Scalable Remote Processing Architecture for Distributed Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computing applications are inadequate for solving complex technical problems due to memory, processing, and display limitations, often resulting in system crashes, long processing times, and erroneous results in fields like science, engineering, and medicine.
Innovation Solution
A scalable remote processing architecture that allows clients to utilize remote processing resources over a network, dynamically selecting and configuring units of execution for parallel or serial processing based on client-defined parameters, resource availability, and pricing structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If standalone computing environments are used, then simplicity of operation is maintained, but processing power and memory capacity are insufficient for complex problems
Solution Approach 1:
The system divides processing tasks into discrete units that can be distributed across multiple remote computing resources. Each unit of execution represents a segment of the overall computational workload, allowing complex problems to be broken down and solved across distributed nodes rather than requiring a single powerful standalone system.
Solution Approach 2:
A remote processing application serves as an intermediary between the user's simple interface and complex distributed computing resources. This mediator handles task distribution, resource management, and result aggregation, allowing users to access powerful computing capabilities without directly managing system complexity.
2Productivity
If more processing resources are allocated, then processing speed increases, but system complexity and resource management difficulty increase
Solution Approach 1:
The system implements self-service mechanisms where the remote processing application automatically manages resource allocation, task distribution, and load balancing across distributed computing nodes. The system monitors resource availability and dynamically adjusts processing assignments without requiring manual intervention, enabling high productivity while minimizing management complexity.
Solution Approach 2:
The system dynamically changes operational parameters such as the number of active processing units, task parallelization degree, and resource allocation strategies based on problem characteristics and available computing resources. This allows optimal processing speed to be achieved by adapting parameters rather than requiring fixed complex resource management structures.
3Loss of time
If complex problems are solved using standalone environments, then self-sufficiency is maintained, but processing time becomes unacceptably long
Solution Approach 1:
The system transitions from a single-dimension standalone processing model to a multi-dimensional distributed processing architecture. By adding the spatial dimension of network-distributed computing nodes, the system can solve complex problems in parallel across multiple processors, dramatically reducing processing time while maintaining user-friendly access through the remote processing application interface.
Data Source
AI summary
Exemplary embodiments may employ techniques for dynamically dispatching requests to resources operating in a distributed computing environment, such as a computing cloud, according to one or more policies. Embodiments may further dynamically adjust resources in the computing environment using predictive models that use current loads as an input. Embodiments may still further maintain a state for a processing environment independent of the type or configuration of a device used to access the environment on behalf of a user.