Multi-Cloud + Private Computing Hybrid Scheduling Platform
Beta Hybrid Cloud Compute Orchestrator
Abstract multiple computing resources, intelligently schedule and optimize utilization to reduce costs, improve efficiency, and maximize computing value.
Background
Enterprises have multiple computing resources including local data centers, public clouds and edge devices.
Resource Waste
Uneven utilization and chaotic scheduling lead to cost waste and business risks.
Low Toolchain Integration & Collaboration Efficiency
Development teams use multiple independent tools, severe data silos, high cross-team collaboration costs, affecting development efficiency.
Solution Content
Resource Ledger
Abstract modeling of heterogeneous compute resources, recording performance, cost, location and availability windows, forming a 'compute asset ledger'.
Policy Scheduling
Set policies based on business priority, real-time load and SLA, prioritize local clusters for training, start public cloud during peak periods; prioritize edge or nearby data centers for low-latency inference.
Unified Access
Provides unified interface for R&D and business, declare computing level, duration and budget cap, system automatically selects optimal resource combination.
Continuous Optimization
Monitor and optimize overall utilization, reduce ineffective investment.
Key Advantages
Let data speak — let results be visible
Cost Optimization
Hybrid scheduling reduces 40% computing costs
Resource Utilization
Average resource utilization improved to 85%+
Unified Management
Unified view for all computing resources
Elastic Scaling
Second-level auto-scaling for peak/valley handling
Technical Capabilities
Deep technical capabilities powering your business
Multi-cloud Resource Unified Abstraction
Uniformly abstracts heterogeneous computing resources including local data centers, public clouds (AWS/Azure/Alibaba/Tencent), and edge nodes, establishing unified resource ledger recording performance, cost, availability and other attributes
Intelligent Scheduling Strategy
Multi-dimensional intelligent scheduling based on business priority, real-time load, cost budget, SLA requirements, prioritizing local clusters for training tasks, automatically starting public cloud during peaks, scheduling inference tasks nearby to reduce latency
Unified Access Interface
Provides unified API and CLI tools for R&D and business, users only need to declare computing requirements (GPU quantity, performance level, duration, budget), system automatically selects optimal resource combination and schedules
Cost Optimization Analysis
Real-time monitoring of resource pool utilization and costs, providing visual analysis dashboard, identifying idle resources and cost optimization opportunities, supporting cost attribution analysis by project/department/user
Target Industries
This solution is designed for the following industries and use cases
Enterprises with Multi-DC/Cloud Resources
Large Group Enterprises
Research Institutions
Cloud Service Providers
Implementation Process
Expert-guided implementation ensuring seamless project delivery
Resource Inventory
Review existing computing resources and usage
Platform Deployment
Deploy scheduling platform and monitoring system
Resource Integration
Integrate various computing pools and cloud platforms
Policy Configuration
Configure scheduling policies and cost optimization rules
Gradual Rollout
Partial task trial, optimization, full rollout
FAQ
Common questions answered
Platform reduces costs through multiple strategies: 1) Resource pool priority: Training tasks prioritize local clusters (sunk cost), using public cloud only during peaks or insufficient local resources; inference tasks choose edge or cloud based on latency requirements; 2) Spot instance utilization: Automatically uses public cloud spot instances for interruptible tasks, reducing costs 70%+; 3) Off-peak scheduling: Non-urgent tasks scheduled during off-peak hours, leveraging low-peak pricing; 4) Resource reclamation: Automatically detects and releases idle resources, avoiding waste; 5) Cost budget control: Supports setting project/department budget limits, auto-downgrading or pausing when exceeded. In actual cases, enterprise overall costs typically reduced 30-50%.
We use multiple mechanisms to ensure scheduling stability: 1) Unified orchestration layer: Based on standard orchestration engines like Kubernetes, shielding underlying cloud platform differences with unified task definitions; 2) Health checks: Real-time monitoring of resource pool availability, automatic isolation of failed nodes and task migration; 3) Graceful degradation: Lower priority tasks automatically yield during resource constraints, ensuring critical business; 4) Resume from checkpoint: Supports task checkpoints, can resume from breakpoint after interruption, avoiding recalculation; 5) Multi-replica redundancy: Critical tasks support multi-replica execution, single point failure doesn't affect results; 6) Gradual switching: New resource pools trial non-critical tasks before going live,承接 core business after stability verification.
Data transfer is a key hybrid cloud challenge, we provide multiple optimization solutions: 1) Data locality: Scheduling prioritizes data location, avoiding large-scale data transfer; training data typically stays local, only model parameters transferred during inference; 2) Incremental sync: Only syncs changed data, reducing transfer volume; supports resume from breakpoint and chunked transfer for improved reliability; 3) Cache mechanism: Common datasets pre-cached in multiple resource pools, reducing redundant transfers; 4) Dedicated network: For frequent cross-cloud transfer scenarios, recommend dedicated lines to reduce costs and latency; 5) Data tiering: Hot data in high-speed storage pools, cold data archived to low-cost storage, retrieved on demand. For sensitive data, supports configuring data-stays-local policy, only scheduling compute tasks to data location.
Yes, the platform provides fine-grained GPU resource management: 1) GPU virtualization: Supports virtualizing single GPU into multiple logical GPUs (e.g., vGPU), improving utilization; multiple small tasks can share same GPU; 2) Heterogeneous GPU scheduling: Uniformly manages different GPU models (A100/H100/V100/proprietary chips, etc.), automatically selecting appropriate model based on task characteristics; 3) GPU topology awareness: Identifies NVLink/PCIe topology between GPUs, multi-card tasks prioritize topology-optimal GPU combinations for improved communication efficiency; 4) Memory management: Monitors GPU memory usage, supports memory overcommit and dynamic reclamation; 5) Cost attribution: Records each task's GPU usage duration and model, supporting accurate cost accounting and department billing.
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