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19, Virtual Machines and Resource Management, covers resource management and machine partitioning, tools used in ensuring service levels to users who may define a slow or unpredictable system as one that s not meeting its availability requirements. 20, The Disaster Recovery Plan, examines disaster recovery issues, ranging from legal and personnel issues to practical issues like whose application gets restored first at the DR site. 21, A Resilient Enterprise, is a case study of the New York Board of Trade, who lost their building and systems in the attacks on September 11, 2001, and were ready to get back to business by 8 P.M. that night. 22, A Brief Look Ahead, is our look at technology trends that will shape high-availability design. And 23, Parting Shots, is a quick wrap-up and some parting shots.
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We end each chapter with a few bullet points that we believe sum up the key messages. For 1, it s really quite easy:
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High availability cannot be achieved by merely installing failover software and walking away. The availability problem domain spans the entire data path from client to network to server.
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CHAPTER
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What to Measure
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What would you think about, if it were you, sitting there in an oversized suit, strapped to an intricate and complex network of components, wires, circuits and engines, all procured by the government, from the lowest bidder John Glenn, on his thoughts before his first spaceflight
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We have become obsessed with data. If we can measure it, in theory, we can control it and improve it. Data collected and analyzed becomes the basis for resource allocations and prioritization decisions, ranging from whether or not to buy an additional 10 terabytes (TB) of disk space for marketing, to which server pairs should be turned into clusters for improved availability. Our goal for this chapter is to present some common measurements of availability, and to provide a framework for interpreting those measurements. In 3, The Value of Availability, we ll ascribe pricing and business benefit to this data. In this chapter we discuss the following topics:
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How we measure availability The failure modes, or typical things that can and do go wrong Situations in which measurements may not be valid
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Throughout this book we use resiliency in terms of overall system availability. We see resiliency as a general term similar to high availability, but without all the baggage that HA carries along with it. High availability once referred to a fairly specific range of system configurations, often involving two computers that monitor and protect each other. During the last few years, however, it has
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lost much of its original meaning; vendors and users have co-opted the term to mean whatever they want it to mean. To us, resiliency and high availability mean that all of a system s failure modes are known and well defined, including networks and applications. They mean that the recovery times for all known failures have an upper bound; we know how long a particular failure will have the system down. Although there may be certain failures that we cannot cope with very well, we know what they are and how to recover from them, and we have backup plans to use if our recoveries don t work. A resilient system is one that can take a hit to a critical component and recover and come back for more in a known, bounded, and generally acceptable period of time.
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When you discuss availability requirements with a user or project leader, he will invariably tell you that 100 percent availability is required: Our project is so important that we can t have any downtime at all. But the tune usually changes when the project leader finds out how much 100 percent availability would cost. Then the discussion becomes a matter of money, and more of a negotiation process. As you can see in Table 2.1, for many applications, 99 percent uptime is adequate. If the systems average an hour and a half of downtime per week, that may be satisfactory. Of course, a lot of that depends on when the hour and a half occurs. If it falls between 3:00 A.M. and 4:30 A.M. on Sunday, that is going to be a lot more tolerable on many systems than if it occurs between 10:00 A.M. and 11:30 A.M. on Thursday, or every weekday at 2:00 P.M. for 15 or 20 minutes.
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Table 2.1 Measuring Availability PERCENTAGE UPTIME 98% 99% 99.8% 99.9% 99.99% 99.999% 99.9999% ( six 9s ) PERCENTAGE DOWNTIME 2% 1% 0.2% 0.1% 0.01% 0.001% 0.0001% DOWNTIME PER YEAR 7.3 days 3.65 days 17 hours, 30 minutes 8 hours, 45 minutes 52.5 minutes 5.25 minutes 31.5 seconds DOWNTIME PER WEEK 3 hours, 22 minutes 1 hour, 41 minutes 20 minutes, 10 seconds 10 minutes, 5 seconds 1 minute 6 seconds 0.6 seconds
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