- freely available
Sustainability 2017, 9(11), 2127; doi:10.3390/su9112127
- An integrated priority metric is developed so that the priority of an incoming request to a suitable BBU can be identified.
- Computational resource allocation problem for incoming requests is formulated as multi-objective non-linear programming optimization problem focusing on maximization of end-user QoE as well as service provider profit.
- Tradeoff between profit and customer satisfaction while selecting the BBUs for service provisioning in CRAN is made by two scheduling algorithms which are computationally viable to be deployed.
- To enhance system performance and resource utilization, the duration of the scheduling interval is determined dynamically according to the incoming requests and available resources.
2. Related Works
3. System Model and Assumptions
4. Proposed Resource Allocation Scheme
4.1. Incoming Request Prioritization
- Amount of data to be processed,
- The tolerable service delay,
- Received signal strength of the connected device, .
4.2. Optimal Problem Formulation
- BBU Constraint: The total number of BBUs in a pool must be constrained as
- Capacity Constraint: The capacity constraint represents that the sum of the processing capacities of the BBUs in a pool must be constrained by the total capacity of a BBU pool. This can be represented as
- Request Assignment Constraint: It ensures that at a time, each request b of one RRH is always assigned to one BBU of a BBU pool,
- Virtual BBU Allocation Constraint: The BBU allocation constraint defines that at a given time, one BBU will be allocated for one request, which is represented as
- Profit Constraint: The profit constraint can be represented as
- QoS Constraint: The QoS constraint can be represented as
4.2.2. Computational Complexity of Resource Allocation Scheme
4.3. Tradeoff between Customer Satisfaction and Service Provider Profit
4.3.1. Satisfaction Optimization with a Profit Bound
|Algorithm 1 First Fit Satisfaction Algorithm for Maximizing User Satisfaction|
|INPUT: Processing Capacity of all BBUs, , priority of each incoming request, , on a scheduling interval and QoS of the incoming requests.
4.3.2. Profit Optimization Under a Satisfaction Target
|Algorithm 2 First Fit Profit Algorithm for Maximizing Service Provider Profit|
|INPUT: Weighted priority of each incoming request, , processing capacity of all BBUs, .
5. Performance Evaluation
5.1. Simulation Environment
5.2. Performance Matrices
5.2.2. Percentage of Requests Satisfying QoS
5.2.3. Average Waiting Time
5.2.4. Service Provider Profit
5.3. Results and Discussion
5.3.1. Impacts of a Varying Number of Incoming Requests
5.3.2. Impacts of Varying Average QoS Requirement per Request
Conflicts of Interest
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|State-of-the-Art Works||QoS||User QoE||Profit||Resource Utilization|
|VBS Provisioning ||(Partially)|
|RRH Clustering ||✓|
|R||Set of all incoming computational resource requests|
|H||Set of all remote radio heads (RRHs)|
|B||Set of all base band units (BBUs) in a BBU Pool|
|Set of attributes of a request,|
|Incoming data of a request, to be processed|
|The QoS requirement of an incoming request,|
|The received signal strength of the connected device associated with a request|
|The priority of an incoming request|
|Set of objective parameters considered for executing a request,|
|Cloud service providers’ profit for executing a request, on a BBU,|
|Total time requires a RRH, to get first response from a BBU, after executing a request,|
|The number of scheduling intervals required for a request, to be assigned to BBU|
|Scheduling interval of the system for allocating resources|
|BBU rental cost for executing a request|
|Monetary cost for other resource usage|
|Number of BBU||5|
|BBU processing speed||20∼50 MHz|
|Number of RRH||10|
|Incoming data per request to be processed||20∼600 KB|
|Maximum allowable delay (QoS)||20∼200 ms|
|RSSI value||−15∼−75 dB|
|Simulation Duration||500 s|
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