My findings today

So. I generated this graph: http://aml.cs.byu.edu/~davidw/over_v_real_num_servers.pdf

The graph shows that for all of the algorithms that I tried, they all performed similarly. In fact, there's really no incentive to use one algorithm over another.

I'm at the stage of my research where I'd like to fix that.

I tried an idea that Kevin and I had of one stage of the algorithm that finds the correct number of solutions and another stage of the algorithm that spreads out the solution. I carried out a similar GA after finding the first preliminary solution whose purpose it was to spread out the solution. The second GA takes the solutions that the first GA found, and just tries to spread out the items in the bins. I changed the fitness of bins so that instead of preferring one really full bin and one not-so-full bin, it prefers two bins which are both semi-full.

My findings were interesting. I was getting a bit discouraged at first because the second stage was not improving solutions. However, I increased the problem set size, and vwala, the second stage saw improvement. For large problems, it gains more out of the optimization of the second step. This means that the conversation that Kevin and I were having about the problem being too simple to optimize holds rather true. Now, I just need to find some way to show this on real VMs. :)

What I got done Today

Today, I did quite a bit of reading about enterprise computing papers. It really seems like the exact topic that we're addressing has not been addressed in any other papers. There are similar topics in other papers (predicting load on xen applications given the load of the application when it's not running on a hypervisor, making a table to predict loads of virtual machines throughout different days, etc).

I'd like to start writing an enterprise computing paper soon (start tonight / tomorrowish?). I should take one of those papers that I was reading and use that as a baseline. Then I can start moving and changing things in that paper.

Goals for this week

• Develop Energy Measurements


• Be able to run experiment on potatoes, get good usage out of potatoes, and summarize usage


• Create an implementation of Naive Bayes


• Integrate MLP with submission


• Make at least one more good idea with submission


• Do reading for Data Mining


• Prepare well for Matt's Wednesday Meeting

LOLP and LOLE continued

LOLE is the expected number of days per year for which available generating capacity is insufficient to serve the daily peak demand or the hours per year where capacity is insufficient to serve hourly load

LOLE is measured in days/year when it represents a comparison between daily peak values and available generation

LOLE is measured in hours/year when it represents a comparison of hourly load to available generation

LOLP is the proportion in % (probability) of days per year, hours per year, or events per season that available generating capacity/energy is insufficient to serve the daily peak or hourly demand

LOLE & LOLP are methodologies that use probabilistic methods to capture the effect of uncertain parameters such as forced outages, unusual load conditions or hydro conditions on the ability of deliverable generation to meet load; the other major approach is to use deterministic methods and perform scenario analyses

LOLP and LOLE

LOLP (Loss of Load Probability) is the probability that generation will be insufficient to meet demand at some point over some specific time window. Check out http://www.nwcouncil.org/energy/powersupply/presentation1999_1208/sld013.htm.

LOLE (Loss of Load Expectation) is a measure of how long, on average, the
available capacity is likely to fall short of the demand. LOLE is a statistical measure
of the likelihood of failure and does not quantify the extent to which supply fails to
meet demand. LOLE is the expected number of days in the year when the daily peak demand exceeds the available generating capacity. It is obtained by calculating the probability of daily peak demand exceeding the available capacity for each day and adding these probabilities for all the days in the year. The index is referred to as Hourly Loss-of-Load-Expectation if hourly demands are used in the calculations instead of daily peak demands. LOLE also is commonly referred to as Loss-of-Load-Probability.