Cloud Traffic Control Center for Distributed Data Load Balancing
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Solution Overview
Problem
Current cloud computing systems face challenges in efficiently and dynamically managing and controlling computing resources distributed over a network to meet varying service demands, particularly in handling abrupt increments in data traffic across physically distributed data centers.
Innovation Solution
A cloud computing system comprising multiple local data centers and a control center that dynamically distributes data traffic by identifying heavy traffic source regions and redirecting packets to other local data centers based on factors like location and time zone, using virtual machines and monitoring modules to manage and balance traffic loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing resources are physically distributed over a network to provide cloud computing services, then service availability and scalability are improved, but traffic management complexity and response time increase
Solution Approach 1:
A controller is introduced as an intermediary component between user terminals and distributed servers. The controller receives traffic information from servers, determines optimal server assignments based on this information, and generates control signals to redirect traffic. This mediator architecture simplifies traffic management complexity while maintaining the benefits of physical distribution and scalability.
Solution Approach 2:
The system implements a feedback mechanism where servers transmit traffic information to the controller, which then adjusts traffic routing based on real-time conditions. This closed-loop feedback enables dynamic adaptation to changing network conditions, maintaining optimal service delivery while managing the complexity of distributed traffic control.
2Productivity
If computing resources are dynamically expanded according to service demands, then service quality is improved, but network latency and traffic control difficulty increase
Solution Approach 1:
The controller pre-calculates optimal server assignments and prepares routing decisions before traffic spikes occur. By having the controller ready with predetermined server lists and routing rules, the system can rapidly respond to demand changes without introducing significant latency, thus preparing actions in advance rather than reacting after delays occur.
Solution Approach 2:
The system dynamically adjusts server assignments and traffic routing based on real-time traffic information. The controller continuously monitors traffic conditions and modifies control signals to match current demand patterns, enabling flexible adaptation to changing service requirements while minimizing latency through active, real-time adjustment rather than static configurations.
3Productivity
If traffic is distributed to multiple local data centers, then processing load is balanced, but data transmission distance and network overhead increase
Solution Approach 1:
The controller assigns servers based on their specific local characteristics and traffic conditions rather than uniformly distributing traffic. By considering local factors such as geographic proximity to users and individual server performance metrics, the system achieves better load balance while minimizing unnecessary long-distance transmissions, allowing each server to handle traffic optimally for its local context.
Data Source
AI summary
Method for distributing and controlling traffic in cloud computing system and cloud computing system using the same. The cloud computing system may include a plurality of local data centers located at different regions and a control center coupled to the plurality of local data centers through a network. The plurality of local data centers each may be configured to process packets from user equipments in order to provide a requested service. The control center may be configured to distribute packets transmitted to one local data center into at least one other local data centers based on at least one given factor when an amount of the packets to the one local data center exceeds a given threshold.