Home > Articles > Programming > General Programming/Other Languages

Modeling Sun Cluster Availability

Modeling the availability of software systems is an extremely difficult task that has not been accomplished successfully to any degree of accuracy until now. This article describes the approach taken to model specific Sun Cluster stacks, including the service layer. This modeling methodology combines black-box measurements with white-box analysis to arrive at an availability model for a system. The methodology facilitates extrapolation of the model to other stacks that differ in well-defined ways. It also entails setting availability budgets of various layers in the stack.
Like this article? We recommend

Maximizing the availability of computer systems and services is becoming the primary focus in IT environments today 1,2. Coupled with a desire to use lower cost solutions than traditional fault tolerant systems, this focus has resulted in an exponential growth in the deployment of clusters as highly available platforms. Clusters typically use redundant off-the-shelf hardware components together with specialized software to provide highly available services at a lower cost.

Evaluating the availability offered by a clustering solution can be done in a variety of ways, ranging from analyzing the availability offered by the system to measuring it, both in pre- and post-deployment phases. Typically, analyzing complex software systems such as clusters is significantly harder than measuring the corresponding metrics, but at the same time, the availability model derived from analysis is more accurate. Software modeling also becomes increasingly difficult as more software components are added to the system. Evaluating software reliability and availability has been studied both in academia and in industry for the past few decades3. While much progress has been made in this research, an accurate assessment of general software availability remains an intractable task.

The inherent difficulty of accurately evaluating software availability is primarily due to the exponential number of ways in which its various software components can interact in different usage scenarios. This is exacerbated by the following facts:

  • a significant percentage of these interactions are typically not completely understood due to the components having been created by different groups.

  • Software components keep changing at a fast pace that defies expedient analysis of their interactions with other potentially changing components.

However, there is a huge demand to evaluate the availability of a clustering solution for several reasons:

  • Customers can use this metric as a yardstick to differentiate among clustering solutions offered by different vendors.

  • Customers can use this metric to determine the availability of their applications running on the cluster.

  • Software designers can use this metric to help understand what part of the software needs redesign or changes to improve the availability of the system.

This article reports on an effort underway at Sun to model Sun Cluster availability for specific configurations that include the data service layer. It describes the proposed methodology that employs a hybrid approach to tackle the inherently NP-complete4 nature of this task, with the aim of delivering a reasonably accurate availability model.

The remainder of this article is organized as following sections:

"RAScad Modeling" describes a top-level availability model of a two-node Sun Cluster configuration using Sun's RAScad tool5. In the context of this article, the term node refers to a server machine.

"Proposed Methodology" discusses the salient components of the methodology proposed for modeling Sun Cluster availability.

"The Stack" details the hardware and software configuration (referred to as a stack in this article) under investigation, and gives some preliminary results measured for this stack. These values are applied to the RAScad model, and the results of differential analysis are presented in this section. This analysis yields a set of best practices for any enterprise.

"Best Practices" discusses a set of best practices for any enterprise.

"Conclusion" presents concluding remarks.

RAScad Modeling

The starting point of modeling a Sun Cluster system is to build a top-level system availability behavioral model with RAScad. RAScad is a Sun internal reliability, availability, and serviceability architecture modeling and analysis tool for use in computer system design and development phases. It highlights the different variables that contribute to system availability, as well as the degree to which they affect it.

FIGURE 1 shows the RAScad Markov model for a two-node Sun Cluster stack for any fault that causes one of the nodes to go down. This fault could be an operating system panic or any hardware fault causing the node to fail. The data service is a scalable service, meaning that each of the two nodes is hosting the service actively. A node going down results in reconfigurations of the clustering framework as well as of the service related components in this stack.

Figure 1FIGURE 1 RAScad Availability Model for a Two-node Sun Cluster Stack

In FIGURE 1 a vertex in the graph represents the system state, and an edge represents the failure rate of the transition between the source and sink states of that edge. A value of 1 marks a state if it represents the service being up or available, otherwise a value of 0 marks the state.

Under steady-state, the cluster is in state Both_Up where, as the name implies, both nodes are functional, servicing client requests. The cluster can transition out of this state if one of the nodes goes down; the rate of either of the two nodes dying is 2/MTBF, where MTBF is the Mean Time Between Failures for a node. This event takes the cluster into the Recovery state, where it remains unavailable while the clustering and the service components reconfigure. The reconfiguration completes successfully with a probability p, and takes Recovery_Time to do so. A successful reconfiguration takes the cluster into an available state, One_Up.

In state One_Up, the surviving node services client requests, but at a higher load level since clients that were being serviced by two nodes are now being serviced by just one. This causes the failure rate of the node to increase by a factor of a, which results in a corresponding decrease in the value of the MTBF of the node.

The time taken to repair the failed node is MTTR_1, where MTTR stands for the Mean Time to Repair a node. After being repaired, it is booted back into the cluster. When it joins, the cluster goes into an unavailable state Node_Rejoin, while the joiner node gets refreshed with the existing cluster state. The average time taken for this is Node_Rejoin_Time, after which the cluster enters the original available state, Both_Up.

The node in state One_Up can go down with a failure rate of (1+a)/MTBF, and the cluster would enter state Both_Down, where both nodes of the cluster have gone down.

Following the second outgoing path from state Recovery, an unsuccessful reconfiguration takes the cluster to an unavailable state, Both_Down. Assuming a repair policy where both nodes are repaired at the same time, the average time to repair and boot back the two nodes is MTTR_2, after which the cluster goes back to the original, available state, Both_Up.

Once the key parameters in this model are identified as described previously, RAScad can be used to compute steady state system availability, system yearly downtime and other related useful metrics. Furthermore, a differential analysis of the effect of the various parameters on the availability of the system for specified value ranges provides very useful information not only about what factors to focus on in product design, development, and testing, but also to tailor the availability assessment efforts to an appropriate subset of scenarios. Perhaps most importantly from a customer's perspective, by clearly pinpointing the outages that must be avoided in order to maintain high availability of a system, this differential analysis helps formulate a set of rules or best practices that should be adopted in any Sun Cluster environment to maximize availability in any enterprise. In "Best Practices," this model is used for computing the various availability metrics and to perform a differential analysis for the stack under discussion.

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020