Cuarta etapa (2000 – 2014): integración de la cultura y el clima

The placement of event processing networks requires dynamic movement to improve the response time across continuous iterations. The event streams involve execution of long running calculations on the performance of the event processing networks. Many applications exhibit significant run time variations that require incremental or decremented processing requirements.

The reconfiguration algorithm determines the count of computing nodes required to process the EPNs and the order in which the EPNs are reconfigured within the avail- able computing nodes. The algorithm focuses on the EPNs whose performance is affected. When a single node hosts multiple EPNs, the configuration scheduler places the EPN in an appropriate computing node to meet the response time target. The re- configuration algorithm maintains the optimum resources by scaling up, scaling down or optimal movement of EPN. The configuration scheduler delivers the target response time T of the applications by hiring the minimum number of cloud computing nodes. The high level architecture of the configuration scheduler as illustrated in Figure 4.3

follows the three main aspects listed below to meet the target response time even when a single node hosts multiple EPNs.

1. Shifting some EPNs among existing hired nodes 2. Hiring more nodes when necessary

3. Releasing nodes when arrival rates decrease

The design goals for the reconfiguration of the EPNs focus on minimizing the latency of the event processing. The configuration of data sources and the computing nodes are achieved using a decentralised and asynchronous architecture. All computing nodes share the same functionality and responsibilities. There is no central computing node with specialised responsibilities. The reconfiguration of the EPNs are based on five parameters: the measured response time RTi (as periodically reported to CS),

the target response time Ti, total arrival rate ARi, the processing time bi and an

estimated response time Wi. The main functions of the configuration scheduler are

summarised below:

7.2.1

Grouping of EPN:

The configuration scheduler finds the optimum set of EPNs to host in a single com- puting node. Each computing node hosts multiple EPNs. For example, consider a scenario where N odei hosts EP N1, EP N2 to process events from data source A1, A2,

N odej hosts EP N3 to process events from data source B1 and so forth. Due to the

fluctuating arrival rate, load sharing needs to be performed frequently to maintain a stable response time target (T). Grouping of the EPNs across two nodes can be accomplished to utilise the minimum number of nodes. Grouping of the EPNs is dependent on the individual response time. The grouping of the EPNs should be determined using the relative deviation δm

i of the measured response time (RTi) and

the average target response-time (Ti) for each EP Ni.

7.2.2

Optimum response time:

The average performance of the computing node is influenced by the arrival rate (ARi) and complexity of the EPN. The complexity of the EPN is identified using

the average service time, b and the second order moment, M2,i simplified to be the

squared coefficient of the service time variance b2_i. The service time for each EPN is obtained through the calibration process described in § 6.1.1. When the arrival rate increases, both the queuing delay and processing delay increase leading to a heavily loaded system. For fixed arrival rate of events ARi and b, the best performance is

achieved when b2

i = 0. The reconfiguration of EPNs across multiple computing nodes

7.2.3

Balanced resource utilization and response time:

Basic performance measurement of any EPN is dependent on the steady-state utiliza- tion of the hardware resource in the computing node. In a single node, multiple EPNs with varying complexities are likely to compete for resources. Consider the arrival of events in n EPNs . The total load utilization of the computing nodes is

U = Σn i=1ρi

Regardless of the complexity of processing, all EPNs should maintain the target re- sponse time. The reconfiguration algorithms should maintain the stable response time under varying event arrival.

7.2.4

Optimum resource utilization:

In continuous event processing, the arrival of events is estimated based on a single data source or by merging events from large number of independent sources. Events are assumed to arrive according to a poisson process with arrival rate λ. Events wait in an unbounded M/G/1 queue. The computing nodes undergo an alternating periods of being idle and busy. An idle period starts with the departure of the event leaving an empty queue and ends with the arrival of the next event.

A busy period in the computing nodes start with the arrival of events until an empty queue is achieved. A busy period in the server of any order should not affect the re- sponse time and optimum number of the computing nodes should be hired to maintain the response time of EPNs.