Abstract This paper presents enCloud, a new aspect‐oriented trusted service migration with SGX‐enabled cloud VM. Addressing the challenge of reconciling end‐to‐end security with VM migration, enCloud incorporates two key aspects: (1) end‐to‐end security for enclave context migration, and (2) VM abstraction for conventional VM context migration. This paper provides a practical guideline with applicable APIs for trusted service migration. In a case study, enCloud demonstrates effective trusted DB service migration on a cloud VM, achieving end‐to‐end security with minimal trust boundaries. The framework supports pre‐copy live VM migration to minimize service downtime. This paper contributes a concise and practical solution in the form of the enCloud framework for secure service migration.

This article investigates how to satisfy network performance requirements that are crucial in achieving the service level objectives (SLOs) in clouds. Traditional techniques for network performance management have a limited ability to satisfy the network SLOs. Our in-depth analysis reveals that the fundamental reason comes from decoupling of the CPU scheduler and the network traffic controller as the current CPU scheduler is not aware of such network requirements but only provides a fair-share amount of CPU to all containers. Thus, the container cannot perform the amount of network processing as needed to satisfy its SLO when the CPU allocation is insufficient. In this article, we propose Autothrottle that dynamically adjusts the CPU allocation for the containers to satisfy their network SLOs. The key element of Autothrottle is a throttle algorithm that autonomously determines the amount of CPU for each container needed to satisfy the requirement. We implement Autothrottle in the Linux kernel and evaluate it with massive real-world workloads such as Apache Kafka. Our evaluation results show that Autothrottle successfully satisfies the given network SLO only with a 2% gap while the existing scheme achieves 20% less than the SLO. We further observe that Autothrottle also reduces the CPU overhead in network processing by 19%, improving the network throughput by 27% compared to the existing scheme.

It is widely believed that software routers based on commodity operating systems cannot deliver high-speed packet processing, and a number of alternative approaches (including user-space network stacks) have been proposed. This paper revisits the inefficiency of kernel-level packet processing inside modern OS-based software routers and explores whether a redesign of kernel network stacks can improve the incompetence. We present a case contrary to the belief through a redesign: Kafe-a kernel-based advanced forwarding engine that can process packets as fast as user-space network stacks. The Kafe neither adds any new API nor depends on proprietary hardware features, but the Kafe outperforms Linux by seven times and RouteBricks by three times. The current implementation of the Kafe can forward 64-byte IPv4 packets at 28.2 Gbps using eight cores running at 2.6 GHz. Our evaluation results show that the Kafe achieves similar packet forwarding performance to Intel DPDK while consuming much less CPU and memory resources.

Service level agreements (SLAs) for storage performance in virtualized systems are difficult to guarantee, because different consolidated virtual machines have their own performance requirements. Moreover, hard disk drives (HDDs) in virtualized systems are being replaced by solid-state drives (SSDs). SSDs have higher throughput and lower latency than HDDs; however, they pose new challenges in terms of SLAs. In this paper, we determine that existing I/O schedulers working with SSDs fail to guarantee SLAs among virtualmachines, and do not effectively utilize the high performance of SSDs. To address this issue, we propose the opportunistic I/O scheduler (OIOS), a novel I/O scheduler for SSDs. OIOS guarantees SLAs and fully utilizes the high performance of SSDs. To support realistic SLAs, OIOS provides diverse SLA support functions, including reservations, limitations, and proportional sharing. In addition, OIOS accepts SLAs that are specified in four measurement types: bandwidth, I/Os per second (IOPS), latency, and utilization. Experimental results show that OIOS increases the aggregated bandwidth of VMs by 80 percent compared to mClock, while achieving a similar level of fairness. In addition, we evaluate the proposed scheduler with realistic benchmarks, such as Filebench and the Yahoo CloudServing Benchmark. OIOS successfully guarantees the requirements of diverse SLAs with different metrics.

Enterprise servers require customized solid-state drives (SSDs) to satisfy their specialized I/O performance and reliability requirements. For effective use of SSDs for enterprise purposes, SSDs must be designed considering requirements such as those related to performance, lifetime, and cost constraints. However, SSDs have numerous hardware and software design options, such as flash memory types and block allocation methods, which have not been well analyzed yet, but on which the SSD performance depends. Furthermore, there is no methodology for determining the optimal design for a particular I/O workload. This paper proposes SSD-Tailor, a customization tool for SSDs. SSD-Tailor determines a near-optimal set of design options for a given workload. SSD designers can use SSD-Tailor to customize SSDs in the early design stage to meet the customer requirements. We evaluate SSD-Tailor with nine I/O workload traces collected from real-world enterprise servers. We observe that SSD-Tailor finds near-optimal SSD designs for these workloads by exploring only about 1% of the entire set of design candidates. We also show that the near-optimal designs increase the average I/O operations per second by up to 17% and decrease the average response time by up to 163% as compared to an SSD with a general design.