Modeling of Resource Granularity and Utilization with Virtual Machine Splitting

Baghban, Hojjat; Chou, Jerry; Hsu, Ching‐Hsien; Chung, Yeh‐Ching

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.134

Cited by 1 publication

(1 citation statement)

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“…Modeling resource utilization of virtual machines running on the cloud infrastructure has been always challenging. 33 Bohra and Chaudhary 34 suggested that though plenty of research is ongoing toward developing an integrated cloud management system to perform proper resource utilization while reducing power consumption, most of these techniques provide online power monitoring based on the power consumption of a physical node running one or more virtual machines (VMs). As resource utilization is crucial to facilitate live VM migrations, a raft of models was developed to meet the never-ending need for energy.…”

Section: Modeling Resource Utilization For Virtual Machinesmentioning

confidence: 99%

Performance modeling for I/O‐intensive applications on virtual machines

Bhattacharya

Peng

Mao

et al. 2022

Concurrency and Computation

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Models for virtual machines running on cloud computing systems. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions. We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and(2) predicting the performance trends of programs running on clouds. At the heart of the modeling system, we pay particular attention to CPU cores, disk size, main memory space, and input data volume, which serve as important factors for the developed prediction module. We devise two resource-utilization prediction algorithms driven by two distinctive sets of I/O and CPU intensive benchmarks, where one algorithm deals with execution time and the other one revolves around input data size. We investigate the correlation between CPU/disk utilization and VM live migrations. Our system aims at not only providing performance optimization for virtualized resources but also ensuring service level agreement (SLA) and Quality of Service (QoS). The model fits the curve quite well, thereby advocating for the efficiency of the algorithm. The case studies conducted in this project draw the comparisons between the performance of striped and monolithic disks as well as bringing forth the problem of cache coherence that causes hindrance to the experiment. We also deal with the cache-coherence problem to improve the accuracy of our prediction algorithms K E Y W O R D S cache coherence, I/O intensive, linear model, monolithic and split disk, resource utilization INTRODUCTIONThis article is motivated by the rapid growth in demand for computation resources in data centers and the widespread of cloud computing. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions.We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and (2) predicting the performance trends of programs running on clouds.This research is inspired by the following four motivations.Motivation 1: virtualization. Cloud computing has changed the landscape of computing resources delivery models. Large-scale virtualized data centers gain popularity thanks to the rapid growth in demand for computational power-driven by modern service applications combined with the shift to the cloud computing model. 1 The virtualization technique emulates physical computers with a mirrored operating system, virtual disks, virtual CPUs, and virtual memory resources. The emergence of virtual machines promises to streamline the on-demand provisioning of software,

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Section: Modeling Resource Utilization For Virtual Machinesmentioning

confidence: 99%