Consumer Device Cloud Computing for Low-Latency Distributed Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud computing infrastructure relies heavily on high-power computers and servers, leading to high costs, energy consumption, and maintenance expenses, while end users' applications are vulnerable to disruptions due to centralized processing.
Innovation Solution
Utilize consumer devices like smartphones and tablets as distributed computing resources, selected based on proximity and characteristics, to supplement cloud infrastructure, reducing costs and enhancing scalability and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized cloud infrastructure is used, then computational power is consolidated, but energy consumption and maintenance costs increase
Solution Approach 1:
The patent segments the centralized cloud infrastructure into distributed consumer devices. Instead of consolidating all computational power in centralized data centers, the system divides the workload across multiple consumer devices (smartphones, tablets, laptops) that are geographically dispersed. Each device runs a client job manager that can independently execute computational tasks, thereby distributing energy consumption across many smaller units rather than concentrating it in large power-hungry data centers.
Solution Approach 2:
The patent utilizes consumer devices that are already owned by users as computing resources. These devices are not expensive specialized servers but rather affordable consumer electronics that users already have. By leveraging these existing devices for cloud computing tasks, the system avoids the need to invest in additional expensive infrastructure while still providing computational capabilities.
2Power
If centralized cloud infrastructure is used, then computational resources are consolidated, but maintenance expenses increase
Solution Approach 1:
The patent implements self-service through the client job manager on consumer devices. Each consumer device independently manages its own computational tasks, tracks its own usage parameters, and communicates status to the cloud service provider. This eliminates the need for centralized maintenance teams to physically access and service every server, as the distributed architecture allows each device to autonomously manage its operations and report issues.
Solution Approach 2:
By segmenting the infrastructure into distributed consumer devices, the patent reduces the maintenance burden on centralized systems. Instead of maintaining a single large centralized infrastructure that requires specialized technical staff for physical access, repairs, and updates, the system distributes maintenance tasks across many smaller units that users can manage independently or through automated remote monitoring.
3Device complexity
If centralized processing is used, then system control is simplified, but network latency increases
Solution Approach 1:
The patent applies local quality by executing computational tasks locally on consumer devices close to the end user rather than remotely in centralized data centers. This ensures that processing occurs at the location where the data is being accessed or used, minimizing the distance that data must travel and thereby reducing network latency. Each consumer device maintains local control over its computational tasks while still coordinating with the cloud service provider.
4Adaptability or versatility
If consumer devices are used as computing resources, then scalability improves, but device complexity increases
Solution Approach 1:
The patent leverages the universality of consumer devices by enabling them to serve multiple functions: they act as both end-user computing devices and as cloud computing resources. The same smartphone or tablet that users interact with daily can also execute cloud-based computational jobs through the client job manager. This multi-functionality allows the system to scale by simply adding more consumer devices to the network without requiring specialized hardware infrastructure.
Solution Approach 2:
The patent implements feedback mechanisms where consumer devices communicate their availability, capability, and status parameters to the cloud service provider. This feedback loop enables dynamic allocation of computational tasks based on real-time device conditions, allowing the system to scale flexibly by activating or deactivating consumer devices as needed based on current demand and device availability.
Data Source
AI summary
A system and corresponding method consumerize cloud computing by incorporating consumer devices into an infrastructure of cloud computing environment. The consumer device comprises a client job manager that spawns a processing task on the consumer device responsive to a job request to perform at least a portion of a computational job. The computational job is requested by an end user device to be performed via cloud computing. The consumer device further comprises a network interface. The job request is received via the network interface from a cloud job manager of a cloud service provider system of a cloud service provider. The processing task performs the at least a portion of the computational job. The consumer device is selected by the cloud job manager based, at least in part, on proximity of the consumer device to the end user device and at least one characteristic of the consumer device. The client job manager communicates the at least one characteristic to the cloud job manager via the network interface.