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In order to reduce the dependence on accumulated experience that is difficult to replicate for the human dispatchers to make power generation scheduling more automatically, quickly, and intelligently, higher requirements are placed on the level of the auxiliary decision-making system. In this paper, a short-term scheduling problem was treated as a regression task that concentrates on how to learn a reliable statistic model that concludes the intrinsic logic of the dispatching policy from extensive historical dispatching experience. In this way, since Kullback-Leibler distance can better measure the distance between two distributions, we designed a novel GAN-based learning method for the scheduling task. Also, we proposed a feasible framework that combines the stage of learning, decision-making, and deployment to support the practical implementation of the proposed algorithm. In the experiment, a real case that takes short-term scheduling tasks on a regional large-scale power system is considered in our experiments. As a result, the comparison of several methods further shows the superiority of the proposed GAN-based method under our implementations. [ABSTRACT FROM AUTHOR]

With the increasing attraction of unmanned aerial vehicles (UAVs) in civil, public, and military applications, multi-UAV systems can perform environmental and disaster monitoring, border surveillance, and search and rescue. It is foreseen that these multi-UAV-based applications will be an important trend for edge computing scenarios. However, due to UAVs’ limited energy supplies as well as their continuous increase in the number of sensors, energy efficiency is a critical issue in multi-UAV systems. We believe that the fusion of edge computing and cloud computing can provide effective support for energy savings. This article presents an energy-efficient edge cloud architecture called RESERVE for intelligent multi-UAV. Under RESERVE, we study the energy-efficient computation offloading decision-making problem in a decentralized manner. The problem is formulated as a three-layer game in which the discretionary approach to reaching Nash Equilibrium is presented. Based on the proposed game, we design decentralized algorithms for two different cases. The algorithms can both achieve Nash Equilibrium. Furthermore, we propose a decentralized computation offloading mechanism and analyze the performance of the game by its efficiency ratio. We conduct simulation experiments and design a framework prototype. Evaluation results demonstrate that the proposed game methods can achieve more than 30 percent extra energy consumption reduction compared with the state-of-the-art decentralized algorithm and less than 10 percent performance loss relative to the centralized solution. The prototype framework we have developed proves the concept we propose. [ABSTRACT FROM AUTHOR]

The fast access to data and high parallel processing in high-performance computing instigates an urgent demand on the improvement of the NVMe storage within modern data centers. However, the former NVMe virtualization’s unsatisfactory performance demonstrates that NVMe devices are often underutilized within cloud platforms. An NVMe virtualization mechanism with high performance and device sharing has captured researchers and developers’ attention. This article introduces MDev-NVMe, a new virtualization solution for NVMe storage device with (1) full NVMe storage virtualization for VMs running native NVMe driver, (2) a mediated pass-through mechanism for NVMe management, and (3) adaptive configuration of active polling optimization to simultaneously achieve high throughput, low latency performance, and substantial device scalability. We practically implement the MDev-NVMe as a Linux kernel module. This article subsequently evaluates MDev-NVMe with Intel OPTANE and P3600 SSD by comparing several mainstream NVMe virtualization mechanisms using application-level I/O benchmarks. MDev-NVMe with active polling can demonstrate a 142 percent improvement over native (interrupt-driven) throughput and over 2.5 × the Virtio throughput with only 70 percent native average latency and 31 percent Virtio average latency. Finally, the advantages of MDev-NVMe and the importance of adaptive polling are discussed, offering evidence that MDev-NVMe is a superior virtualization choice for cloud storage. [ABSTRACT FROM AUTHOR]

Storage virtualization is an important component of large-scale online services in multi-tenant clouds. It typically shares the physical storage among guest machines and performs transactional operations for high-performance data processing. However, even with the recent mediated pass-through virtualization optimization, the operations of multi-tenant storage I/O meet the bottleneck, and thus degrade the throughput performance of the cloud storage services. We observe that the root cause of the problem is the unawareness of varying and imbalanced workload inefficiency of resource management in the multi-tenant cloud storage setting. In this paper, we present FinNVMe, a new throughput-oriented NVMe storage virtualization management mechanism, that (1) passes-through I/O performance-critical resources and emulates privileged resources to provide high throughput in a workload-aware manner among multi-tenant VMs, (2) enables fine-grained scheduling for I/O resources to achieve promising flexibility and scalability with respective to virtualization, and (3) adopts the queue binding and the queue shuffling to reduce the virtualization and management overhead, and involves active polling for further I/O acceleration. This article subsequently evaluates FinNVMe with micro benchmarks on two typical scenarios (both balanced and imbalanced workload) and the real-world storage workloads to show its high throughput performance, along with the flexibility and scalability of virtualization and resource management. For example, FinNVMe achieves up to 20 percent throughput improvement with more stable latency in the varying and imbalanced workload. [ABSTRACT FROM AUTHOR]

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously. [ABSTRACT FROM AUTHOR]

This paper introduces gMig, an open-source and practical vGPU live migration solution for full virtualization. Taking the advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy mechanism combined with the hashing based Software Dirty Page technique to achieve efficient vGPU live migration. Particularly, we propose three core techniques for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping and adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach with sampling pre-filtering to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) Overlapped Migration Process, which significantly compresses the hanging overhead by overlapping the dirty page verification and transmission concurrently. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by up to 80.0 percent. The design of sampling filter and overlapped processing can bring about further 30.0 and 10.0 percent improvements in page processing. [ABSTRACT FROM AUTHOR]

This paper systematically expounded the theory of optimal frequency hopping patterns based on Welch Costas arrays with 1-gap row, a theory established after studying the properties and mathematical models of the frequency hopping patterns obtained by making two-dimensional cyclic shift to Welch Costas arrays. The algebraic construction and the autocorrelation and cross-correlation properties of the Welch Costas arrays with 1-gap row were studied and certain theorems were proved. The optimal frequency hopping patterns will have ideal autocorrelation and cross-correlation when designed by means of Welch Costas arrays with 1-gap row. [ABSTRACT FROM AUTHOR]

Considering the performance improvement the cloud technology provides by processing workloads in parallel, applications and services are now migrating to online clouds. In a cloud platform, workloads can be executed in a virtualized environment to have a great improvement of the resource utilization. However, there is a new challenge in the allocation problem, which is quantifying and optimizing the fairness and efficiency of heterogeneous resources (CPUs and GPUs) required by applications such as cloud gaming. The solving approach needs scalarization methods of the requirement vector, relevant functions for fairness metrics, and an acceptable algorithm to solve that, where the difficulties mainly locate. We design an iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences. Data are recorded and analyzed to discover the effect of the allocation in different situations, including the promotion of fairness and the effect on the frame rate of the workloads. The result indicates that there is a considerable fairness improvement after the resource allocation, especially in situations that many virtual machines are executing simultaneously. Compared with the VGASA strategy, the fairness metric value improved 45 percent in three virtual machines' situation. [ABSTRACT FROM AUTHOR]

In the ARM-based mobile virtualized environment, optimizing both the network throughput and the latency for network-intensive applications is of great importance. Our experimental studies have shown that the context switch between the host and guest triggered by a hypercall results in CPU overload and thus degrades the I/O performance under an intensive workload. To address this problem, we propose the Adaptive Hypercall Coalescing (HyperCo) algorithm, a software-only approach to optimizing the network I/O performance by reducing the number of hypercalls and achieving a trade-off between throughput and latency. We implement HyperCo by modifying the front-end driver of Virtio-net on the KVM/ARM platform, and we carry out extensive performance evaluations, which indicate that the number of hypercalls and the load on the CPU can be significantly reduced. We show that HyperCo can significantly improve the network throughput under an intensive workload while keeping a low penalty by adapting a coalescing interval dynamically at a low frequency of network I/O requests. [ABSTRACT FROM AUTHOR]

Communication in cellular networks is based on serving cells that provide the basic network service. In the real world, serving cells overlap which means the number of serving cells covering one position is usually more than one. Recently, the instability of mobility management in cellular networks has been studied to monitor and analyze the handoff process in mobile devices. However, the handoff process is actually produced by base stations instead of mobile devices. Hence, it is of great importance to measure the handoff process of mobility management from the base station side. In this article, we present a series of experiments performed using the data obtained by mobile network operators. The contributions of this study are three-fold. We reproduce a handoff process and handoff loop from both the mobile device level and the base station level, and confirm the existence of a handoff loop by measurements from the base station side. Through large-range measurements, we discover that only a small part of serving cells is involved in the handoff process, and in most cases, the number of candidate serving cells is much smaller than the number of cells that cover some position; namely, when a handoff loop occurs, the number of candidate serving cells is quite small, which is in contrast to our assumption. We confirm that the handoff loop often occurs in indoor conditions or when the mobile device has frequent communication with the base station. Finally, we present several comprehensive facts about the handoff process and handoff loop and provide suggestions that can be used to increase the quality of service of cellular networks. [ABSTRACT FROM AUTHOR]

Demand response (DR) currently plays a significant role in the operation of the electric grid. As a result, quantification of DR flexibility is an important aspect in the utilization of various DR resources. Generally, the evaluation of DR flexibility at bulk supply points (BSPs) is a challenging problem, especially without the monitoring of downstream customers’ load profiles in some areas. To solve this problem, we develop a two-layer DR flexibility estimation framework. In the top layer, a top-down optimization approach is proposed to disaggregate the BSP load into different building categories based on a suite of prototype building (PB) load profiles. In the bottom layer, simplified DR estimation models are deployed to quantify the theoretical DR flexibility of each PB type. Key advantages of this framework include: 1) quantifying DR flexibility at BSPs without relying on smart meter data or detailed customer surveys and 2) providing day-ahead, hour-ahead, and near real-time prediction of DR resources based on weather forecasts and other data. Case studies demonstrate the effectiveness of load disaggregation and DR flexibility quantification at a BSP. The prediction is compared with detailed physical models, and the mean relative errors for upper/lower DR capacity at the BSP are 1.5% and 3.1%, respectively. [ABSTRACT FROM AUTHOR]

Aggregate thermostatically controlled loads (TCLs) are good candidates for providing load following services in power systems. This paper is concerned with the modeling, evaluation, and control problems of a population of heterogeneous TCLs. Specifically, the heterogeneous population is divided into multiple homogeneous clusters and each cluster, i.e., TCL aggregator, is modeled by an approximated three-input single-output state space model. Here, the aggregators serve as a bridge connecting the load utility and the terminal TCLs, which have their own decision makers and are responsible for aggregate estimation and command issuing. And aggregate evaluation is carried out for the aggregator so as to provide the aggregate regulation capacities and ramping rates, which is useful for setting of the reference power trajectory. Based on the established control model, we furthermore propose a hierarchical centralized control algorithm for a bus load utility to regulate all TCLs inside it so as to provide load following service, while not affecting the customers’ comfort levels. Finally, simulation results with respect to a common bus load are provided to demonstrate the effectiveness of the proposed aggregate modeling and the centralized load following strategy. [ABSTRACT FROM AUTHOR]

Cyber–physical system (CPS) integrates variety of applications where some work in a time-triggered manner while others work in an event-triggered manner. Supporting unified designs of both time-triggered and event-triggered tasks facilitates the integration of CPS applications. This paper presents a mixed time/event-triggered architecture called MixCPS to make CPS applications work in the integrated and optimized manner. To optimize MixCPS, we propose a new timing performance metric for CPS applications, i.e., the application-level delay, which is induced from sensor to actuator and captures the coupling between cyber and physical worlds. Using the system-level time-triggered scheduling based on the synchronization between computation and communication nodes, we optimize the assignment of computation tasks and the packet transmissions in order to minimize the application-level delays. Furthermore, we discuss the scheduling policy for event-trigged tasks after the resource reservation for time-trigged tasks. Finally, the proposed MixCPS is evaluated by several simulations which show that the application-level delays can be significantly reduced through the time-triggered cooperation mechanism of computation and communication. [ABSTRACT FROM AUTHOR]

In response to the computational challenges produced by the integrated dispatch of generation and load (IDGL), this paper proposes a novel and efficient decomposition method. The IDGL is formulated using the mixed-integer quadratic constrained programming (MIQCP) method. To efficiently solve this complex optimization problem, the nodal equivalent load shifting bidding curve (NELSBC) is proposed to represent the aggregated response characteristics of customers at a node. The IDGL is subsequently decomposed into a two-level optimization problem. At the upper level, grid operators optimize load shifting schedules based on the NELSBC of each node. Transmission losses are explicitly incorporated into the model to coordinate them with generating costs and load shifting costs. At the bottom level, customer load adjustments are optimized at individual nodes given the nodal load shifting requirement imposed by the grid operators. The key advantage of the proposed method is that the load shifting among different nodes can be coordinated via NELSBCs without iterations. The proposed decomposition method significantly improves the efficiency of the IDGL. Parallel computing techniques are utilized to accelerate the computations. Using numerical studies of IEEE 30-bus, 118-bus, and practically sized 300-bus systems, this study demonstrates that accurate and efficient IDGL scheduling results, which consider the nonlinear impact of transmission losses, can be achieved. [ABSTRACT FROM PUBLISHER]

The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today’s IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach. [ABSTRACT FROM AUTHOR]