**Main Research Question** : Is there a platform for cloud services providers with NFV which provide
Network traffic monitoring
Load balancing
Future predictions
Resource allocations
using a single platform ?
• Network traffic monitoring
• Load balancing
• Future predictions
• Resource allocations using a single platform ?
**Main Objective** : To provide a reliable and efficient service to the client by creating a smart cloud platform.
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**Workload allocation** :
Member 1 : **IT18026030** - Network traffic monitoring
Member 1 : IT18026030 - Network traffic monitoring
There are few ways that we can conduct the cloud monitoring. They are virtual network monitoring, website monitoring, cloud storage and database monitoring, and virtual machine monitoring. We can monitor the network traffic through 24/7 or periodically using algorithms. The selected strategy must be able to identify when service-level objectives (SLOs) are not being met, likely negatively affecting the user experience. The cloud administrator can review the operational status and health of any cloud-based device or component using the results obtained with the implementation of monitoring strategy.
Member 2 : **IT18066494** - Network traffic load balancing
Specified sub Objectives :
Generating an output which is categorised and could be used by necessary personnel.
To increase the validity of the generated output using aspects such as
Security
Commercial based
User demand
Member 2 : IT18066494 - Network traffic load balancing
Load balancing in software defined network virtualization can be achieved at two levels: NFV level and SDN level. In order to achieve a load balancing in the SDN network, multiple selections for the least loaded/busy real-time server (RLSs) are made. The task of RLS is to choose the most appropriate route for forwarding each new stream and it is based on network time statuses. Algorithm or routing protocol should be implemented in the routable devices to identify the best path. Also, there are static and dynamic load balancing algorithms.
Member 3 : **IT18080704** - Network traffic prediction
Specified sub Objectives :
Understand the functionality and behaviour of max-min load balancing algorithm and Weighted round robin algorithm to determine the best one.
Implement traffic marking and QoS functionality to the selected algorithm.
Member 3 : IT18080704 - Network traffic prediction
Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. It will be a proactive approach for network management and planning tasks accordingly. In this we will be using predictors such as time series-based predictors, artificial neural networks-based predictors, and wavelet transform-based predictors.
Member 4 : **IT18099928** - Resource allocation
Specified sub Objectives :
Predict using time-based technique.
Past data vs Present data
Get better ideas for future scalability.
Maintain zero downtime .
Line-chart output.
Member 4 : IT18099928 - Resource allocation
There are several resource allocation strategies (RAS) to use according to the client’s requirement and fulfill the needs of the client. For an example, Execution Time - Based RAS, VM-Based RAS and Reservation Based RAS can be considered. The strategy will be selected considering the following scenarios.
• Under provisioning or over provisioning of resources
• Resource contention
• Resource starvation/scarcity of resources
• Resource fragmentation
Specified sub Objective :
To create a proper resource allocation strategy algorithm in order to prevent below mentioned scenarios,
Under provisioning of resources
Over provisioning of resources
Resource contention
Resource starvation/scarcity of resources
Resource fragmentation
To minimize the time and cost of completion of tasks
