Container Scheduling via System Call Risk Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current container schedulers in cloud environments do not adequately consider security implications when making placement decisions, leading to potential vulnerabilities due to shared system call interfaces and increased container density, which can result in compromised host security.
Innovation Solution
A method that utilizes host interface risk and data sensitivity scores to schedule containers, minimizing the total number of system calls across hosts and spreading container placements to reduce overall security risk, while accounting for user-defined security preferences and system call usage profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If container density is increased to improve host utilization, then productivity is improved, but security risk increases due to more containers sharing the same system call interface
Solution Approach 1:
The patent segments the cluster into multiple security zones based on system call usage profiles and risk assessments. Containers are distributed across different zones rather than densely packed on single hosts, reducing the attack surface while maintaining high utilization through efficient resource allocation across the segmented infrastructure.
Solution Approach 2:
The patent applies local quality by assigning different security policies and system call restrictions to different hosts or zones based on the specific containers deployed there. Each host can have customized system call interfaces tailored to its container workload, allowing high density in low-risk areas while maintaining strict security in high-risk areas.
2Object-generated harmful factors
If system call interface is restricted to improve security, then object-generated harmful factors are reduced, but container functionality may be compromised
Solution Approach 1:
The patent implements dynamic system call filtering that adapts to each container's actual usage patterns. The system monitors and learns which system calls are necessary for container functionality while blocking only those that pose security risks. This dynamic approach maintains container execution capability by allowing necessary calls while reducing the attack surface by blocking harmful ones.
Solution Approach 2:
The patent changes the parameters of system call access by introducing risk-based thresholds and profiles. Instead of uniform restriction, the system adjusts system call accessibility parameters based on container risk assessment, usage patterns, and security policies, allowing functional calls while blocking malicious ones.
3Object-generated harmful factors
If containers are placed on separate hosts to improve security, then object-generated harmful factors are reduced, but device complexity and resource utilization decrease
Solution Approach 1:
The patent creates a universal scheduling framework that handles multiple objectives simultaneously - security isolation, resource utilization optimization, and load balancing. The scheduler integrates security zone awareness with traditional resource management, allowing containers to be placed on the same host within secure zones while maintaining isolation guarantees, thus reducing scheduling complexity compared to strict physical separation.
4Object-generated harmful factors
If system-wide system call restrictions are applied to improve security, then object-generated harmful factors are reduced, but productivity decreases due to over-restriction
Solution Approach 1:
The patent applies local quality by implementing host-specific or zone-specific system call policies rather than uniform system-wide restrictions. Each host or security zone can have customized allowed system call sets based on its container workload characteristics, preventing over-restriction that would harm productivity while still reducing the attack surface locally where needed.
Data Source
AI summary
A method, apparatus and computer program product for scheduling placement of containers in association with a set of hosts. The technique utilizes metrics that characterize container-specific risks. A first metric is a host interface risk for a container that quantifies how similar or dissimilar the container is relative to other containers running on a host. Preferably, host interface risk is derived with respect to a system call interface comprising a set of system calls, and the metric is based at least in part on a measure of dissimilarity among system calls. A second metric is a data sensitivity score that quantifies a degree to which sensitive data accesses are associated to the container. Based at least in part on the host interface risk scores and the data sensitivity scores, one or more containers are automatically scheduled for placement on the set of hosts to minimize security risk for the set of hosts.