Interdomain I/O Optimization in Virtualized Sensor Networks

Jiang, Congfeng; Fan, Tiantian; Qiu, Yeliang; Wu, Hongyuan; Zhang, Jilin; Xiong, Naixue; Wan, Jian

doi:10.3390/s18124395

Cited by 18 publications

(7 citation statements)

References 64 publications

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“…The major mechanisms involved in Xen's I/O procedure are the shared ring buffer and event channel [11,18,[21][22][23]. The shared ring buffer provides a shared memory communication mechanism that is used for communication among VMs and between DOM0 and guest VMs.…”

Section: Xen's I/o Proceduresmentioning

confidence: 99%

“…However, without considering the workload type of the VM, allocating a relatively long time slice to all VMs increases the average run-queue waiting time for all vCPUs in the run-queue, including I/O-intensive vCPUs, resulting in I/O latency for I/O-intensive VMs. Further, concurrently executing a CPU-intensive workload and an I/O-intensive workload in the VM affects I/O performance [18,22,31,32]. For VMs with mixed workloads that perform CPU-intensive and I/O-intensive tasks simultaneously, when the vCPU processes I/O responses from DOM0, it may not complete the processing of tasks for I/O in the current scheduling round.…”

Section: Motivationmentioning

confidence: 99%

“…Xen has adopted a split-driver model that comprises a driver domain (DOM0) and a user's VM (guest VM). DOM0 has a physical driver that handles the I/O of the guest VMs [9,13,[16][17][18]. The guest VM has a logical driver that sends an I/O request to the backend driver of DOM0 when an I/O appears.…”

Section: Introductionmentioning

confidence: 99%

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I/O Strength-Aware Credit Scheduler for Virtualized Environments

Lee

2020

Electronics

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With the evolution of cloud technology, the number of user applications is increasing, and computational workloads are becoming increasingly diverse and unpredictable. However, cloud data centers still exhibit a low I/O performance because of the scheduling policies employed, which are based on the degree of physical CPU (pCPU) occupancy. Notably, existing scheduling policies cannot guarantee good I/O performance because of the uncertainty of the extent of I/O occurrence and the lack of fine-grained workload classification. To overcome these limitations, we propose ISACS, an I/O strength-aware credit scheduler for virtualized environments. Based on the Credit2 scheduler, ISACS provides a fine-grained workload-aware scheduling technique to mitigate I/O performance degradation in virtualized environments. Further, ISACS uses the event channel mechanism in the virtualization architecture to expand the scope of the scheduling information area and measures the I/O strength of each virtual CPU (vCPU) in the run-queue. Then, ISACS allocates two types of virtual credits for all vCPUs in the run-queue to increase I/O performance and concurrently prevent CPU performance degradation. Finally, through I/O load balancing, ISACS prevents I/O-intensive vCPUs from becoming concentrated on specific cores. Our experiments show that compared with existing virtualization environments, ISACS provides a higher I/O performance with a negligible impact on CPU performance.

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Section: Xen's I/o Proceduresmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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I/O Strength-Aware Credit Scheduler for Virtualized Environments

Lee

2020

Electronics

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“…Therefore, it is possible to save more energy when the server is underutilized compared to the 100% utilization case [38][39][40][41]. Moreover, efficiency in hypervisor itself can also impact virtualized server's energy efficiency, such as inter-domain communication performance [42]. However, EP requires more exploration for energy efficiency and proportionality-aware VM scheduling.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Virtual Machine Scheduling in Data Centers

et al. 2019

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Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

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“…These smart sensors are capable of providing complex services with diverse requirements, including data aggregation and analytics [15]. In the emerging edge computing paradigm [16], data analytics can be performed in sensor nodes to save energy consumption or privacy preserve [17]- [20]. There are also some cloud based platforms providing data analytics for edge devices, such as AWS Greengrass [21] and Microsoft Azure IoT Edge [22].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

et al. 2019

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In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design. INDEX TERMS Co-allocated jobs, workload characterization, online services, batch jobs, data center, scheduling.

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Interdomain I/O Optimization in Virtualized Sensor Networks

Cited by 18 publications

References 64 publications

I/O Strength-Aware Credit Scheduler for Virtualized Environments

I/O Strength-Aware Credit Scheduler for Virtualized Environments

Energy Aware Virtual Machine Scheduling in Data Centers

Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

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