Kubernetes (k8s) is a system for managing containerized applications across multiple hosts. It offers automatic deployment, maintenance, scaling, and resource management for applications. Applications in k8s usually have different storage requirements in the form of service-level objectives (SLOs). However, the current k8s storage management has several limitations which cause explicit performance and cost overhead. K8s administrators have to configure storage in advance manually, and users must know configurations and capabilities of provided storage. Users' storage SLOs can be easily violated in k8s.In this paper, we design and implement k8s Enhanced Storage (k8sES) which efficiently supports applications with various storage SLOs along with all other requirements in the Kubernetes environment. We design and incorporate storage scheduling as part of the node scheduling process in k8s. Applications will be scheduled onto the correct nodes and storage without intervention from either users or administrators. Proper storage resources will be dynamically carved based on users' storage SLOs. In addition, we provide a tool to monitor the I/O activities of both applications and storage devices in k8sES. The evaluation shows that k8sES can better meet users' storage SLOs along with other requirements. Also, k8sES can achieve higher resource utilization efficiency with overhead similar to that of the current k8s.

Application virtual memory footprints are growing rapidly in all systems from servers down to smartphones. To address this growing demand, system integrators are incorporating ever larger amounts of main memory, warranting rethinking of memory management. In current systems, applications produce page fault exceptions whenever they access virtual memory regions that are not backed by a physical page. As application memory footprints grow, they induce more and more minor page faults. Handling of each minor page fault can take a few thousands of CPU cycles and blocks the application till the OS kernel finds a free physical frame. These page faults can be detrimental to the performance when their frequency of occurrence is high and spread across application runtime. Specifically, lazy allocation-induced minor page faults are increasingly impacting application performance. Our evaluation of several workloads indicates an overhead due to minor page faults as high as 29% of execution time. In this article, we propose to mitigate this problem through a hardware, software co-design approach. Specifically, we first propose to parallelize portions of the kernel page allocation to run ahead of fault time in a separate thread. Then we propose the Minor Fault Offload Engine (MFOE), a per-core hardware accelerator for minor fault handling. MFOE is equipped with a pre-allocated page frame table that it uses to service a page fault. On a page fault, MFOE quickly picks a pre-allocated page frame from this table, makes an entry for it in the TLB, and updates the page table entry to satisfy the page fault. The pre-allocation frame tables are periodically refreshed by a background kernel thread, which also updates the data structures in the kernel to account for the handled page faults. We evaluate this system in the gem5 architectural simulator with a modified Linux kernel running on top of simulated hardware containing the MFOE accelerator. Our results show that MFOE improves the average critical path fault handling latency by 33× and tail critical path latency by 51×. Among the evaluated applications, we observed an improvement of runtime by an average of 6.6%.

In this paper, we propose a novel caching mechanism, called FussyCache, that differs from the traditional DRAM caching mechanisms that automatically cache a data block when it is requested. Instead, FussyCache evaluates each requested data block for its caching eligibility, and reads ineligible blocks directly from the device each time. We show that the Fussy-Cache performs substantially better than the traditional caching algorithms and its performance increases with the storage device speed. In particular, for the first generation Intel Optane based storage, FussyCache provides 25-30% reduction in the average access latency as compared to the native caching mechanism such as plain LRU. We also observe close to 15-20% improvement in performance even for a mainstream TLC SSD. Furthermore, the FussyCache design includes two mechanisms that allow for its easy deployment in any environment: (a) a self-monitoring stage that reverts it to a normal LRU when partial caching is not beneficial, and (b) a training phase that automatically tunes the configurable parameters for the deployment environment.