Carmela Comito and Domenico Talia. University of Calabria & ICAR-CNR

Transcription

1 Carmela Comito and Domenico Talia University of Calabria & ICAR-CNR

2 Introduction & Motivation Energy-aware Experimental Performance mobile mining of data setting evaluation 2

3 Battery power is a vital resource for mobile computing Can either increase supply or reduce demand Increasing the supply of energy is difficult: Battery constrains size, weight of mobile device Battery technology improving slowly Processing requirements also increasing 3

4 Lots of effort aimed at reducing energy demand Hardware-based techniques Low-power Circuits, architectures, protocols Power management Right power at the right place at the right time Recent devices (CPU, disk,) support multiple power modes Dynamic voltage scaling Low-power network design (e.g., Bluetooth) But, lower-level efforts have not been enough! Can the higher levels of the system help?

5 Adapt resources based on system layers Adaptive Layers Application Operating System /Network Hardware Most approaches in research adapt a single layer Possible to adapt across multiple layers?

6 Applications can reduce energy, but usually at a cost! Dynamic balancing of quality and energy conservation Energy-aware adaptation: significantly reduces energy usage complements hardware power management often exhibits predictable effect Resource Monitors Applications The application periodically: measures energy supply predicts energy demand os Redirector 6

7 Defer decisions as much as possible: Applications statically expose possible tradeoffs OS dynamically advises which tradeoffs are best When demand exceeds supply: Applications adapt to conserve energy usage When supply significantly exceeds demand: Applications increase data fidelity Energy En erg y su pp ly Energy Demand Time 7

8 The main focus today is on energyaware algorithms, tasks, applications. The other side of the coin is data and costs of operating on it. Abstract energy-cost models for exchanging, accessing and transform data are primary elements for energy-aware data management at large scale. They are useful for sustainable data science. 8

9 Data is everywhere, ubiquitous Big, complex, real-time, unstructured Every life process today is data intensive. The information stored in digital data archives is enormous and its size is still growing very rapidly. 9

10 Some decades ago the main problem was the shortage of information, now the challenge is the very large volume of information to deal with and the associated complexity to process it and to extract significant and useful parts or summaries. 10

11 We evaluated the energy cost of analyzing data by using some well-known data mining techniques on mobile devices. Our interest was mainly on how the same technique consumes energy when dimension of data change. Tests with different Data set dimensions, Attribute number, Class number. 11

12 Data mining is the process of automating finding implicit, previously unknown, and potentially useful information from large volumes of data We focused on algorithms for Data clustering Association rule mining Decision tree induction 12

13 A variety of powerful mobile devices is available Smart phones, PDAs, laptops, netbooks, Wireless networks are always more end-user oriented Emergence of the ubiquitous computing paradigm Mobile users perform intelligent analysis and monitoring of data Applications Risk management Collaborative computing, 13

14 Transportation Smart cars and smart roads Onboard systems talk to the road : Map obstacles and delays Inform the road of its actions Emergency search-and-rescue operations Police Fire Ambulance Military usage Real-time feedback for the battlefield Movement of intelligence Mobile Healthcare Smart Home 14

15

16 16

17 Due to resource restrictions a mobile node can not execute a complete data mining process: execute only a step of the knowledge discovery process: preprocessing, data mining, visualization, etc adapt a given data mining algorithm to the capability of the node: light-weight version of the algorithm collaboratively execute a data mining algorithm with stationary and mobile nodes partially execute a data mining algorithm: if the resource measurements indicates that the device can not achieve the required accuracy according to the incoming data rate, it sends a data mining request to a data mining server (either stationary or mobile) to continue a current 17

18 Identify the energy consumption characteristics of some commonly used data mining algorithms running on-board a mobile device Experimentally quantified the performance of specific data mining algorithms Machine learning approach to predict energy consumption of mobile devices to perform data mining algorithms 18

19 Energy characterization of data mining algorithms running on-board mobile devices K-Means (data clustering) J48 (data classification) Apriori (association rules) Common performance parameters Number of instances (data set size) Number of attributes Algorithm-specific performance parameters K-Means: number of clusters J48: decision tree size, confidence support Apriori: Number of rules, minimum support and minimum confidence 19

20 Perform data mining algorithms over an Android smartphone Algorithm selection Data set selection Performance parameters setting Gather of statistics about the mobile device and the algorithm during its execution Battery level CPU occupancy Memory usage Execution time 20

21 Each algorithm has been executed 10 times the values reported in the experiments are the average values of CPU, energy and execution time consumed The results are obtained with two Android smartphones Sony Xperia P, a 1 GHz Dual Core ARM processor with 1 GB RAM Each test is executed with the following setting 100% battery level radio signals off (airplane mode) automatic shut-off display off 21

22 Data Sets Census ( Used with K-means Data set size: 14 Number of instances: Number of attributes: 11 Census_disc ( Used with Apriori Data set size: 19 Number of instances: Number of attributes: 11 Covertype ( Used with J48 Data set size: 14.5 Number of instances:

23

24 Method Algorithm Data Set Size RAM Memory (yte) Virtual Memory (yte) CPU (%) Battery Charge Depletion (mah) Energy Consumption (J) Time (sec) Association Rules CENSUS_DISC.arff Rule Induction Apriori 0,1 0,2 0,4 0,8 1,6 3,2 15,86 16,97 18,06 19,87 23,32 26,92 95,19 105,36 104,95 102,75 103,99 100,01 96,92 98,03 98,24 98,13 96,87 95, ,7 13,5 23, , ,82 310, , ,47 20,15 23,87 27, ,94 104,92 105,6 103, ,4 29,8 59,4 194, , , , , ,75 8,1 18,9 18,9 43, ,91 107, , , , Classification COVERTYPE.arff Trees J48 0,1 0,2 0,4 0,8 1,6 3,2 6,4 96,23 98,21 97,43 97, Clustering CENSUS.arff Instancebased/La zy Learning K-Means 0,1 0,2 0,4 0,8 1,6 3,2 6,4 16,73 17,95 19,72 23,08 26, ,56 102,05 102,16 101,86 95, ,03 97,65 97,02 97,97 97,

25 A way of grouping together data samples that are similar in some way - according to some criteria that you pick A form of unsupervised learning you generally don t have examples demonstrating how the data should be grouped together So, it s a method of data exploration a way of looking for patterns or structure in the data that are of interest

26 Choose a number of clusters k Initialize cluster centers µ1, µk Could pick k data points and set cluster centers to these points Or could randomly assign points to clusters and take means of clusters For each data point, compute the cluster center it is closest to (using some distance measure) and assign the data point to this cluster Re-compute cluster centers (mean of data points in cluster) Stop when there are no new re-assignments The computational complexity of K-means is O(nmk)

27 Number of instances and number of clusters 27

28 Number of attributes and number of instances 28

29 Find the set of all subsets of items that frequently occur in database records. In addition, ARM applications extract rules regarding how a given subset of items influence the presence of another Two key parameters Confidence (A B) = #tuples containing both A & B / #tuples containing A = P(B A) = P(A U B ) / P (A) Support (A B) = #tuples containing both A & B/ total number of tuples = P(A U B) What do they actually mean? Find all the rules A & B C with minimum confidence and support support, s, probability that a transaction contains {A, B, C} confidence, c, conditional probability that a 29

30 Apriori is arguably the most influential ARM algorithm Apriori has two phases of execution 1. Find the frequent itemsets: the sets of items that have minimum support A subset of a frequent itemset must also be a frequent itemset i.e., if {AB} is a frequent itemset, both {A} and {B} should be a frequent itemset Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset) 1. Use the frequent itemsets to generate 30

31 If m is the number of instances and n the number of distinct attributes, the number of potentially frequent itemsets is O(2n) and the overall computational complexity of the algorithm is O(m2n). Performance parameters data set size (number of instances) number of attributes minimum support minimum confidence 31

32 Reducing the generation of frequent itemsets Number of instances and attributes 32

33 The objective of a classification algorithm is to use a training dataset to build a model such that the model can be used to assign unclassified observations into one of the defined classes. Decision trees are a common knowledge representation used for classification. In classification, the decision tree predicts, based on data from a specific instance, the membership class of an instance. Each node in the tree consists of a test, based on one of more attributes of the instance to be classified. The leaf nodes provide the class label. 33

34 Creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. Reduced-errror based pruning Post-pruning Confidence factor Online-pruning Number of instances per node

35 The computational complexity of C4.5 is O(mn2) where m is the number of instances and n the number of attributes Performance parameters Number of instances Number of attributes Confidence factor Minimum number of instances per node 35

36 Tree pruning Number of instances and number of attributes

37 Results obtained with different smart phones Sony Xperia P: 1 GHz Dual Core ARM processor and 1 GB RAM HTC Hero: 528 MHz Qualcomm processor and 288 RAM Samsung Galaxy ACE: 800 MHz Qualcomm processor and 512 RAM

38 Energy-aware adaptation is an important part of a comprehensive energy management strategy! The main goal is to converse energy without affecting usability A collaborative relationship between the operating system and applications can effectively reduce the energy usage of mobile computers. Data-intensive applications demands for energy cost models based on data characteristics. 38

39