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[This problem is similar to Design a Resource Allocation Service Problem with additional requirement of high-concurrency, shared resource pool]
Assume 500K API requests over 5 minutes
that's around 1700 requests per second
With an expected rate of 1700 requests per second, the system should employ a load balancer for even request distribution, auto-scaling for elasticity, and rate limiting to protect against overload.
POST /resources/request
Allows users to request resources based on criteria.
POST /resources/release
Allows users to release resources they no longer need.
GET /resources/status/{resource_id}
Retrieves the status of a specific resource.
GET /resources/available
Returns a list of available resources that match specific criteria.
Resource Table: Tracks each resource’s type, size, status (available/allocated), and other relevant metadata.
User Requests Table: Logs user requests, including requested resource types, sizes, timestamps, and allocation statuses.
Avoiding Full Table Scans When Searching for Available Resources involves optimizing resource lookup speed with indexing and partitioning.
The database is partitioned by resource type and availability, ensuring that each query targets only relevant partitions, minimizing scanned rows. Multi-dimensional indexing on attributes like type, availability, and size further speeds up lookups, enabling efficient queries for available resources.
An inverted index can be maintained in memory, mapping each resource type directly to available resources, allowing fast, direct access without a table scan. The inverted index is updated dynamically whenever resources are allocated or released, keeping availability data current and eliminating the need for full scans.
The API Gateway manages incoming requests and routes them to the allocation service. It also provides rate limiting and authentication.
This is the core service responsible for processing resource allocation and deallocation requests.
This stores metadata about available resources, their statuses, and types (e.g., compute, storage).
Stores frequently accessed data (e.g., available resources by type) to reduce database load and improve response time.
Monitors resource usage and automatically scales the resource pool based on demand.
Manages high request loads, balancing spikes in traffic and queuing allocation requests when needed.
Request Submission:
Allocation Service:
Response to User:
Deallocation and Monitoring:
[Allocation is basically covered in Design a Resource Allocation Service problem. However we will talk about it here as well.]
Bin-Packing Algorithm for Optimal Allocation
The Best Fit Decreasing algorithm is a bin-packing method that minimizes wasted space by placing each request into the closest fitting resource available. Here’s the process:
For example, if a request needs a medium compute resource (e.g., 4 vCPUs, 16 GB RAM), BFD will try to allocate an exact match first; if unavailable, it allocates the next smallest suitable option (e.g., 4 vCPUs, 32 GB RAM), optimizing utilization and reducing fragmentation.
Cache-Indexed Searching
Cache-Indexed Searching for Fast Access speeds up allocation by storing metadata on available resources in a distributed cache (e.g., Redis), organized by resource type and size.
Approach
resource_type:size, each pointing to lists of available resources for efficient lookups.Fallback for Overloaded Pools
If a requested resource is unavailable, the system places the request in a priority queue based on urgency, buffering it until the resource becomes available. Urgent requests in the queue are prioritized for allocation once resources are freed.
For persistent demand, auto-scaling policies add additional resources, while proactive scaling anticipates demand spikes based on historical data, pre-provisioning resources as needed. Additionally, if no exact match is available, the system can offer users the closest alternative resource (e.g., a large compute instance if no medium one is available), with user-defined flexibility to accept or decline this option.
For example, if a medium compute resource request is made and none are available, the request enters the queue. If the queue remains unfulfilled for a set time, an alternative, like a large compute resource, is offered. If the queue length surpasses a threshold, the system triggers auto-scaling to add more medium resources, keeping up with demand efficiently.
Handling High Load
Handling the Load of 500K API Requests to Create Pool in 5 Minutes requires a combination of rate-limiting, queuing, batching, and scaling.
To prevent overload, the API Gateway enforces rate limits and prioritizes critical requests. Requests are funneled into a message queue, allowing asynchronous, batch processing by the Allocation Service, which optimizes resource allocation and smooths traffic peaks.
The Allocation Service is auto-scaled based on request volume, with load balancing across multiple instances. Resource pools are partitioned by region or type, enabling parallel processing and ensuring the system efficiently manages the high demand within the 5-minute window.
Logging and monitoring
Log all allocation and deallocation events, as well as any scaling actions, for monitoring and debugging.
Resource Leaks from Failed Deallocation
If resources are not properly released or marked available after use, they remain in an “allocated” state despite being unused. We should regularly audit and monitor resource states, implementing automated checks to detect and correct allocation inconsistencies.
Race Conditions in Resource Allocation
When multiple requests are processed concurrently, race conditions can occur if two or more threads try to allocate the same resource at the same time.
This can lead to double allocation (where a single resource is assigned to multiple requests), resulting in user dissatisfaction and potential system instability.
A good solution is to implement distributed locking mechanisms (e.g., Redis locks) to ensure that only one request can modify a resource’s state at a time. Atomic operations and transactions are crucial to maintaining consistency in high-concurrency environments.
High Latency from Contention on Shared Resources
Shared resources like databases, caches, or queues can experience high contention under concurrent loads, resulting in increased latency as multiple requests wait for access.
Use sharding or partitioning for shared resources to distribute the load. For instance, partition the resource pool database by type or region to reduce contention. Load balancing across multiple instances of the Allocation Service can also help distribute request load more evenly.
Distributed Locking (e.g., Redis locks, Zookeeper) provides strict consistency by preventing race conditions, making it ideal for high-concurrency systems where conflicts, like double allocation, must be avoided. However, it can introduce latency due to lock management, potentially causing bottlenecks under heavy contention, and is best for cases where consistency is critical.
Optimistic Locking assumes low conflict rates, checking for consistency only after operations complete. This non-blocking approach reduces overhead, performing well with low-to-moderate contention. However, in high-contention scenarios, retries can increase latency, making optimistic locking better suited for systems where performance is prioritized over strict consistency.
For high-concurrency resource allocation, distributed locking is generally recommended to ensure consistency, especially for non-duplicable resources, while careful design (like fine-grained locks and lock timeouts) can help mitigate bottlenecks.
Overbooking increases resource utilization by assuming that not all allocated resources will be fully used, making it effective when usage patterns are predictable. However, if the assumption fails, it risks resource contention, causing performance issues, latency, or even denial of service. Overbooking works best when historical data shows consistent underutilization, such as in shared environments where resources are rarely maxed out.
Strict Allocation provides guaranteed resources for each request, ensuring predictable performance and avoiding over-provisioning risks. However, this approach can lead to lower utilization, as resources remain idle when not explicitly allocated. Strict allocation is ideal for systems where reliability and performance consistency are critical, and some underutilization is acceptable.
Recommendation:
The database or cache layer may experience downtime or connectivity issues, making resource metadata inaccessible.
The allocation service can’t access resource status information, leading to failed or delayed requests.
We can implement a multi-layered architecture with failover databases and distributed caching. For critical operations, maintain a secondary (or backup) data source.
Network issues can cause communication breakdowns between different components, such as the allocation service, database, or cache.
Resource availability might be misreported, and allocation decisions may become unreliable or inconsistent.
We can use distributed databases with network resilience and consider quorum-based consensus for critical decisions to avoid inconsistencies.
One improvement we could make is use machine learning to predict demand spikes based on historical data, adjusting resource pools proactively.