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This section describes the oPOAA’s performance-aware selection process as indicated in Figure 4-5. For each learner request, the oPL system typically generates a list of Suitable and Relevant LOs – a SRLO list. The LO’s suitability is based on the oPL system User Model while the LO relevance depends on the characteristics of the current learning request (learning objective).

Identify and select relevant and suitable LOs

Perform performance oriented selection

Generate final presentation

oPLS: oPOAA: oPLS:

Figure 4-5: oPOAA Algorithm Steps

The SRLO list is then forwarded to oPOAA. Figure 4-6 illustrates the sequence diagram for the subsequent performance-aware selection process.

Client Server

Request learning content

List of relevant & suitable LOs

Calculate Performance Ratings Request LOs

Provide requested LOs

Update DER Log Deliver requested learning content

Deliver presentation

oPLS oPOAA

Figure 4-6: oPOAA Selection Process Sequence Diagram

As the oPL system is aware of servers (DERs) containing different LOs, it is assumed that the SRLO list provided by the oPL system contains the following information for each suitable LO:

 LO_ID - LO’s identification code (unique within oPL system Domain Model);  LO_URL - LO’s Uniform Resource Locators (URLs);

 LO_SR - LO’s suitability rating, ranging from 0 (not suitable at all) to 100 (perfect match to the learner’s profile) as provided by oPL.

The oPOAA performance adaptation process begins upon receipt of the SRLO list from the oPL system. The list is processed in order from the most suitable LO to less suitable ones. The oPOAA Performance Engine calculates performance ratings and generates a performance data enriched SRLO (PSRLO) list. This is the SRLO list extended with LO performance data: object media type, object size and a list of alternative locations as follows:

 LO_TYPE - LO’s type, namely Text, Image (Graphics) and Multimedia (Audio or Video) determined based on the file extension;

 LO_SIZE - LO’s size in kilobytes (kb);

 LO_LOCS - A list of alternative locations (DERs that store the LO).

Sample content of a PSRLO list is given in Table 4-5, where LO_LOCS03 and LO_LOCS04 are lists containing alternative URIs for LO1 (Mat980) and LO2 (Mat344) respectively.

LO_ID LO_TYPE LO_SIZE LO_LOCS

Mat980 Video 4500 LO_LOCS03

Mat344 Image 60 LO_LOCS04

Table 4-5: PSRL List: Sample Content

The content of a LO_LOCS list is derived based on an oPOAA CM table containing data about LO_IDs and associated URLs as given in Table 4-4 (b).

The oPOAA adaptation algorithm selects the LOs from the currently most efficient servers by considering the performance of the DER-oPL system network link. The link performance is calculated based on the logs collected over a number (X) most recent transactions with each DER. The logs are stored for each DER in a sliding window-like structure (in oPOAA PM as indicated in Table 4-3). The DER’s sliding window log is updated with new performance information every time a learning object is delivered from the DER. The measured Throughput is calculated as the quantity of delivered content (Delivered) over the measured delivery time (Duration). All log readings are considered to be of equal importance. Therefore, the estimated RTT and throughput of a server Y (estRTTDERy and estTPDERy) are calculated as the average of

previous recordings of RTT and Throughput. It is calculated for each LO requested from a DER. For each LOj within the provided SRLO List, beginning with the most suitable, POAA PE

calculates expected delivery times (expDelivTimeLOjDERi) for each DERi on which the LO

resides, based on the size of the LO (sizeLOj), on estimated throughput of the hosting server

DERi (estTPDERi) and on estimated delay (estDelayDERi = estRTTDERi/2) along the network link.

The expected download time is calculated based on formula (4.2.3.1).

i i j i j DER DER LO DER LO

estDelay

estTP

size

DelivTime





exp

The DERS with the shortest expected delivery time for the particular LO is sent a request for that

Input: LO_LOCSj: List of servers (DERs) hosting content (LOj) SRLO: List of suitable LOs

DER logs: Collated historic DER-oPL link performance data

Output: SDER: List of DERs that can provide best delivery of the

LOs under the current network conditions

Algorithm:

for LOj  SRLO

for DERi  LO_LOCSj

expDelivTimeLOjDERi = (sizeLOj/estTPDERi + estDelayDERi) endfor

expDelivTimeLOjDERs = min {DERi  LO_LOCSj : expDelivTimeLOjDERi}

SDER <- (LOj, DERs) endfor

Algorithm 4-1: The oPOAA Algorithm in Pseudo-code

This simple algorithm is of low computational complexity. It uses small logs collected over time. The scalability of the solution depends on the number of remote hosts storing learning content utilised by the associated oPL system. The oPL system could deploy content scattered across millions of web servers. However, it could be argued that it is not likely to have copies of a single LO stored on more than M (M < 100) different servers. Most are so far away that they do not need to be considered. A threshold can be introduced where servers with RTT twice as large as the average RTT are not considered. Therefore, while logs about thousands of servers are maintained, the performance calculation considers a small subset (e.g. M) of these.

However, there are limitations as the solution depends on the oPL system to provide LO details and locations (URLs). Furthermore, all recorded performance metrics are considered equally important, so stale logs could affect accuracy of the estimated delivery time which is used for server selection.