Connected Vehicles Computation Task Offloading Based on Opportunism inCooperative Edge Computing

The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud. Meanwhile, offloading decision and resource allocation of the user CVs must be ascertained. Furthermore, the joint offloading decision and resource allocation problem is described as a Mixed Integer Nonlinear Programming (MINLP) problem, which optimizes the task response latency of user CVs under various constraints. The original problem is decomposed into two subproblems. First, the Lagrange dual method is used to acquire the best resource allocation with the fixed offloading decision. Then, the satisfaction-driven method based on trial and error (TE) learning is adopted to optimize the offloading decision. Finally, a comprehensive series of experiments are conducted to demonstrate that our suggested scheme is more effective than other comparison schemes.     

A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts

Manufacturing of aircraft structural parts has the characteristics of multiple varieties, complex structures and small batches, which make the manufacturing resource allocation highly difficult. This paper proposes a manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation, considering multiple manufacturing resources including process planners, machine tools and cutting tools, as well as manufacturing process schemes of aircraft structural parts. Knowledge in terms of experts’ experience and historical data is used for fuzzy comprehensive evaluation. A manufacturing resource allocation model is proposed based on the analysis of manufacturing processes of aircraft structural parts. The capability of planners, the complexity of structural parts, the reliability of machine tools, the reliability of cutting tools and the correlations between manufacturing resources and structural parts are evaluated using the fuzzy comprehensive evaluation method. Multiple manufacturing resources are allocated based on the fuzzy comprehensive evaluation results. A prototype system has been implemented and a case study is used to validate the proposed approach.     

Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes for RPs. Here, we propose a novel resource bidding mechanism for the RP in which each SP bids resources based on the demand of a single task (task-based) rather than the whole service (service-based) and then the RP allocates resources to these tasks with following the resource constraints at edge servers and the sequential rule of task-offloading to guarantee the interest of SPs. We set the incomes of the RP as our optimization target and then formulate the resource allocation problem. Two typical greedy algorithms are adopted to solve this problem and analyze the performance differences using two different bidding methods. Comprehensive results show that our proposal optimizes resource utilization and improves the RP’s incomes when resources in the edge computing datacenter are limited.     

部分波长光网络中的动态波长分配算法

为适应网络中不同服务质量（QoS）的光路建立请求具有不同的优先级的情况,提出了一种用于部分波长可变网络中支持QoS的动态波长分配算法。该算法对网络中的业务请求分高、低两个优先级进行处理。对于高优先级的光路建立请求,通过充分利用网络中已配置的波长转换器实时改变可用波长集,以降低高优先级业务请求的阻塞率。对低优先级的光路建立请求,只考虑所选路径的当前位置是否有波长转换器来改变可用波长集,保证了低优先级的光路建立请求速度。仿真结果表明,该算法既能保证较高优先级的光路建立请求具有较低的阻塞率,又充分利用了有限的网络资源,实现了对波长转换器的最优利用。     

Microembolic signal characterization using adaptive chirplet expansion

The adaptive chirplet expansion (ACE) is proposed to characterize high-intensity, transient signals from circulating microemboli. The nonnegative adaptive spectrogram based on the ACE gives a compact representation of the microembolic signal (MES) in joint-time, frequency domain. The mean instantaneous power (MIP) and mean instantaneous frequency (MIF) of MES are estimated from the adaptive spectrogram. Then, several important characteristics of MES, such as embolus-to-blood ratio (EBR), half width maximum (HWM), and embolic signal onset (ESO), are computed from the MIP, and the frequency modulation is examined in the MIF. To validate the new method, we improved the simulation model of the audio Doppler ultrasound signal. Some MESs together with a Doppler ultrasound signal from carotid blood flow are simulated in the simulation study. As a comparison, the adaptive Gabor expansion (AGE) also is implemented on these simulated signals. The experimental results of the simulation study show that the new method, based on the ACE, outperforms the AGE-based method in MES characterization. The consistent conclusion has been confirmed by the clinical study on some clinical MESs.     

面向电力物联网URLLC业务的智能网络切片管理方法

基于5G通信技术的电力物联网正在如火如荼地建设,随之产生的是用电信息采集、输变电状态监测以及精准负荷控制等新型电力物联网业务。为了满足这些业务对5G网络的超低时延和超高可靠性的需求,提出一种面向电力物联网URLLC(ultra reliable low latency communication)业务的智能网络切片管理方法。该方法综合运用5G切片和移动边缘计算(mobile edge computing,MEC)技术,建立电力业务传输和计算的时延、能耗以及可靠性模型,并通过DQN(deep Q network)算法对切片资源进行优化。仿真实验表明,所提出的智能网络切片管理方法的可靠性高于98%,且优于经典的基于坐标块下降方法和资源平均分配方法。     

Fuzzy-logic optical optimization of mainframe CPU and memory

The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.     

LFF:一种面向大数据应用的众核处理器访存公平性调度机制

研究了众核处理器的访存公平性问题。针对众核处理器距离访存资源较近的处理单元拥有较大的访存带宽而造成的访存公平性问题,提出了一种面向大数据应用的众核处理器访存公平性调度机制:最少最远(LFF)优先访存。这种机制的原理如下:依据处理单元距离访存资源的距离以及处理单元访存的次数来调度访存顺序,以保证各个处理单元的公平性。首先,访问次数较少的节点被赋予更高的访存优先权。其次,在具有相同访问次数的节点中,距离更远的节点优先访存。再次,在相同距离的节点中,已被选中优先次数少的有优先级。实验评估表明,该调度机制能够有效解决众核处理器的访存公平性问题,其公平性调度效果优于FR-FCFS,PAR-BS、ATLAS。在1024核情况下,系统异步率由FR-FCFS的15.5%降低到1.89%。     

Resource Allocation in Edge-Computing Based Wireless Networks Based on Differential Game and Feedback Control

In this paper, we have proposed a differential game model to optimally solve the resource allocation problems in the edge-computing based wireless networks. In the proposed model, a wireless network with one cloud-computing center (CC) and lots of edge services providers (ESPs) is investigated. In order to provide users with higher services quality, the ESPs in the proposed wireless network should lease the computing resources from the CC and the CC can allocate its idle cloud computing resource to the ESPs. We will try to optimally allocate the edge computing resources between the ESPs and CC using the differential game and feedback control. Based on the proposed model, the ESPs can choose the amount of computing resources from the CC using feedback control, which is affected by the unit price of computing resources controlled by the CC. In the simulation part, the optimal allocated resources for users’ services are obtained based on the Nash equilibrium of the proposed differential game. The effectiveness and correctness of the proposed scheme is also verified through the numerical simulations and results.     

Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

In today’s world, smart phones offer various applications namely face detection, augmented-reality, image and video processing, video gaming and speech recognition. With the increasing demand for computing resources, these applications become more complicated. Cloud Computing (CC) environment provides access to unlimited resource pool with several features, including on demand self-service, elasticity, wide network access, resource pooling, low cost, and ease of use. Mobile Cloud Computing (MCC) aimed at overcoming drawbacks of smart phone devices. The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance. For remote execution, recent studies suggested downloading all or part of mobile application from mobile device. On the other hand, in offloading process, mobile device energy consumption, Central Processing Unit (CPU) utilization, execution time, remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed. To address the issues, a Heuristic and Bent Key Exchange (H-BKE) method can be considered by both ways to optimize energy consumption as well as to improve security during offloading. First, an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where, the secondary users are allocated with the unused primary users’ spectrum. Next, a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’ unused spectrum by the secondary user. Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption, energy consumption along with the security aspects covered during offloading in MCC.     

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.     

Concurrent optimal allocation of distributed manufacturing resources using extended Teaching-Learning-Based Optimization

The optimal allocation of distributed manufacturing resources is a challenging task for supply chain deployment in the current competitive and dynamic manufacturing environments, and is characterised by multiple objectives including time, cost, quality and risk that require simultaneous considerations. This paper presents an improved variant of the Teaching-Learning-Based Optimisation (TLBO) algorithm to concurrently evaluate, select and sequence the candidate distributed manufacturing resources allocated to subtasks comprising the supply chain, while dealing with the trade-offs among multiple objectives. Several algorithm-specific improvements are suggested to extend the standard form of TLBO algorithm, which is only well suited for the one-dimensional continuous numerical optimisation problem well, to solve the two-dimensional (i.e. both resource selection and resource sequencing) discrete combinatorial optimisation problem for concurrent allocation of distributed manufacturing resources through a focused trade-off within the constrained set of Pareto optimal solutions. The experimental simulation results showed that the proposed approach can obtain a better manufacturing resource allocation plan than the current standard meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation and Harmony Search. Moreover, a near optimal resource allocation plan can be obtained with linear algorithmic complexity as the problem scale increases greatly.     

Allocation of bandwidth and storage

We consider two allocation problems in this paper, namely, allocation of bandwidth and storage. In these problems, we face a number of independent requests, respectively, for reservation of bandwidth of a communication channel of fixed capacity and for storage of items into a space of fixed size. In both problems, a request is characterized by: (i) its required period of allocation; (ii) its required bandwidth (item width, respectively)and (iii)the profit of accepting the request. The problem is to decide which requests to accept so as to maximize the total profit. These problems in general are NP-hard. In this paper we provide polynomial-time algorithms for solving various special cases, and develop polynomial-time approximation algorithms for very general NP-hard cases with good performance guarantees.     

基于资源配（等效性的数据中心能耗优化

针对数据中心服务器的低能效问题,进行了利用资源配置的等效性来优化服务器能效比的研究。研究发现,应用程序的多种资源分配方案具有相同的性能,但表现出较大的能耗差异,这种现象叫做“基于性能等效的资源配置”,简称“等效配置”。基于这种观察,提出了两种优化能效比的算法———SmartRank 算法和 SmartBalance 算法。 Smar-tRank算法使用资源等效替换的方法寻找能耗最低的资源配置,来达到局部最优的能效比;SmartBalance算法通过评估资源需求向量与剩余资源间的关系来均衡资源分配,同时兼顾单个应用的能耗开销,从而达到全局最大能效比。实验表明,通过对这两个算法的优化,可实现平均节省3％的系统能耗,局部最大可以节省12.5％的能耗。     

Network capacity control using self-adjusting bid-prices

The computation of bid-prices for resources is the most popular instrument for capacity control in network revenue management. The basic task of this control includes supporting accept/reject decisions on dynamically arriving requests for products that differ in their revenues and resource demands, respectively. Within actual control, bid-prices can be used to approximate the opportunity cost of reserving resources to satisfy a request. Using this valuation, the request is accepted if the associated revenue equals or exceeds the opportunity cost. Most commonly, bid prices are computed by linear programming based on the forecasted demand with a few updates during the booking period. Due to accepted requests and variations between forecasted and real demand, the approximation of the opportunity cost becomes less accurate with time passing by, leading to inferior accept/reject decisions. Therefore, we propose the concept of self-adjusting bid-prices. The basic idea includes defining bid-prices as functions of the amount of capacity already used and of the expected demand-to-come. Coefficients for calibrating the bid-price functions are obtained by a simulation-based optimization using the metaheuristic scatter search.     

Reliability-oriented multi-resource allocation in a stochastic-flow network

A stochastic-flow network consists of a set of nodes, including source nodes which supply various resources and sink nodes at which resource demands take place, and a collection of arcs whose capacities have multiple operational states. The network reliability of such a stochastic-flow network is the probability that resources can be successfully transmitted from source nodes through multi-capacitated arcs to sink nodes. Although the evaluation schemes of network reliability in stochastic-flow networks have been extensively studied in the literature, how to allocate various resources at source nodes in a reliable means remains unanswered. In this study, a resource allocation problem in a stochastic-flow network is formulated that aims to determine the optimal resource allocation policy at source nodes subject to given resource demands at sink nodes such that the network reliability of the stochastic-flow network is maximized, and an algorithm for computing the optimal resource allocation is proposed that incorporates the principle of minimal path vectors. A numerical example is given to illustrate the proposed algorithm.     

Condensation algorithms for the regular mesh substructuring

This paper describes and analyses the implementation of two static condensation algorithms used to exploit the regular patterns created by two schemes of regular mesh substructuring. Computer resources required by the implementation are assessed in terms of the number of multiplications, the total CPU time, the core and the disk storage requirements. It is discovered that, when implemented for the regular mesh substructuring, the newly proposed condensation algorithm is generally more cost effective than the one proposed by Han and Abel, and modified for the regular mesh substructuring by the present authors.     

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.     

School resources, resource allocation, and risk of physical assault against Minnesota educators

To investigate the relation between schools’ resource levels (i.e., annual per student expenditures), school resource allocations, and physical assault (PA) against Minnesota's educators, a study was conducted from the two-phase Minnesota Educators’ Study (MES) that incorporated school-level fiscal and demographic data from the Minnesota Department of Education (MDE). The MES examined a randomly selected cohort of employed, state-licensed kindergarten through grade 12 educators. From mailed questionnaires, response rates for both Phase I (comprehensive data collection on violent events) and Phase II (case-control) were 84%. Cases experienced a work-related PA event in the previous 12 months; controls reported no assaults. Based on the school in which they worked the most time and available MDE school-level data, together with MES questionnaire data, analyses were conducted on 238 cases and 640 controls. Multivariate analyses, using directed acyclic graphs to guide selection of confounders, suggested that increased spending (i.e. resources) was associated with decreased risk of PA. Analyses further suggested that the highest quartiles of resource allocations, compared with the lowest quartiles (referents), were associated with decreased risks of PA for: district level administration; regular instruction; special education; student activities and athletics; and pupil support services expenditures. Associations between increased resource allocations to student activities expenditures and decreased risks of PA were the strongest. For example, an allocation greater than 5% of the total annual per student expenditure to student activities programming (referent, less than 0.04%) was associated with a decreased risk of PA (OR = 0.30, 95% CI: 0.12, 0.77). Results suggested that allocations of school resources (i.e., expenditures) to key program areas such as student athletics and extracurricular activities may reduce risk of work-related PA against educators. Research to further explore the nature of the relations between disparities in school resources and spending, resource allocations, and PA will be important to the continued development of relevant prevention strategies.     

HARQ Optimization for PDCP Duplication-Based 5G URLLC Dual Connectivity

Packet duplication (PD) with dual connectivity (DC) was newly introduced in the 5G New Radio (NR) specifications to meet the stringent ultra reliable low latency communication (URLLC) requirements. PD technology uses duplicated packets in the packet data convergence protocol (PDCP) layer that are transmitted via two different access nodes (ANs) to the user equipment (UE) in order to enhance the reliability performance. However, PD can result in unnecessary retransmissions in the lower layers since the hybrid automatic retransmission request (HARQ) operation is unaware of the transmission success achieved through the alternate DC link to the UE. To overcome this issue, in this paper, a novel duplication-aware retransmission optimization (DRO) scheme is proposed to reduce the resource usage induced by unnecessary HARQ retransmissions. The proposed DRO scheme can minimize the average channel use while satisfying the URLLC requirements. The proposed DRO scheme derives the optimal HARQ retransmission attempts for different ANs by solving a nonlinear integer programming (NLIP) problem. The performance of the proposed DRO scheme was evaluated using MATLAB simulation and is compared to the existing 5G HARQ support schemes. The simulations results show that the proposed DRO scheme can provide a 14.71% and 15.11% reduced average channel use gain compared to the selective data duplication upon failure (SDUF) scheme and latency-aware dynamic multi-connectivity algorithm (LADMA) scheme, respectively, which are the existing 5G PD schemes that use HARQ.