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%.

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.

The increasing demand for cloud-based services, such as big data analytics and online e-commerce, leads to rapid growth of large-scale internet data centers. In order to provide highly reliable, cost effective, and high quality cloud services, data centers are equipped with sensors to monitor the operational states of infrastructure hardware, such as servers, storage arrays, networking devices, and computer room air conditioning systems. However, such coarse grained monitoring cannot provide fine grained real time information for resource multiplexing and job scheduling. Moreover, the monitoring of node level power consumption plays an important role in the optimization of workload placement and energy efficiency in data centers. In this paper, we propose an edge computing platform for intelligent operational monitoring in data centers. The platform integrates wireless sensors and on-board built-in sensors to collect data during the operation and maintenance of data centers. Using logical functions, we divide the data center clusters into grids, and then deploy wireless sensors and edge servers in each grid. As such, data processing on edge servers can reduce the latency in data transmission to central clouds and thereby enhance the real time resource mapping decisions in data centers. In addition, the proposed platform also provides predictions of resource utilization, workload characteristics, and hardware health trends in data centers.