This paper focuses on the live migration of virtual machines (VMs) across semi-heterogeneous processors (SH), which share the same ISA but expose different features. Processor incompatibilities in such settings can block or break migrations, limiting resource utilization and operational flexibility. We provide the first detailed study of live migration feasibility across SH processors, yielding several findings valuable to cloud tenants, operators and hypervisor vendors. Building on these insights, we present MigCheck, a simulation tool that predicts the feasibility and outcome of VM migrations across SH processors without requiring costly real migrations. We demonstrate its accuracy and effectiveness through multiple use cases, including machine addition/removal, hypervisor updates, and VM platform adjustments.

This paper explores a practical means to employ Data Plane Development Kit (DPDK), a kernel-bypassing framework for packet processing, in resource-limited multi-tenant edge data centers. The problem is that the traditional virtual CPU (vCPU) schedulers are not well compatible with the event detection model of DPDK, which needs to monopolize a physical CPU (pCPU) for NIC register polling. Consequently, DPDK-applied systems running on consolidated Virtual Machines (VMs), a common setup at edges, fail to achieve low serving latencies regardless of the use of DPDK. Toward edge data center providers, this work presents a new vCPU scheduling policy named Polling vCPU Consolidation (PvCC) which runs DPDK-applied systems on dedicated pCPUs adopting microsecond-scale time slices. Along with this, we introduce a mechanism to determine an appropriate number of dedicated pCPUs according to customers' demands represented through a newly introduced vCPU scaling API enabling customers to scale up/down the vCPUs of their VMs at runtime. Our experiments show that PvCC allows DPDK-applied systems running on consolidated VMs to achieve low serving latencies, and our vCPU scaling API enables customers to adjust CPU resource assignment according to the incoming request rate and providers to effectively assign spare pCPUs to VMs executing non-latency-sensitive best-effort tasks.

In virtualized environments, virtual CPUs (vCPUs) are commonly oversubscribed on physical CPUs (pCPUs) to utilize CPU resources efficiently. However, excessive vCPU spinning, which occurs when a vCPU is waiting in a spin loop for an event from a descheduled vCPU, greatly degrades application performance in virtualized environments. VM-agnostic hypervisors aim to prevent excessive vCPU spinning by rescheduling vCPUs when an excessive spin is detected by hardware support for virtualization. We investigate the effectiveness of the KVM vCPU scheduler and show that it fails to avoid excessive vCPU spinning under various situations. We identify three problems: 1) scheduler mismatch, 2) aggressive limitation of candidate vCPUs, and 3) IPI context misuse. The first problem stems from the mismatch between the KVM vCPU scheduler and the Linux scheduler. The second and third problems come from failures in choosing candidate vCPUs to be scheduled next. Our in-depth analysis reveals simple modification to KVM (89 LoC) can mitigate excessive vCPU spinning. Our simple modification reduces excessive vCPU spinning by up to 96% and improves benchmark performance by up to 2.6×. Part of the proposed mitigation has been integrated with KVM from Linux KVM v5.13 onward.

In virtualized environments, oversubscribing virtual CPUs (vCPUs) on physical CPUs (pCPUs) is common to utilize CPU resources efficiently. Unfortunately, excessive vCPU spinning, which occurs when a vCPU is waiting in a spin loop for an event from a descheduled vCPU, causes serious performance degradation. Usually, the VM-agnostic hypervisor tries to prevent excessive vCPU spinning by rescheduling vCPUs when an excessive spin is detected by hardware support for virtualization.

This paper proposes a hybrid method for short-term load forecasting in power systems. Short-term load forecasting is one of the most important problems in power system operation and planning. Therefore, more accurate models are required to handle it appropriately. The proposed method is based on the fuzzy regression tree of a data mining method and the multi-layer perceptron (MLP) of artificial neural networks. The fuzzy regression tree works to discover important rules from actual data and classify input data into some classes. On the other hand, MLP is used to predict one-step ahead loads. This paper aims to clarify the nonlinear relationship between input and output variables. In this paper, to enhance the accuracy of the regression tree, simplified fuzzy inference is introduced to determine the split values. The proposed method is successfully applied to real data.