Dynamic Resource Allocation in Distributed Computing
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Solution Overview
Problem
Current distributed computing environments face challenges in optimizing resource allocation to meet target completion times and minimize costs, especially in dynamic and decentralized systems like cloud computing, where network failures and varying workload demands are not adequately addressed by existing methods.
Innovation Solution
A method and system for determining optimal resource configuration in a distributed computing environment by identifying and quantifying resources capable of performing assigned tasks, using pre-determined parameters such as target completion time, provisioning parameters, and service parameters, which includes computing optimal resources allocation and dynamically adjusting based on changes in service and configuration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used in distributed computing environments, then system simplicity is maintained, but the ability to meet target completion times and handle dynamic workload demands deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload demands and network conditions, then adjusting resource allocation in real-time to meet changing requirements while maintaining system productivity
Solution Approach 2:
The system employs feedback mechanisms where completion time metrics and workload demands are continuously measured and fed back to the resource allocation algorithm, which then optimizes allocation decisions to improve task completion efficiency
2Reliability
If existing resource allocation methods are used, then system simplicity is maintained, but the ability to minimize costs while meeting service level agreements deteriorates
Solution Approach 1:
The patent changes allocation parameters dynamically based on service level agreement requirements and cost metrics, adjusting resource allocation strategies to minimize costs while ensuring reliability constraints are met
Solution Approach 2:
The system performs preliminary analysis of service level agreement requirements and cost parameters before execution, pre-configuring allocation strategies that will minimize costs while guaranteeing agreement compliance
3Adaptability or versatility
If static resource allocation is used, then system simplicity is maintained, but the ability to handle network failures and varying workload demands deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload demands and network conditions, then adjusting resource allocation in real-time to meet changing requirements while maintaining system productivity
Solution Approach 2:
The distributed system performs self-optimization by autonomously detecting network failures and workload changes, then automatically reallocating resources without external intervention to maintain adaptability
Data Source
AI summary
A method, system and a computer program product for determining resources allocation in a distributed computing environment. An embodiment may include identifying resources in a distributed computing environment, computing provisioning parameters, computing configuration parameters and quantifying service parameters in response to a set of service level agreements (SLA). The embodiment may further include iteratively computing a completion time required for completion of the assigned task and a cost. Embodiments may further include computing an optimal resources configuration and computing at least one of an optimal completion time and an optimal cost corresponding to the optimal resources configuration. Embodiments may further include dynamically modifying the optimal resources configuration in response to at least one change in at least one of provisioning parameters, computing parameters and quantifying service parameters.