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How Content Locality Caching could improve your iPhone “Favorites” contact list (Part 2: Adaptiveness)

Wouldn’t it be amazing if your phone intelligently changed your “Favorites” list based on where you were and who you were calling?

In the first part in this series, I explained how the compression capabilities of Content Locality Caching could improve your smartphone’s “Favorites” contact list. In this part you will be amazed at how smart your phone could be if it used Content Locality Caching. And I hope you will also understand how VeloBit HyperCache is able to perform better than other SSD caching software.

Adaptiveness

I live in Boston so I call a lot of 617 area code phone numbers and I put the most frequent in my mobile phone’s favorites. But when I visit my hometown near Washington, DC, I start dialing a lot of 703 area codes because I call friends and family that I see only when I’m down there. But I’m too lazy to change my phone’s favorites list.

If a smart algorithm determined the “Favorites” list on my phone, it would realize that I am calling different people and change the list of favorites to include the people I will probably call next.

Let’s say I have made some phone calls over the past week, but arrived in DC only yesterday:

Contact

Phone

Calls in the week before going to DC

Mom

703-143-4572

10

Dad

703-143-2993

12

Girlfriend

617-198-4406

7

College Buddy

703-191-1213

1

Family friend

703-146-3587

1

Sister

703-191-2398

1

Plumber

617-189-4674

2

Draino hotline

617-102-2309

2

Indian Takeout

617-189-6148

2

If my favorites list can only hold a few of these contacts, which should it include?

You’ll remember from the first part of this blog series that your smartphone’s “Favorites” list is just like a manual cache containing the people you call the most. Applying caching algorithms to your call history could better select which contacts go in the “Favorites” list. And next you will see how the typical caching algorithm would select a set of “favorite” contacts and how VeloBit’s Content Locality Caching algorithm would do a much better job.

Any decent caching algorithm tries to find the “hot” items using “temporal locality” -- meaning that it would include in the favorites list the contacts that you call frequently and recently. The typical caching algorithm would pick the most frequently called contacts for the past week (an arbitrary time horizon): Mom, Dad, Girlfriend, Plumber, Draino Hotline, and Indian Takeout.

But Content Locality Caching would not pick those same contacts as the best to include in your Favorites list. Unlike other caching algorithms, Content Locality Caching looks inside the content -- applied to this situation it would look for patterns inside the phone numbers. It recognizes the 703- pattern as the most popular pattern. College Buddy, Family Friend, and Sister all share the 703- pattern, so the algorithm would identify those three as better to cache than Plumber, Draino Hotline, and Indian Takeout which do not have the 703- pattern.

(Also note that the compression optimizations described in Part 1 of this series apply as well. So the 703-143 and 617-189 patterns would be recognized and compressed so that the favorites list could hold even more contacts without taking up more space!)

Let’s see how often I call my contacts in the week while I am in DC:

Contact

Phone

Calls while in DC

Mom

703-143-4572

10

Dad

703-143-2993

12

Girlfriend

617-198-4406

7

College Buddy

703-191-1213

7

Family Friend

703-146-3587

4

Sister

703-191-2398

6

Plumber

617-189-4674

0

Draino Hotline

617-102-2309

0

Indian Takeout

617-189-6148

0


While I am in DC, I call College Buddy, Family Friend, and Sister way more often than Plumber, Draino Hotline, and Indian Takeout because they are more relevant to my life at that time. Thus, the Content Locality Caching has done a better job at identifying contacts that should be included in my ever-changing ‘favorites’ list.

(When I return to Boston, the Content Locality Caching algorithm sees the 617- pattern as becoming popular, so it would readjust the “Favorites” list accordingly.)

Note that this behavior is fundamentally different from simply shrinking the caching analysis time horizon: it is not the same as just looking at the past 1 day rather than the past 1 week. The Content Locality Caching algorithm finds items based on their contents matching patterns of popular content. Adding this new dimension dramatically improves the quality of the cache.

Content patterns inside enterprise software applications are often changing. So you can see how having a caching algorithm that looks at the content and adapts accordingly would have more cache hits and thus provide better cache performance.

In summary, VeloBit can deliver superior cache performance because:

  • It looks inside your data and detects shifting popular patterns
  • It chooses what items to cache based on those patterns (among other factors)
  • The cached items have higher cache hits
  • More cache hits means higher performance

The next blog post in this series shows how VeloBit’s algorithms could extend your iPhone’s lifespan. See you then!

Read the original blog entry...

More Stories By Peter Velikin

Peter Velikin has 12 years of experience creating new markets and commercializing products in multiple high tech industries. Prior to VeloBit, he was VP Marketing at Zmags, a SaaS-based digital content platform for e-commerce and mobile devices, where he managed all aspects of marketing, product management, and business development. Prior to that, Peter was Director of Product and Market Strategy at PTC, responsible for PTC’s publishing, content management, and services solutions. Prior to PTC, Peter was at EMC Corporation, where he held roles in product management, business development, and engineering program management.

Peter has an MS in Electrical Engineering from Boston University and an MBA from Harvard Business School.

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