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In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.
Kaiqiang Zhang; Dongyang Ou; Congfeng Jiang; Yeliang Qiu; Longchuan Yan. Power and Performance Evaluation of Memory-Intensive Applications. Energies 2021, 14, 4089 .
AMA StyleKaiqiang Zhang, Dongyang Ou, Congfeng Jiang, Yeliang Qiu, Longchuan Yan. Power and Performance Evaluation of Memory-Intensive Applications. Energies. 2021; 14 (14):4089.
Chicago/Turabian StyleKaiqiang Zhang; Dongyang Ou; Congfeng Jiang; Yeliang Qiu; Longchuan Yan. 2021. "Power and Performance Evaluation of Memory-Intensive Applications." Energies 14, no. 14: 4089.
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%.
Yeliang Qiu; Congfeng Jiang; Yumei Wang; Dongyang Ou; Youhuizi Li; Jian Wan. Energy Aware Virtual Machine Scheduling in Data Centers. Energies 2019, 12, 646 .
AMA StyleYeliang Qiu, Congfeng Jiang, Yumei Wang, Dongyang Ou, Youhuizi Li, Jian Wan. Energy Aware Virtual Machine Scheduling in Data Centers. Energies. 2019; 12 (4):646.
Chicago/Turabian StyleYeliang Qiu; Congfeng Jiang; Yumei Wang; Dongyang Ou; Youhuizi Li; Jian Wan. 2019. "Energy Aware Virtual Machine Scheduling in Data Centers." Energies 12, no. 4: 646.
In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface.
Congfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors 2018, 18, 4395 .
AMA StyleCongfeng Jiang, Tiantian Fan, Yeliang Qiu, Hongyuan Wu, Jilin Zhang, Neal N. Xiong, Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors. 2018; 18 (12):4395.
Chicago/Turabian StyleCongfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. 2018. "Interdomain I/O Optimization in Virtualized Sensor Networks." Sensors 18, no. 12: 4395.