waiting time < Tdisk: – Time to fetch + Twait < Tdisk L + (p+d) * C + Twait < Tdisk

EISCambridge 2009MoN8 Developing an Operational Space Define an operational space consisting of three axes Twait, demand misses < Tdisk Tnet(p+d) < P* Tcpu

EISCambridge 2009MoN8 Exploring the Space for a given inter-arrival time, 350 microseconds

EISCambridge 2009MoN8 Exploring the Space, 350 microseconds

EISCambridge 2009MoN8 Exploring the Space, 350 microseconds

EISCambridge 2009MoN8 School of EIS, CCM Research group 38 Exploring the Space, 350 microseconds

EISCambridge 2009MoN8 Future Work Further explore analytical model Investigate the effect of different network loads Develop a practical algorithm that can be part of an autonomous system that can balance pre-fetching and demand paging. Develop a file server that uses the algorithm and measure its performance.

EISCambridge 2009MoN8 QUESTIONS? Thank You