A novel multi-authority access control scheme for fine grained access to users data in the cloud-based storage

Two-level Network Bandwidth Allocation for Multi-tenant AI Clouds in Kubernetes

Compute and memory resources in cloud environments are strictly managed and isolated by the control plane; in contrast, network resources lack equivalent management and isolation mechanisms. This best-effort treatment of networking leads to significant challenges for modern AI workloads, which have diverse and bandwidth-intensive communication patterns. Without fine-grained network resource control, these workloads suffer from interference, unpredictable throughput, and suboptimal cluster utilization. To address these issues, this paper demonstrates how network bandwidth can be elevated to a first-class, schedulable, and enforceable resource within Kubernetes, the de facto standard for cloud-native orchestration. We introduce a new scheduling capability that models network interfaces as allocatable resources and regulates bandwidth sharing through the Dynamic Resource Allocation (DRA) framework, with enforcement implemented using the Hierarchical Token Bucket (HTB) mechanism. We evaluate the system using multitenant AI workloads derived from real-world communication characteristics with a simulation-based approach and validate the proposed enforcement strategy in a real cluster. Results show that the proposed two-level bandwidth allocation improves tenant performance predictability and satisfaction while maintaining packed cluster utilization.

Kernel-Level Dynamic Priority Scheduling for Containers

The use of containerized cloud platforms has now become inevitable for latency-sensitive applications; but resource contention between different workloads often creates the problem of noisy neighbors, which causes higher response latency and poor service quality. We present SCX_MUS (Scheduler Extensions for Multi-User Scheduling), a dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments. The proposed framework uses Linux sched_ext architecture along with the extension of eBPF to support dynamic cgroup-based scheduling through runtime awareness. A lightweight user space component continuously observes the workload of Kubernetes and adjusts the scheduling priorities of the containers without any need for recompiling the kernel or restarting containers. The experimental evaluation of the proposed framework was done with the help of Redis and stress-ng workloads in the same benchmarking condition compared to the native Linux Completely Fair Scheduler (CFS). The experimental results reveal a significant improvement in terms of latency reduction, enhanced throughput, efficient utilization of CPU resources, and stable performance of scheduling under resource contention.

A Survey on Caching Systems in Serverless Computing

Serverless computing, particularly Function-as-aService (FaaS), has emerged as a dominant cloud computing paradigm offering automatic scaling, pay-per-use pricing, and simplified deployment. However, the stateless and ephemeral nature of serverless functions introduces significant performance challenges, most notably cold-start latency and costly remote data access. Caching has been widely recognized as a key technique to mitigate these issues, yet the unique constraints of serverless environments-such as ephemeral containers, limited local state, and multi-tenant resource sharing-demand fundamentally new caching designs. In this paper, we present a concise survey of five representative caching systems for serverless platforms published between 2021 and 2025. We categorize them into three classes: container-level caching for cold-start mitigation, datalevel caching for reducing storage access latency, and cache coherence mechanisms for distributed consistency. We compare these systems along key dimensions including caching granularity, eviction strategy, transparency, and performance gains. Finally, we identify open challenges and promising directions for future research.

Performance Prediction of Data-Rebalancing Actions in Sharded NoSQL Clusters

A queueing network (QN) model of the rebalance process of the process used in MongoDB is developed, and a preliminary evaluation of performance prediction by the QN model via simulations is described, to understand the impact of different parameters of the rebalance process.

Towards Practical Latency SLOs on Cloud Data Warehouses

Enhancing the Kubernetes Scheduler: A State-of-the-Art Review from Cloud to Edge

A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.