We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
Published online by Cambridge University Press: 05 March 2015
Modeling the distribution of each workload attribute in isolation is not enough. An important issue that has to be considered is possible correlations between different attributes, as well as between different samples from the same distribution.
Correlations are important because they can have a dramatic impact on system behavior. Consider the scheduling of parallel jobs on a massively parallel machine as an example. Such scheduling is akin to 2D bin packing: each job is represented by a rectangle in processors×time space, and these rectangles have to be packed as tightly as possible. Assuming that when each job is submitted we know how many processors it needs, but do not know for how long it will run, it is natural to do the packing according to size. Specifically, packing the bigger jobs first may be expected to lead to better performance [144]. But what if there is a correlation between size and running time? If this is an inverse correlation, we find a win-win situation: the larger jobs are also shorter, so packing them first is statistically similar to using SJF (shortest job first), which is known to lead to the minimal average runtime [418]. But if size and runtime are correlated, and large jobs run longer, scheduling them first may cause significant delays for subsequent smaller jobs, leading to dismal average performance results [449].
TYPES OF CORRELATION
When we say that workload attributes are correlated, we mean that they exhibit similar behavior. For example, if parallel jobs with more than the average number of processes also run for longer than the average runtime, we say that size and runtime are correlated. In probability theory such relationships are represented by the notion of dependence: random variable X is said to be dependent on random variable Y if knowing Y tells us something about the value of X. When X and Y are strongly correlated, we can use Y to actually predict the value of X.
To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Find out more about the Kindle Personal Document Service.
To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.
To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.
