1 UNIVERSITY OF OSLO Department of Informatics High-Level Load Balancing for Web Services Master thesis Sven Ingebrigt Ulland 20th May 2006
3 Abstract A structured approach to high availability and fault tolerance is essential in a production-grade service delivery network, where delays and faults can occur for a multitude of reasons. In this thesis, we consider the highlevel scheduling and load sharing properties offered by the Domain Name System, as implemented in popular DNS software packages. At this level, the scheduling mechanism can account for server availability, geographical proximity, time zones, etc. We explore the performance and capabilities of high-level DNS-based load balancing, where we draw special attention to the choice of caching policy (time-to-live) for DNS data. Our findings confirm the high performance of modern DNS server implementations, but question the use of DNS as a suitable load balancing mechanism in itself. Further, we analyse the use of a database-supported DNS service for allowing highly dynamical query responses, and show that this approach has both potentially negative (single point of failure) and positive (improved balancing flexibility) properties.
4 ii Abstract
5 Preface The work presented herein signifies the conclusion of a two year master s degree in network and system administration attained at Oslo University College in collaboration with the University of Oslo. The degree has spanned the years 2004 through 2006, with this thesis being the focus of the last four months in the final semester. High level load balancing is the main topic of the thesis, motivated by a cooperative proposal to study the nature of operation critical services. Indeed, the topic was suggested as part of a set of parallel projects on load balancing for networked services, each with a different approach. Where in this paper attention is drawn towards high level properties of load balancing, the other projects address lower level characteristics of such systems. With a basis in the results obtained in this work, Professor Mark Burgess and myself have co-written a paper entitled Uncertainty in Global Application Services with Load Sharing Policy, which has been submitted to the 17th IFIP/IEEE Distributed Systems: Operations and Management (DSOM 2006) conference. It is currently awaiting acceptance as per May Now at the summit of two years of demanding work, there are many people to whom I am most indebted their help and suggestions have been and are greatly appreciated. First, I would like to extend my genuine thanks to my advisor and course instructor, Professor Mark Burgess, whose enthusiasm, dedication and understanding have guided me and my work on the right path many a time throughout the degree, especially these last months. Also, I am very grateful for the cooperation and support from my fellow students and friends, Jon Henrik Bjørnstad, Espen Tymczuk Braastad, Ilir Bytyci and Gard Undheim. Without the encouragement and mutual understanding inspired by their presence through these two years, the work would not have progressed as smoothly as it did. A special thanks to Kyrre M. Begnum for his efforts in maintaining the virtual network setup, and helping me with problems and questions in its regard. To my family and friends, your support has been very much honoured in these months of challenging endeavour. Oslo, May 20th 2006 Sven Ingebrigt Ulland
6 iv Preface
7 Contents Abstract Preface i iii 1 Introduction The Need for Reliable Services Quality of Service Round-Trip Time Throughput The Flash Crowd Effect High Sustained Load Methods of Combatting Congestion Topology Overview Extending Topologies Brief Note on Terminology Paper Organisation Overview of Load Balancing The Load Balancing Big Picture Cause Tree Perspective Levels of Scheduling High-Level Scheduling Modes of Operation Load Balancer Components Lower Level Load-Balancing The Domain Name System Example of Use Challenging Caches Routing-based Load Balancing IP Anycast Site Multihoming Global Server Load Balancing Related Technologies
8 vi CONTENTS Content Delivery Networks Multicast HTTP Redirect Traffic Characteristics Previous Research DNS-based Load Balancing Summary Traffic Characteristics Pareto and Gaussian Distributions Aim of this Study Hypotheses 39 5 Experimental Design System Modelling Queues and Delays Hardware Considerations Virtual Network with Xen Emulating WANs NetEm and IProute Delay Distributions Software Operating System Details DNS Server BIND DNS Server PowerDNS Apache Webserver Traffic Generator Tools QueryPerf DNS testing Flood HTTP testing HTTPerf HTTP testing Clock Synchronisation NTP Measuring Load Analysis Tools Methodology DNS Server Performance RRset Ordering Implications BIND Scheduling Entropy Impact of TTL on Response-time Static Back-end Dynamic Back-end
9 CONTENTS vii 7 Experiment Results DNS Server Performance BIND RRset Ordering BIND Scheduling Entropy Impact of TTL on Response-time Static Back-end Dynamic Back-end Dynamic Back-end Equilibrium Conclusions and Discussion Review of Hypotheses DNS Performance RRset Ordering Scheduling Entropy Effect of TTL On DNS as a Balancing Mechanism Future Work Alternative Protocols Proactive Caching A Configuration Files 91 A.1 MLN/Xen Configuration A.2 Flood Configuration A.2.1 Flood static A.2.2 Flood dynamic B Scripts 97 B.1 Analysis Scripts B.2 Web Server Scripts
10 viii CONTENTS
11 List of Figures 1.1 Insertion points for load balancing Simplified QoS cause tree Brief overview of basic queueing models Essential components in a load balancer Dispatcher-based topology setup Standard DNS lookup procedure Potential caching-points for DNS lookups Simplified schema of a multihomed site Global server load balancing Examples of Pareto and Gaussian distributions Supposed effect of TTL on RTT Simplified Wide-Area Network Model Xen conceptual design NetEm delay distributions Pareto Generator Phenomenon Logical Network Setup Experiment Setup DNS Performance Experiment Setup RRset Ordering Supposed effect of TTL on RTT Experiment setup, RTT vs TTL DNS server performance Normalised request distributions Flood static Total page load time Flood static DNS lookup times Flood static DNS lookup times (scaled) Flood static HTTP round-trip times Flood dynamic HTTP response time Web server utilisation with varying TTL
12 x LIST OF FIGURES 7.9 Flood dynamic Cumulative HTTP response time Flood dynamic TTL equilibrium
13 List of Tables 2.1 Typical metrics used in load-balancing algorithms A selection of DNS load-balancing approaches Traffic Generator Tools Flood static NetEm settings RRset ordering implications Normalised request distributions
14 xii LIST OF TABLES
15 Chapter 1 Introduction Since its infancy, the Internet has experienced a near exponential growth in user base, infrastructure, content size and resources like low-latency, high throughput network links. According to the Internet World Stats initiative, Internet users now total over one billion approximately 16 percent of the world s population.  This explosive increase means that high traffic sites offering e-commerce, community and other resource intensive services, face an enormous challenge when it comes to ensuring high availability and fault tolerance for their services. This paper examines how load balancing is used as a central concept to achieve these goals while ensuring transparency and interoperability with existing technology. 1.1 The Need for Reliable Services With online business-to-business and business-to-customer revenues soaring to ever new heights each passing year, it is trivial to deduce that for a company that relies heavily on online sales and service provision, even a small amount of downtime could have a disastrous impact on its economy. To put this in perspective, consider Google s total revenues for the first quarter of 2006 (92 days): 2,254 billion USD.  Assuming a uniform distribution of traffic over the entire period, this translates to $24,497 million per day, or roughly $284 per second. Now, even with a seemingly high uptime rating of 99.99% (0.01% downtime), a company like Google would face a quarterly loss of around $22.5 million. The repercussions of downtime not only affect direct revenue lost to unavailable services; it could also potentially hurt a business reputation. However, high-profile enterprises like Google and Amazon have, through their long term service quality, established strong trust relationships with customers. Accordingly, they would not experience massive customer migration, at least not for one-time occurences. Also, dissatisfied customers do not always have alternatives to turn to, and may very well decide to stay
16 2 Introduction with their current service provider, despite a degrade in service quality. It should be noted that the examples of world-class service systems, like the ones mentioned by name here, are not directly applicable to the cases of smaller companies or ventures that have not yet established a sustainable market share. In such situations, the quality of a service could mean the difference between growth and, in the worst case, bankruptcy. The keywords here have already been mentioned: Service quality more commonly coined Quality of Service, or QoS. This is the essence of what we would like to achieve with the diversity of techniques and solutions for high availability and load balancing Quality of Service In a technical and quantitative context, QoS refers to the level of quality in a service, represented by several measurable factors. These factors typically include network metrics or properties such as latency, jitter, throughput, error rate, availability, etc. Through analysis of past and present communication information, a service provider is able to calculate several metrics which are then used to determine the QoS level in business-to-business or business-to-customer service level agreements, which are contracts detailing the QoS guarantees given by the provider. Depending on scope, the term QoS is applied to different parts of a network. Often, it is related to the entire end-to-end communication path, that is, from a customer s local computer to the very server from which the content is retrieved. In other scenarios it might be applied to a single network link between two businesses. For a service provider, it is impossible to guarantee QoS levels of networks outside its jurisdiction. For example, a business serving audio streams to the public cannot be responsible for failures and limitations in the end-users networks Round-Trip Time In the context of load balancing and QoS, one factor is particularly interesting: The response time when querying a resource. Response time is often referred to as round-trip time (RTT) or latency. For example, we are interested in knowing how long a user has to wait for a general web page to load. This response time should be kept as low as possible: Research has shown that poor web site performance relates to degraded corporate image, and even has a poor impact on users perception of site security.  As a user browses through a site, the tolerance for latency decreases with every page click. In other words, a user might tolerate a high initial delay, but will tend to not accept a static or increasing delay. This is an important point that will be discussed later.
17 1.1 The Need for Reliable Services Throughput Another factor to consider for large sites is throughput. Though it is not necessarily as important as response time, it does have distinct applications. Throughput is a measure of how much data can be pushed through a connection over a given time interval. Common denominators for network throughput are bits and bytes per time unit, often preceded by SI or IEC binary prefixes; for example kilobits (10 3 bits) per second or mebibytes (2 20 bytes) per second kbps and MiBps respectively. Certain applications, like streaming live media and real-time interactive systems, normally have throughput requirements, e.g. 128kbps per connection for streaming audio. In such scenarios, response time does not play an important part as long it is kept below acceptable levels. Therefore, a company should determine what factor to focus on when designing their highly available, loadbalanced infrastructure. In general, we can take a step back and observe what is the cause of the high demands on server performance. Somewhat simplified, we observe two possible causes that we want to combat with load balancing: The flash crowd effect and high sustained load The Flash Crowd Effect The relatively new concept of a flash crowd surfaced with the growing popularity of large commercial and community news sites, with prominent examples including Slashdot.org and Digg.com, together with world events such as war reports, the Olympics and various other cultural happenings. It refers to the effect of hundreds and thousands of users simultaneously accessing a certain web site or page, thus overloading the server capacity. The word simultaneous is not very accurate, however, because it implies that events happen at the exact same time. In this context, we use the term a bit more loosely to mean over a relatively short time interval. Real-world intervals typically range from seconds and minutes to days and even weeks. There are many relevant questions to ask when it comes to dealing with the flash crowd effect. Is it possible to safely expect when such an event will occur, or could it be modelled in a probabilistic manner? Are we able to determine the size of the crowd, and its requirements from our services? How could internal resources be regrouped to deal with the load, or would we end up with lots of unused or underutilised resources after the crowd? On-demand resource reallocation using virtualisation could be an alternative. Maybe co-location 1 is a temporary solution to a temporary problem? 1 Co-location refers to the practice of renting server space and hardware at remote data centres and establishing service level agreements with one or more providers to offload the company s own resources.
18 4 Introduction These questions are not always straightforward to answer, and present a challenge to any undertaker willing or needing to complete projects that might face the effect High Sustained Load Many popular sites experience a more or less continual high rate of traffic. Typical examples like Google, Microsoft, Amazon, BBC News and CNN are accessed by users from all over the world and thus at all times of the day. Being the largest search engine on the Internet, Google faces over 200 million queries per day, or roughly 2300 queries per second. Given an average query result of 20kB, this translates to around 3.6 terabytes of outgoing data per day. Again, Google is an extraordinary case, but it serves as a good example of the possibilities and what might lay ahead for smaller companies in the future, assuming a continuing growth of the online service market. A service provider capable of handling a high sustained load will typically reserve more bandwidth and resources than is currently needed, simply to be able to cope with variations in traffic intensity a technique often referred to as over-provisioning. Deploying this kind of buffer or safeguard means that the flash crowd problem is also effectively dealt with, as long as the crowd size and demands remain below a certain threshold. In this paper, we are interested in the latter case of congestion, where a service is put under relatively continuous pressure from users, though it also encompasses the possibility of flash crowds. 1.2 Methods of Combatting Congestion When a service provider has identified and analysed the challenges of availability, the questions arise on how to mitigate the effects of high loads on infrastructure hardware and software. Solving such challenges is all about recognising and improving on bottlenecks, a process realised through the concept of network engineering, and its derivative, traffic engineering. These are basic yet complex building blocks underlying the QoS paradigm. Network engineering often refers to the practice of pinpointing specific service requirements and implementing both hardware and software solutions to meet these requirements. Traffic engineering (TE), on the other hand, is about introducing performance-enhancing optimisations in already operational networks. [4, 5] Normally, such optimisations are carried through using statistical and scientific principles oriented towards the four phases of TE: measurement, modelling, characterisation and control of Internet traffic. The main objective of the optimisations is simply to achieve
19 1.2 Methods of Combatting Congestion 5 given QoS requirements with the intentional side-effect of lowered costs through utilising existing resources. To summarise, traffic engineering seeks to effectively balance traffic load throughout existing networks, thus achieving QoS demands and minimising typical costs of adding hardware and software implementations, common to network engineering. Ideally, the two approaches complement each other in a manner satisfying both project requirements and budgets. When dealing with real-world cases of load balancing, both network and traffic engineering are general purpose tools used throughout all steps of an implementation. However, they can only be applied to a limited scope of the network topology; typically that under administrative authority of the provider Topology Overview Actual network and traffic engineering techniques could and should be introduced on multiple levels in any given topology: It is well known that a chain is only as strong as its weakest link. This holds very true for computer networking, which is why providers seek to strengthen each link by introducing redundant and load-balanced systems. Consider a company launching a campaign involving distribution of a streaming movie from their web pages. Even if they have a very capable cluster of web servers in their data centre, their network connection to the outside world could be disproportionate to the demand, and thus create a bottleneck or weak link. It is apparent that multi-layered balancing and redundancy is required for any crucial online service. Details on the layered approach will follow in the next chapter. For now, consider the somewhat simplified topology in figure 1.1 to get a quick overview of the involved parties. Some of the most common spots for introducing redundancy and load balancing are enumerated. 1. Client-side load balancing is not a normal practice, but it is indeed possible. For some time, Netscape incorporated a simple balancing algorithm in their Navigator browser, making it choose a random Netscape web-server when visiting  2. Core Internet routing uses protocols and agreements that allow for automated load balancing and fail-over mechanisms. These are commonly based on the Border Gateway Protocol, used for data-exchange between large Internet operators. 3. DNS-based load balancing, one of the main topics of this paper, is a popular way of distributing traffic amongst a set of Internet addresses
20 6 Introduction by returning a list of active addresses to the requesting client. These addresses can point to a set of servers or even a set of geographically separate sites. 4. Sites can connect to the net through several links, a practice known as multihoming. This enables both incoming and outgoing load balancing, in addition to the increased redundancy. 5. Dispatcher-based load balancing is used within a site to balance load between a set of real servers. Generally, the dispatcher assumes a virtual address for a service and receives requests which it then redirects to an appropriate server based on given criteria. 6. The real servers can again operate some form of balancing mechanism to decide whether to handle the request or redirect it to a more suitable server or site. 7. Content servers could access back-end servers typically running databases and low-level services in a load-balanced fashion. 8. Back-end servers could also incorporate balancing amongst themselves to avoid over-utilisation. When talking about varying levels of load balancing, it is fair to identify the level to be proportional to the distance from the content served long distance equals high level, and vice versa. In an informal manner, we can designate steps one through four in the figure as high-levels, and steps five through eight as low-levels of load balancing. This is discussed more in detail in the next chapter Extending Topologies As mentioned earlier, it is generally not possible to exercise guarantees or even loose assumptions about QoS levels outside a provider s own network domain. Referring to the figure again, mid-size service operators would typically have control over their local network (steps five through eight), and possibly the DNS servers. The solution adopted by many operators in the industry is therefore to extend their domain. In stead of physically extending it by building new infrastructure, they negotiate contracts with other instances to handle traffic for them, creating a logical extension of operator authority. These contracts are the previously mentioned service levels agreements, SLAs. Business SLAs allow for a variable degree of flexibility in traffic engineering, and can prove to be exactly what is needed for the operation of a set of services. However, because of the complex nature of SLAs and their applications, this paper does not consider topology extension other than that of DNS and routing, which are discussed in the next chapter.
21 1.3 Brief Note on Terminology 7 Back-end servers LAN Content servers 3 DNS servers 1 Client 5 Dispatcher 4 2 Core routers Internet Figure 1.1: Potential insertion points for load balancing. Load balancing can be introduced in several extents of a network path, for example at the core routing level (2) or for a cluster of content servers (5). 1.3 Brief Note on Terminology Now that we have introduced the topic, there are two mechanisms that can be identified: load balancing and redundancy. These are two distinct terms. Load balancing refers to the act of sharing an imposed service load between multiple processing servers. Redundancy in engineering is about allocating backup resources in case of failure in the operating resources. Depending on context, redundancy refers to having unused equipment waiting for failures to occur in the currently running equipment. However, when we talk about load balancing, we imply a form of active redundancy, with the result being a load-balanced service with the welcome side effect of high availability. 1.4 Paper Organisation The remainder of the paper is organised as follows. Chapter 2 presents an introduction of background material and relevant technologies, with an accompanying survey of previous research in chapter 3. An overview of our experimental hypotheses is found in chapter 4. The experimental setup is detailed in chapter 5. Methodology and procedures are described in chapter 6, with the experimental results following in chapter 7. The paper is rounded off with a discussion and conclusions in chapter 8.
22 8 Introduction
23 Chapter 2 Overview of Load Balancing With the previous chapter focusing on why load balancing is desirable, the following pages outline and detail some background material and research in the field, and a selection of available implementation schemes. Interest in load balancing for computer servers and networks dates back at least two decades, with the introduction of various software hacks for the Berkeley Internet Name Domain server (BIND).  Since then, research has brought forward a vast array of load balancing techniques, some of which are relevant for this study. One of the main issues to consider when implementing balancing is that of interoperability. Again, this has different meanings depending on scope. For example, it is generally a good idea to ensure client interoperability, so that new users need not modify their software or hardware to be able to communicate with a service. Specifically, this means that implementors should follow the guidelines and protocols described in the de facto Internet standards, e.g. IPv4 and HTTP version 1.0. Though it might be preferable to develop an entirely new set of IP standards that account for many of the problems faced by load balancing, it is unrealistic to expect wide adoption and replacement of fundamental Internet protocols in the short run. However, there is a middle ground between adherence to standards and developing new ones: Interoperability is ensured by having load balancing operate behind the scenes. Exactly what is done behind the scenes is up to each implementation, as long as the public experiences a consistent play, i.e. standard protocol behaviour is assured. This freedom has led to some proprietary solutions, often implemented in specific commercial load-balancing hardware or software suites. 2.1 The Load Balancing Big Picture Proper classification of load balancing methods all depends on the viewpoint or perspective needed for a particular application or problem. In the
24 10 Overview of Load Balancing case of a strict TCP/IP stack model, protocols like DNS and BGP would be put in the so-called application layer. This is also the case for, say, FTP and SMTP the ubiquitous Internet protocols for file and mail transfer, respectively. However, there is a subtle yet important difference in wording that may appear confusing: It is commonplace to load-balance protocols like DNS, FTP and HTTP. This means that a load-balancing algorithm takes protocol type into account when distributing load, for example by sending all DNS packets to a specific server designed to handle them. On the other hand, there is the wording load-balancing using DNS or DNS-based load balancing, which means that DNS itself is used as an integral component of the load-balancing mechanism, i.e. it plays the role of a middle-ware dispatcher because of its ability to provide a mapping between OSI layers. Using the same phrasing, it does not make as much sense to do load-balancing using FTP, for example. This distinction is important to maintain when we now look at some of the many models on the topic Cause Tree Perspective To better understand where the different types of load balancing challenges fit into the big picture, we introduce the QoS cause tree in figure 2.1. Cause trees are used to approach a problem from the bottom up, detailing the properties of each individual branch and then linked together to form the complete system. In this example, the notion of QoS (i.e. primarily the response time of a query) is divided into a set of causes that all contribute to the perceived end result. Therefore, QoS is placed at the root of the tree, and all the delays are aggregated from the bottom elements up towards the root. Assuming that on a reasonable scale, as depicted by a layer in the figure, each single cause of uncertainty is independent, we are able to express the total perceived uncertainty in an end-user Quality-of-Service perspective: t = ( t DNS ) 2 + t routing ) 2 + For an intricate system like the Internet, this approach has unfortunate shortcomings in that uncertainty in DNS is itself dependent upon the underlying network, with layers upon layers of inter-dependencies. On the other hand, this approach does help in categorisation of system elements. Each of the causes in the figure is possible to use as a basis for balancing. For example, it could be convenient to balance server load based on client proximity to the server, or based on server CPU load. More conventionally, sites consider a combination of delay-inducing causes when deploying load-balancing equipment. It would be ideal to accurately calculate weights or probabilities for each cause in the tree. This would be of great help for service providers when designing their network. Of course, since different sites can experi-
White paper Request Routing, Load-Balancing and Fault- Tolerance Solution - MediaDNS June 2001 Response in Global Environment Simply by connecting to the Internet, local businesses transform themselves
Lecture 3: Scaling by Load Balancing 1. Comments on reviews i. 2. Topic 1: Scalability a. QUESTION: What are problems? i. These papers look at distributing load b. QUESTION: What is the context? i. How
International Journal of Scientific & Engineering Research Volume 3, Issue 5, May-2012 1 OPTIMIZATION OF WEB SERVER THROUGH A DOMAIN NAME SYSTEM APPROACH Dr varaprasad.s..kondapalli 1 1 Director&principal,G.H.
UNIVERSITY OF OSLO Department of Informatics Predicting Performance and Scaling Behaviour in a Data Center with Multiple Application Servers Master thesis Gard Undheim Oslo University College May 21, 2006
1. Comments on reviews a. Need to avoid just summarizing web page asks you for: i. A one or two sentence summary of the paper ii. A description of the problem they were trying to solve iii. A summary of
OpenFlow Based Load Balancing Hardeep Uppal and Dane Brandon University of Washington CSE561: Networking Project Report Abstract: In today s high-traffic internet, it is often desirable to have multiple
White Paper Overview Many enterprises attempt to scale Web and network capacity by deploying additional servers and increased infrastructure at a single location, but centralized architectures are subject
Whitepaper WAN Traffic Management with PowerLink Pro100 Overview In today s Internet marketplace, optimizing online presence is crucial for business success. Wan/ISP link failover and traffic management
Single Pass Load Balancing with Session Persistence in IPv6 Network C. J. (Charlie) Liu Network Operations Charter Communications Load Balancer Today o Load balancing is still in use today. It is now considered
Key Components of WAN Optimization Controller Functionality Introduction and Goals One of the key challenges facing IT organizations relative to application and service delivery is ensuring that the applications
CS3250 Distributed Systems Lecture 4 More on Network Addresses Domain Name System DNS Human beings (apart from network administrators and hackers) rarely use IP addresses even in their human-readable dotted
Chapter 1 Load Balancing 57 Understanding Slow Start When you configure a NetScaler to use a metric-based LB method such as Least Connections, Least Response Time, Least Bandwidth, Least Packets, or Custom
CHAPTER 2 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 22 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 2.1 INTRODUCTION As the main emphasis of the present research work is on achieving QoS in routing, hence this
Internet Protocol: IP packet headers 1 IPv4 header V L TOS Total Length Identification F Frag TTL Proto Checksum Options Source address Destination address Data (payload) Padding V: Version (IPv4 ; IPv6)
FortiBalancer: Global Server Load Balancing WHITE PAPER FORTINET FortiBalancer: Global Server Load Balancing PAGE 2 Introduction Scalability, high availability and performance are critical to the success
SiteCelerate white paper Arahe Solutions SITECELERATE OVERVIEW As enterprises increases their investment in Web applications, Portal and websites and as usage of these applications increase, performance
Chapter 3 TCP/IP Networks 3.1 Internet Protocol version 4 (IPv4) Internet Protocol version 4 is the fourth iteration of the Internet Protocol (IP) and it is the first version of the protocol to be widely
TRUFFLE Broadband Bonding Network Appliance A Frequently Asked Question on Link Bonding vs. Load Balancing 5703 Oberlin Dr Suite 208 San Diego, CA 92121 P:888.842.1231 F: 858.452.1035 firstname.lastname@example.org
DEPLOYMENT GUIDE Version 1.1 DNS Traffic Management using the BIG-IP Local Traffic Manager Table of Contents Table of Contents Introducing DNS server traffic management with the BIG-IP LTM Prerequisites
Web Caching and CDNs Aditya Akella 1 Where can bottlenecks occur? First mile: client to its ISPs Last mile: server to its ISP Server: compute/memory limitations ISP interconnections/peerings: congestion
DATA COMMUNICATOIN NETWORKING Instructor: Ouldooz Baghban Karimi Course Book: Computer Networking, A Top-Down Approach, Kurose, Ross Slides: - Course book Slides - Slides from Princeton University COS461
EECS 489 Winter 2010 Midterm Exam Name: This is an open-book, open-resources exam. Explain or show your work for each question. Your grade will be severely deducted if you don t show your work, even if
THE MASTER LIST OF DNS TERMINOLOGY v 2.0 DNS can be hard to understand and if you re unfamiliar with the terminology, learning more about DNS can seem as daunting as learning a new language. To help people
Architecture of distributed network processors: specifics of application in information security systems V.Zaborovsky, Politechnical University, Sait-Petersburg, Russia email@example.com 1. Introduction Modern
Protocols Precise rules that govern communication between two parties TCP/IP: the basic Internet protocols IP: Internet Protocol (bottom level) all packets shipped from network to network as IP packets
Combining Global Load Balancing and Geo-location with Emissary TM A New Kind of Global Internet Traffic Management Appliance from Coyote Point Systems and Digital Envoy Establishing a Geo-Sensitive, Highly
Application Note IP Addressing A Simplified Tutorial July 2002 COMPAS ID 92962 Avaya Labs 1 All information in this document is subject to change without notice. Although the information is believed to
ClusterLoad ESX Virtual Appliance quick start guide v6.3 ClusterLoad terminology...2 What are your objectives?...3 What is the difference between a one-arm and a two-arm configuration?...3 What are the
Superior Disaster Recovery with Radware s Global Server Load Balancing (GSLB) Solution White Paper January 2012 Radware GSLB Solution White Paper Page 1 Table of Contents 1. EXECUTIVE SUMMARY... 3 2. GLOBAL
Basic Network Configuration 2 Table of Contents Basic Network Configuration... 25 LAN (local area network) vs WAN (wide area network)... 25 Local Area Network... 25 Wide Area Network... 26 Accessing the
Windows Server 2008 R2 Hyper-V Live Migration Table of Contents Overview of Windows Server 2008 R2 Hyper-V Features... 3 Dynamic VM storage... 3 Enhanced Processor Support... 3 Enhanced Networking Support...
PolyServe High-Availability Server Clustering for E-Business 918 Parker Street Berkeley, California 94710 (510) 665-2929 wwwpolyservecom Number 990903 WHITE PAPER DNS ROUND ROBIN HIGH-AVAILABILITY LOAD
A Link Load Balancing Solution for Multi-Homed Networks Overview An increasing number of enterprises are using the Internet for delivering mission-critical content and applications. By maintaining only
Uncertainty in Global Application Services with Load Sharing Policy Mark Burgess and Sven Ingebrigt Ulland Oslo University College, Norway firstname.lastname@example.org Abstract. With many organizations now employing
Alteon Global Server Load Balancing Whitepaper GSLB Operation Overview Major Components Distributed Site Monitoring Distributed Site State Protocol Internet Topology Awareness DNS Authoritative Name Server
White Paper Global Server Load Balancing APV Series Application Delivery Controllers May 2011 Global Server Load Balancing Access. Security. Delivery. Introduction Scalability, high availability and performance
NETWORK DEVICE MONITORING pag. 2 INTRODUCTION This document aims to explain how Pandora FMS is able to monitor all network devices available on the marke such as Routers, Switches, Modems, Access points,
Chapter 14: Distributed Operating Systems Chapter 14: Distributed Operating Systems Motivation Types of Distributed Operating Systems Network Structure Network Topology Communication Structure Communication
Scalable Linux Clusters with LVS Considerations and Implementation, Part I Eric Searcy Tag1 Consulting, Inc. email@example.com April 2008 Abstract Whether you are perusing mailing lists or reading
TRUFFLE Broadband Bonding Network Appliance BBNA6401 A Frequently Asked Question on Link Bonding vs. Load Balancing LBRvsBBNAFeb15_08b 1 Question: What's the difference between a Truffle Broadband Bonding
v. Test Node Selection Having a geographically diverse set of test nodes would be of little use if the Whiteboxes running the test did not have a suitable mechanism to determine which node was the best
UNIVERSITY OF OSLO Department of Informatics Performance Measurement of Web Services Linux Virtual Server Muhammad Ashfaq Oslo University College May 19, 2009 Performance Measurement of Web Services Linux
Ensuring Business Continuity and Disaster Recovery with Coyote Point Systems Envoy WHITE PAPER Prepared by: Lisa Phifer Core Competence, Inc. As e-business quickly becomes the norm, virtually every enterprise
Layer 4-7 Server Load Balancing Security, High-Availability and Scalability of Web and Application Servers Foundry Overview Mission: World Headquarters San Jose, California Performance, High Availability,
COMPSCI 742 THE UNIVERSITY OF AUCKLAND SECOND SEMESTER, 2008 Campus: City COMPUTER SCIENCE Data Communications and Networks (Time allowed: TWO hours) NOTE: Attempt all questions. Calculators are NOT permitted.
THE MASTER LIST OF DNS TERMINOLOGY First Edition DNS can be hard to understand and if you re unfamiliar with the terminology, learning more about DNS can seem as daunting as learning a new language. To
Avaya P330 Load Balancing Manager User Guide March 2002 Avaya P330 Load Balancing Manager User Guide Copyright 2002 Avaya Inc. ALL RIGHTS RESERVED The products, specifications, and other technical information
ZEN LOAD BALANCER EE v3.04 DATASHEET The Load Balancing made easy OVERVIEW The global communication and the continuous growth of services provided through the Internet or local infrastructure require to
Globally Distributed Content (Using BGP to Take Over the World) Horms (Simon Horman) firstname.lastname@example.org November 2001 http://supersparrow.org/ 1 Introduction Electronic content is becoming increasingly
Building a Systems Infrastructure to Support e- Business NO WARRANTIES OF ANY NATURE ARE EXTENDED BY THE DOCUMENT. Any product and related material disclosed herein are only furnished pursuant and subject
Load Balancing for Microsoft Office Communication Server 2007 Release 2 A Dell and F5 Networks Technical White Paper End-to-End Solutions Team Dell Product Group Enterprise Dell/F5 Partner Team F5 Networks
Internet Content Distribution Chapter 2: Server-Side Techniques (TUD Student Use Only) Chapter Outline Server-side techniques for content distribution Goals Mirrors Server farms Surrogates DNS load balancing
Network Level Multihoming and BGP Challenges Li Jia Helsinki University of Technology email@example.com Abstract Multihoming has been traditionally employed by enterprises and ISPs to improve network connectivity.
White Paper White Paper June 2013 Resonate Global Dispatch Intelligent Multisite Global Service Level Control Resonate, Inc. 16360 Monterey St., STE 260 Morgan Hill, CA 95037 Phone: 1-408-545-5501 Fax:
Agenda Distributed System Structures CSCI 444/544 Operating Systems Fall 2008 Motivation Network structure Fundamental network services Sockets and ports Client/server model Remote Procedure Call (RPC)
Lecture 7: Distributed Operating Systems A Distributed System 7.2 Resource sharing Motivation sharing and printing files at remote sites processing information in a distributed database using remote specialized
CHAPTER 3 Using IPM to Measure Network Performance This chapter provides details on using IPM to measure latency, jitter, availability, packet loss, and errors. It includes the following sections: Measuring
White Paper The Ten Features Your Web Application Monitoring Software Must Have Executive Summary It s hard to find an important business application that doesn t have a web-based version available and
Fibre Channel over Ethernet in the Data Center: An Introduction Introduction Fibre Channel over Ethernet (FCoE) is a newly proposed standard that is being developed by INCITS T11. The FCoE protocol specification
SonicOS Configuring WAN Failover & Load-Balancing Introduction This new feature for SonicOS 2.0 Enhanced gives the user the ability to designate one of the user-assigned interfaces as a Secondary or backup
Course Name: TCP/IP Networking Course Overview: Learn the essential skills needed to set up, configure, support, and troubleshoot your TCP/IP-based network. TCP/IP is the globally accepted group of protocols
Chapter 2 TOPOLOGY SELECTION SYS-ED/ Computer Education Techniques, Inc. Objectives You will learn: Topology selection criteria. Perform a comparison of topology selection criteria. WebSphere component
Purpose-Built Load Balancing The Advantages of Coyote Point Equalizer over Software-based Solutions Abstract Coyote Point Equalizer appliances deliver traffic management solutions that provide high availability,
CSE 123b CSE 123b Communications Software Spring 2003 Lecture 13: Load Balancing/Content Distribution Networks (plus some other applications) Stefan Savage Some slides courtesy Srini Seshan Today s class
MikroTik RouterOS Workshop Load Balancing Best Practice Warsaw MUM Europe 2012 MikroTik 2012 About Me Jānis Meģis, MikroTik Jānis (Tehnical, Trainer, NOT Sales) Support & Training Engineer for almost 8
IP Telephony Contact Centers Mobility Services WHITE PAPER Avaya ExpertNet Lite Assessment Tool April 2005 avaya.com Table of Contents Overview... 1 Network Impact... 2 Network Paths... 2 Path Generation...
Requirements of Voice in an IP Internetwork Real-Time Voice in a Best-Effort IP Internetwork This topic lists problems associated with implementation of real-time voice traffic in a best-effort IP internetwork.
The Domain Name System (DNS) is a distributed database in which you can map hostnames to IP addresses through the DNS protocol from a DNS server. Each unique IP address can have an associated hostname.
Lab Exercise DNS Objective DNS (Domain Name System) is the system & protocol that translates domain names to IP addresses. Step 1: Analyse the supplied DNS Trace Here we examine the supplied trace of a
Citrix NetScaler Global Server Load Balancing Primer: Theory and Implementation www.citrix.com Background...3 DNS Overview...3 How DNS level GSLB works...4 Basic NetScaler GSLB Configuration...8 Accepting
Application Note 8 Latency on a Switched Ethernet Network Introduction: This document serves to explain the sources of latency on a switched Ethernet network and describe how to calculate cumulative latency
Improving Quality of Service Using Dell PowerConnect 6024/6024F Switches Quality of service (QoS) mechanisms classify and prioritize network traffic to improve throughput. This article explains the basic
The IntelliMagic White Paper on: Storage Performance Analysis for an IBM San Volume Controller (SVC) (IBM V7000) IntelliMagic, Inc. 558 Silicon Drive Ste 101 Southlake, Texas 76092 USA Tel: 214-432-7920