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This paper studies the problem of coordinated rebalancing and charging scheduling for mobility-on-demand systems with electric vehicles. A joint framework consisting of multi-server M/M/s queueing and fluid model is proposed to solve the problem, in which the former is used to deal with charging scheduling of vehicles, while the latter describes the dynamics of vehicles and users in the system. A fluid policy is presented to minimize the total number of in-transit empty vehicles under static equilibrium, yielding the optimal assignment by nonlinear programming. To cope with dynamically varying traffic conditions, we further develop a two-stage real-time policy for charging and rebalancing scheduling, where rebalancing assignment is periodically adjusted and a time-weighted averaging method is proposed to predict the future travel demand. Also, the amount of vehicles to be deployed in each charging station is given to minimize the customer waiting time for charging. The effectiveness of the proposed method is verified via simulations and experiments. [ABSTRACT FROM AUTHOR]

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80 percent while guaranteeing content freshness in heavily loaded ICVNs. [ABSTRACT FROM AUTHOR]

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15% and 8.35% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computting, and caching resources. How to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important. Most existing works on resource management in vehicular networks assume static network conditions. In this paper, we propose a general framework that can enable fast-adaptive resource allocation for dynamic vehicular environments. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs), and we combine hierarchical reinforcement learning with meta-learning, which makes our proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. Extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios, and significantly improve the performance of resource management in dynamic vehicular networks. [ABSTRACT FROM AUTHOR]

In this paper, we study learning-assisted multi-user scheduling for the wireless downlink. There have been many scheduling algorithms developed that optimize for a plethora of performance metrics; however a systematic approach across diverse performance metrics and deployment scenarios is still lacking. We address this by developing a meta-scheduler – given a diverse collection of schedulers, we develop a learning-based overlay algorithm (meta-scheduler) that selects that “best” scheduler from amongst these for each deployment scenario. More formally, we develop a multi-armed bandit (MAB) framework for meta-scheduling that assigns and adapts a score for each scheduler to maximize reward (e.g., mean delay, timely throughput etc.). The meta-scheduler is based on a variant of the Upper Confidence Bound algorithm (UCB), but adapted to interrupt the queuing dynamics at the base-station so as to filter out schedulers that might render the system unstable. We show that the algorithm has a poly-logarithmic regret in the expected reward with respect to a genie that chooses the optimal scheduler for each scenario. Finally through simulation, we show that the meta-scheduler learns the choice of the scheduler to best adapt to the deployment scenario (e.g. load conditions, performance metrics). [ABSTRACT FROM AUTHOR]

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations. [ABSTRACT FROM AUTHOR]

It is critical to reduce the operating cost of shared buses for bus companies and improve the user experience of passengers. However, existing studies focus on either bus scheduling or route planning, which cannot accomplish the above mentioned goals concurrently. In this paper, we construct a joint bus scheduling and route planning framework to maximize the number of passengers, minimize the total length of routes and the number of required buses, as well as guarantee good user experience of passengers. First, we establish a system model based on a real-world scenario and formulate a multi-objective combinational optimization problem. Then, based on the extracted traffic topology of urban traffic networks and the generated candidate line set, we propose an offline algorithm to cope with the similar passenger flow distributions, e.g., morning or evening peak of every day. In order to cope with dynamic real-time passenger flows, an online algorithm is designed. Experiments are carried out based on real-word scenarios. The results show that the proposed algorithms can greatly reduce the operating cost of bus companies and guarantee good user experience based on real-world scheduling data in comparison with several existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we conceive the design of dynamic wireless networks assisted by multiple intelligent reflecting surfaces (IRSs), where the connection states between users and IRSs are capable of being updated timely. Taking into account the time-varying states of the system, we further construct a long-term dynamic process. Our goal is to maximize the time average sum-rate of the dynamic system under the time average rate and power constraints of users, via jointly optimizing the power allocation at users and the reflecting coefficients at IRSs. With the aid of Lyapunov concept-based drift-plus-penalty (DPP) algorithm, the long-term optimization problem is formulated as an infinite-horizon time-average one. Subsequently, the fractional programming method based on Lagrangian dual transform is applied to optimize power allocation and reflecting coefficients in an iterative manner, and the closed-form solutions of power and reflecting coefficients can be obtained at each iteration. Finally, simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Further performance comparisons indicate that the proposed algorithm can maintain a balance between supply and demand for resource allocation and improve the fairness of users. [ABSTRACT FROM AUTHOR]

With significantly dynamic characteristics of the new aerial users, the emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm raises great challenges to current research of UAV applications. As far as the robust channel allocation is concerned, the high mobility of UAV nodes and the unexpected disturbance of external environment would render most existing methods which rely on definite information and are vulnerable to dynamic environment, become less attractive or even invalid. In this paper, we particularly investigate a cellular-enabled mesh UAV network exploiting partially overlapping channels (POCs), and propose a distributed fuzzy space based learning scheme for POCs allocation to combat the dynamic environment. Rather than the perfect channel state information (CSI) assumption, the dynamic and uncertain CSI of UAVs is characterized by fuzzy number. On this basis, the allocation process can be implemented in a mapped fuzzy space. Integrating fuzzy-logic and game based learning, we formulate the problem of POCs assignment as a fuzzy payoffs game (FPG), and demonstrate the existence of fuzzy Nash equilibrium for our designed FPG. Then, with the derived priority vector in the fuzzy space, the equilibrium solution can be achieved by the proposed algorithm. Numerical simulations demonstrate the advantages of our new scheme. [ABSTRACT FROM AUTHOR]

Cloud computing is an Internet-provisioned computing paradigm that provides scalable resources for the execution of the end user's tasks. The cloud users lease optimal resources that meet their demands with minimum cost and time. The cloud service providers need high utilization of cloud resources and minimized execution cost. To achieve high user satisfaction and improve utilization of cloud resources, the task scheduling techniques should be resource and deadline aware and distribute the workload in a balanced manner. A number of heuristic and meta-heuristic-based task scheduling approaches have been proposed; however, the majority of these approaches are not resource and deadline aware. Moreover, these schedulers either optimize a single objective or multiple objectives with non-conflicting parameters. However, there is a need for schedulers that can provide a balanced solution for conflicting parameters like time and cost. In this paper, a modified and adaptive PSO-based resource- and deadline-aware dynamic load-balanced (PSO-RDAL) algorithm is proposed. The PSO-RDAL scheduling technique aims to provide an optimized solution for the workload of independent and compute-intensive tasks with reasonable time and cost. Moreover, the proposed approach also supports multi-objective-based optimization with conflicting parameters like time and cost. The experimental results reveal that the PSO-RDAL has gained up to 66%, 162%, 56%, 89%, 98%, and 97% enhancement in terms of makespan, average resource utilization, task response time, meeting task deadline, penalty cost, and total execution cost, respectively, as compared to existing state-of-the-art tasks scheduling heuristics. [ABSTRACT FROM AUTHOR]

Hardware Trojans can compromise System-on-Chip (SoC) performance. Protection schemes implemented to combat these threats cannot guarantee 100% detection rate and may also introduce performance overhead. This paper defines the risk of running a job on an SoC as a function of the misdetection rate of the hardware Trojan detection methods implemented on the cores in the SoC. Given the user-defined deadlines of each job, our goal is to minimize the job-level risk as well as the deadline violation rate for both static and dynamic scheduling scenarios. We assume that there is no relationship between the execution time and risk of a task executed on a core. Our risk-aware scheduling algorithm first calculates the probability of possible task allocations and then uses it to derive the task-level deadlines. Each task is then allocated to the core with minimum risk that satisfies the task-level deadline. In addition, in dynamic scheduling, where multiple jobs are injected randomly, we propose to explicitly operate with a reduced virtual deadline to avoid possible future deadline violations. Simulations on randomly generated graphs show that our static scheduler has no deadline violations and achieves 5.1%–17.2% lower job-level risk than the popular Earliest Time First (ETF) algorithm when the deadline constraint is 1.2×–3.0× the makespan of ETF. In the dynamic case, the proposed algorithm achieves a violation rate comparable to that of Earliest Deadline First (EDF), an algorithm optimized for dynamic scenarios. Even when the injection rate is high, it outperforms EDF with 8.4%–10% lower risk when the deadline is 1.5×–3.0× the makespan of ETF. [ABSTRACT FROM AUTHOR]

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization. [ABSTRACT FROM AUTHOR]

With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks face a huge challenge which is providing diverse and complex network services with different requirements on a common infrastructure. To solve this challenge, Software Defined Network (SDN) and Network Function Virtualization (NFV) are adopted to build next-generation IoT networks which are softwarized and virtualized. This way, network functions are virtualized as Virtualized Network Functions (VNFs) and a network service consists of a set of VNFs. One of the main challenges for realizing this paradigm is the optimal resource allocation for VNFs. Most existing works assumed that services are represented as Service Function Chains (SFCs) which are chains. However, network services in IoT networks are more complex and diverse, therefore, more appropriate representations are Virtualized Network Function Forwarding Graphs (VNF-FGs) which are Directed Acyclic Graphs (DAGs). Previous works failed to exploit this special graph structure, which makes them sub-optimal or non-applicable for IoT networks. In this paper, we investigate the VNF-FG placing problem in dynamic IoT networks where DAG-represented services arrive and depart. To fully exploit the graph structures of services and handle the complexity of dynamic IoT networks, we combine a novel neural network structure Graph Neural Network (GNN) with Deep Reinforcement Learning (DRL) and propose an efficient algorithm for VNF-FG placing, which is called Kolin. Extensive simulation results suggest that Kolin outperforms the state-of-the-art solutions in terms of system cost, acceptance ratio, and computation complexity. [ABSTRACT FROM AUTHOR]

Graph analytics are an emerging class of irregular applications. Operating on very large datasets, they present unique behaviors, such as fine-grained, unpredictable memory accesses, and highly unbalanced task level parallelism, that make existing high-performance general-purpose processors or accelerators (e.g., GPUs) suboptimal. To address these issues, research and industry are developing a variety of custom accelerator designs for this application area, including solutions based on reconfigurable devices (Field Programmable Gate Arrays). These new approaches often employ High-Level Synthesis (HLS) to Speed up the development of the accelerators. In this paper, we propose a novel architecture template for the automatic generation of accelerators for graph analytics and irregular applications. The architecture template includes a dynamic task scheduling mechanism, a parallel array of accelerators that enables supporting task-level parallelism with context switching, and a related multi-channel memory interface that decouples communication from computation and provides support for fine-grained atomic memory operations. We discuss the integration of the architectural template in an HLS flow, presenting the necessary modifications to enable automatic generation of the custom architectures starting from OpenMP annotated code. We evaluate our approach first by synthesizing and exploring triangle counting, a common graph algorithm, and then by synthesizing custom designs for a set of graph database benchmark queries, representing series of graph pattern matching routines. We compare the synthesized accelerators with previous state-of-the-art methodologies for the synthesis of parallel architectures, showing that the proposed approach allows reducing resource usage by optimizing the number of accelerators replicas without any performance penalty. [ABSTRACT FROM AUTHOR]

The increasing deployment of electric vehicles (EVs) across the United States has introduced many new opportunities and challenges with regards to energy management and control. In smart and connected communities (SCCs), where advanced communication infrastructures are in place, optimal coordination of EVs can significantly impact EV owners, power systems, and charging station owners. This paper develops two scheduling frameworks (static and dynamic) for optimal coordination of a fleet of cooperative EVs in a community with many charging stations and potentially different types of chargers (e.g., level 1, level 2, and DC fast). The scheduling problems are formulated as mixed-integer multi-objective optimization models and then multi-objective solution methods are utilized to find the optimal solution for each of the two scheduling frameworks. Numerical experiments simulated based on the State of Florida verify the usefulness of smart charging for better energy management and satisfying key players’ objectives and constraints. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to schedule conditional DAG parallel tasks, with which we can derive safe response time upper bounds significantly better than the state-of-the-art counterparts. The main idea is to eliminate the notorious timing anomaly in scheduling parallel tasks by enforcing certain order constraints among the vertices, and thus the response time bound can be accurately predicted off-line by somehow "simulating" the runtime scheduling. A key challenge to apply the timing-anomaly free scheduling approach to conditional DAG parallel tasks is that at runtime it may generate exponentially many instances from a conditional DAG structure. To deal with this problem, we develop effective abstractions, based on which a safe response time upper bound is computed in polynomial time. We also develop algorithms to explore the vertex orders to shorten the response time bound. The effectiveness of the proposed approach is evaluated by experiments with randomly generated DAG tasks with different parameter configurations. [ABSTRACT FROM AUTHOR]

Employing dynamic time division duplexing can increase the system-wide spectral efficiency of applications with varying and unbalanced uplink and downlink data traffic requirements. However, in order to achieve this efficiency gain, it is necessary to manage the effects of cross-link interference, which are generated among cells transmitting in opposite link directions. This paper considers bidirectional sum-power minimization beamforming as a means to deal with this cross-link interference, by forcing a minimum signal-to-interference-plus-noise ratio constraint for both uplink and downlink. We propose two iterative approaches to solve this beamforming problem. The first approach assumes centralized processing and requires the availability of global channel state information. The second approach is performed in a decentralized manner, based on the alternating direction method of multipliers and requires only local channel state information and reduced signaling load. Both approaches are shown to converge to a minimum network power expenditure, whereas close-to-optimum performance can be obtained when limiting the number of iterations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint dynamic power control and user pairing algorithm for power-efficient and delay-constrained hybrid multiple access systems. In a hybrid multiple access system, user pairing determines whether the transmitter serves as a certain user by orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA). The proposed optimization framework minimizes the long-term time-average transmit power expenditure while reducing the queuing delay and guaranteeing the minimum time-average data rates. The proposed technique observes both channel and queue state information and adjusts queue backlogs to avoid an excessive queueing delay by appropriate user pairing and power allocation. Furthermore, the flexible use of resources is captured in the proposed algorithm by employing NOMA. The data-intensive simulation results show that the proposed scheme for power allocation and user scheduling achieves a balance among multiple performance goals, i.e., power efficiency, queueing delay, and data rate. [ABSTRACT FROM AUTHOR]

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes. [ABSTRACT FROM AUTHOR]

Licensed shared access (LSA) is a spectrum sharing mechanism where bandwidth is shared between a primary network, called incumbent, and a secondary mobile network. In this paper, we address dynamic spectrum management mechanisms for LSA systems. We propose a fair spectrum management algorithm for distributing incumbent's available spectrum among mobile networks. Then, we adapt the proposed algorithm to take mobile network operator's regulatory compliance aspect into account and penalize the misbehaving network operators in spectrum allocation. We extend our results to the scenario where more than one incumbent offer spectrum to the mobile operators in a service area and propose various protocols, which ensure long term fair spectrum allocation within the individual LSA networks. Finally, we numerically evaluate the performance of the proposed spectrum allocation algorithms and compare them using various performance metrics. For the single incumbent case, the numerical results show that the spectrum allocation is fair when the mobile operators follow the spectrum access regulations. We demonstrate the effect of our proposed penalty functions on spectrum allocation when the operators do not comply with the regulatory aspects. For the multi-incumbent scenario, the results show a tradeoff between efficient spectrum allocation and flexibility in spectrum access for our proposed algorithms. [ABSTRACT FROM AUTHOR]

The state-of-the-art MW-frequency control is performed by two hierarchical mechanisms: economic dispatch (ED) and automatic generation control (AGC). The ED is solved every 5 min identifying the most economic generation dispatch and reserve schedule. AGC is a feedback control system that regulates area control error by sending signals to regulation reserve every 2–6 s. In system with high renewable penetration and associated high net-load variability, the conventional ED-AGC hierarchical model may result in degraded MW-frequency performance and increasing operational cost. In this paper, an AGC dynamic constrained ED model is proposed to provide a more reliable and economical regulation reserve schedule in handling high net load variability. The continuous AGC dynamics is transformed into discrete state-space model, and the fast AGC dynamics are eliminated to reduce the system order. The discretized reduced-order AGC dynamics are incorporated into the ED to optimize the regulation schedule. The proposed model can be applied in a look-ahead mode with very short-term load forecast or in a look-back mode to provide a “perfect dispatch” benchmark based on historical net load data. The 5-bus and 118-bus systems are tested to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the management of medical packet transmissions in electronic health (e-health) networks is studied. Unlike most existing works in the literature, we focus on beyond wireless body area network (beyond-WBAN) communications, i.e., data transmissions from WBAN-gateways (e.g., smart phones) to the base station of medical centers, and consider a delay-dependent priority-aware transmission scheduling, which jointly takes into account both the criticality of medical packets and their starving time (i.e., experienced delays). In our model, medical packets are randomly aggregated at WBAN-gateways (each of which stands for one patient), and their beyond-WBAN transmission requests are reported to the base station with different priority information, which reflects their heterogeneities in medical importance. The base station then manages the beyond-WBAN transmissions following a constructed queueing system with a delay-dependent dynamic priority discipline. With the aim of maximizing the network welfare while preventing unexpected strategic behaviors from smart gateways, we design a truthful and efficient mechanism based on a virtual delay-dependent prioritized queueing game. Analytical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over the counterparts. [ABSTRACT FROM AUTHOR]

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches. [ABSTRACT FROM AUTHOR]

Network slicing enables diversified services to be accommodated by isolated slices in network function virtualization-enabled software-defined networks. To maintain satisfactory user experience and high profit for service providers in a dynamic environment, a slice may need to be reconfigured according to the varying traffic demand and resource availability. However, frequent reconfigurations incur certain cost and might cause service interruption. In this paper, we propose a hybrid slice reconfiguration (HSR) framework, where a fast slice reconfiguration (FSR) scheme reconfigures flows for individual slices at the time scale of flow arrival/departure, while a dimensioning slices with reconfiguration (DSR) scheme is occasionally performed to adjust allocated resources according to the time-varying traffic demand. In order to optimize the slice’s profit, i.e., the total utility minus the resource consumption and reconfiguration cost, we formulate the problems for FSR and DSR, which are difficult to solve due to the discontinuity and non-convexity of the reconfiguration cost function. Hence, we approximate the reconfiguration cost function with ${L} _{1}$ norm, which preserves the sparsity of the solution, thus facilitating restricting reconfigurations. Besides, we design an algorithm to schedule FSR and DSR, so that DSR is timely triggered according to the traffic dynamics and resource availability to improve the profit of slice. Furthermore, we extend HSR with a resource reservation mechanism, which reserves partial resources for near future traffic to reduce potential reconfigurations. Numerical results validate that our reconfiguration framework is effective in reducing reconfiguration overhead and achieving high profit for slices. [ABSTRACT FROM AUTHOR]

Ternary content-addressable memory (TCAM) designed using static random-access memory (SRAM)-based field-programmable gate arrays (FPGAs) offers a promising lookup performance. However, the update process in a TCAM table poses significant challenges for efficiently employing SRAM-based TCAM. SRAM-based TCAM for FPGAs is designed using block RAM or distributed RAM resources in FPGAs. Such designs suspend search operations during an already high-latency update operation, rendering them infeasible in applications that require high-frequency updates. This paper presents a dynamically updateable energy- and resource-efficient TCAM design (DURE) based on FPGAs. DURE exploits the distributed RAM resources in FPGAs. More specifically, the lookup table RAMs (LUTRAMs) available in SLICEM resources are configured as quad-port RAM, which constitutes the basic memory (BM) block in the implementation of DURE. The contents of the TCAM table are divided into chunks of equal size and mapped onto the LUTRAMs of the proposed BM blocks. DURE implements dynamic updates by reconfiguring the LUTRAMs of only those BM blocks that are associated with the word being updated, thereby allowing search and update operations to be performed simultaneously. This achieves a lookup rate of 335 million lookups per second, with an update rate of 5.15 million updates per second on a $512\times36$ size TCAM on a Virtex-6 FPGA. Compared with the existing SRAM-based TCAMs, DURE has a smaller single-cycle search latency and achieves at least 2.5 times more energy efficiency and a 67% higher performance per area. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) decouples the traditional network functions from specific or proprietary hardware, such that virtualized network functions (VNFs) can run in software form. By exploring NFV, a consecutive set of VNFs can constitute a service function chain (SFC) to provide the network service. From the perspective of network service providers, how to maximize the network utility is always one of the major concerns. To this end, there are two main issues need to be considered at runtime: 1) how to handle the unpredictable network traffic burst? and 2) how to fairly allocate resources among various flows to satisfy different traffic demands? In this paper, we investigate a fairness-aware flow scheduling problem for network utility maximization, with joint consideration of resource allocation and rate control. Based on a discrete-time queuing model, we propose a low-complexity online-distributed algorithm using the Lyapunov optimization framework, which can achieve arbitrary optimal utility with different fairness levels by tuning the fairness bias parameter. We theoretically analyze the optimality of the algorithm and evaluate its efficiency by both simulation and testbed-based experiments. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic resource allocation (DRA) problem for LTE-based vehicle-to-infrastructure networks, where the goal is to minimize the total power consumption (TPC) in the downlink, subject to both power constraints and rate requirements. Under time-varying channel conditions, the TPC minimization takes the form of a discrete-time sequence of NP-hard combinational optimization problems. To solve these sequential problems, we propose a novel two-stage algorithm, named as DRA and precoding algorithm (DRA-Pre). In the first stage, the resource allocation problem (i.e., pairing of vehicle users to roadside units, and subcarrier allocation) is solved by applying the multi-value discrete particle swarm optimization method. This approach takes advantage of the channel correlation by exploiting the relationship between resource allocation solutions in adjacent time slots, which can improve the TPC performance. In the second stage, the precoding design problem is solved by a low-complexity algorithm, where the original problem is split into two subproblems, i.e., a rate max–min subproblem and a TPC minimization subproblem. Simulation results show that the proposed algorithm converges rapidly and significantly outperforms benchmark approaches in terms of TPC. [ABSTRACT FROM AUTHOR]

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance. [ABSTRACT FROM AUTHOR]

The dramatic growth of machine-to-machine (M2M) communication in cellular networks brings the challenge of satisfying the quality of service (QoS) requirements of a large number of M2M devices with limited radio resources. In this paper, we propose an optimization framework for the semi-persistent scheduling of M2M transmissions based on the exploitation of their periodicity with the goal of reducing the overhead of the signaling required for connection initiation and scheduling. The goal of the optimization problem is to minimize the number of frequency bands used by the M2M devices to allow fair resource allocation of newly joining M2M and human-to-human communications. The constraints of the problem are delay and periodicity requirements of the M2M devices. We first prove that the optimization problem is NP-hard and then propose a polynomial-time heuristic algorithm employing a fixed priority assignment according to the QoS characteristics of the devices. We show that this heuristic algorithm provides an asymptotic approximation ratio of 2.33 to the optimal solution for the case where the delay tolerances of the devices are equal to their periods. Through extensive simulations, we demonstrate that the proposed algorithm performs better than the existing algorithms in terms of frequency band usage and schedulability. [ABSTRACT FROM AUTHOR]

Simultaneous random access attempts from massive machine-type communications (mMTC) devices may severely congest a shared physical random access channel (PRACH) in mobile networks. This paper presents a novel two-phase random access (TPRA) procedure to deal with the congestion caused by mMTC devices accessing the PRACH. During the first phase, the TPRA splits the mMTC devices into smaller groups according to a preamble selected randomly by the devices. Then, in the second phase, each group of devices is assigned with a dedicated channel to complete the random access procedure. The proposed concept allows a base station to adjust the number of dedicated channels in real-time according to the actual network load. We then present an analytical model to estimate the access success probability and the average access delay of the TPRA. Finally, we propose a simple formula to determine the optimal number of random access resources for the second phase of the proposed TPRA. Simulations are carried out to validate the analytical models and to demonstrate the benefits of the TPRA compared to competitive techniques. [ABSTRACT FROM AUTHOR]

Expanding the market of mobile network services and defining solutions that are cost efficient are the key challenges for next generation mobile networks. Network slicing is commonly considered to be the main instrument to exploit the flexibility of the new radio interface and core network functions. It targets splitting resources among services with different requirements and tailoring system parameters according to their needs. Regulation authorities also recognize network slicing as a way of opening the market to new players who can specialize in providing new mobile services acting as “tenants” of the slices. Resources can also be distributed between infrastructure providers and tenants so that they meet the requirements of the services offered. In this paper, we propose a model for dynamic trading of mobile network resources in a market that enables automatic optimization of technical parameters and of economic prices according to high level policies defined by the tenants. We introduce a mathematical formulation for the problems of resource allocation and price definition and show how the proposed approach can cope with quite diverse service scenarios presenting a large set of numerical results. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced mixed droop control strategy is proposed for a fuel cell/supercapacitor-based auxiliary power unit (FC/SC-APU), which is usually used in large electric vehicles, to achieve the dynamic load power allocation in a decentralized way. By applying an adjustable virtual impedance droop scheme to the SC converter and a virtual resistor droop scheme to the FC converter, the SC is able to buffer the fast-changing or pulsating load power flow, leaving the FC only providing the average load power. Since this method is fully decentralized, it features high flexibility and scalability. Furthermore, the proposed strategy is very suitable for extending the system's service life because the recovery and operational limit of the state-of-charge of SC have been considered in the power allocation. The operational principle of the proposed control strategy and the system design are elaborated, followed by which real-time simulations are conducted to verify theoretical analyses. [ABSTRACT FROM AUTHOR]

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs’ resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs’ resource demands. Most approaches in the latter category are heuristic and lack theoretical performance guarantees. In this paper, we formulate the dynamic VM management as a large-scale Markov decision process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, we show that MadVM can be implemented in a distributed system with at most two times of the optimal migration cost. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage, and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms. [ABSTRACT FROM AUTHOR]

The performance of wireless networks can be largely enhanced by coordinated multipoint (CoMP). To design an efficient CoMP in multiuser multiple-input multiple-output scenario, conventional transmission scheduling and power allocation are usually performed in a static and centralized manner. In this paper, we focus on dynamic and distributed transmission scheduling and power allocation. We first determine the coordinated base-station sets (defined as CBSs) candidates in each subband by the channel energy (i.e., square Frobenius norm of channel matrix) of each user. Each CBS candidate contains a set of coordination base-stations and edge users. By chordal distance, we can measure the orthogonality between space spanned of users in the same CBS candidate. Then, we propose two heuristic user scheduling algorithms based on channel energy and chordal distance between users to determine the set of users served by each CBS candidate. The first algorithm is based on an open problem, which reveals the philosophy of user scheduling with orthogonalization threshold guarantee. The second one deals with user scheduling by selecting a set of edge users with the largest total channel energy and orthogonalization threshold guarantee. With the total channel energy per CBS candidate, the CBSs and their served edge users can be determined. Then, water-filling power allocation is further applied to CBSs with block diagonalization precoding. Algorithm performance is demonstrated by extensive simulations. [ABSTRACT FROM AUTHOR]

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements. [ABSTRACT FROM AUTHOR]

IEEE 802.16 and Passive Optical Network (PON) are two promising broadband access technologies for high-capacity wireless access networks and wired access networks, respectively. The convergence of 802.16 and PON networks can take the mobility feature of wireless communications and the bandwidth advantage of optical networks jointly. Dynamic bandwidth allocation (DBA) plays an important role in each of these two networks for QoS assurance. In converged 802.16 and PON networks, the integration of the DBA schemes in both networks plays an even more critical role, since bandwidth request/grant mechanisms used in 802.16 and PON are different and the performance of the integrated DBA directly determines the overall system performance. In this paper, we investigate integrated dynamic bandwidth allocation schemes and their signaling overhead. First, this paper starts with discussing the converged network architecture and especially the issues on integrating optical network unit (ONU) and 802.16 base station (BS). Second, it proposes a slotted DBA (S-DBA) scheme and its performance analytic model. The S-DBA scheme takes into account the specific features of the converged network, aiming to reduce signaling overhead caused by cascaded bandwidth requests and grants. The simulation results show that the proposed S-DBA scheme can effectively reduce signaling overhead and increase channel utilization. [ABSTRACT FROM PUBLISHER]

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design. [ABSTRACT FROM AUTHOR]

This paper presents a Quality of Service (QoS) guaranteed dynamic grid resource scheduling algorithm. It mainly deals with multiple QoS-based grid resource scheduling models and solves the scheduling problems using optimization techniques. The paper proposes the idea of decomposing a global optimization problem in multiple QoS-based resource scheduling into two sub-problems, which simplifies the problem and makes it mathematically tractable. An iterative algorithm is also presented to get grid users and grid resource providers to interact with each other in an interactive process in resource market and achieve optimal QoS constraint dynamic grid resource scheduling. The paper provides performance studies comparing the proposed optimal approach with three possible easier approaches, which provide only partial optimization. The experimental results show that the proposed approach considering both computation and network traffic is able to better optimize the execution time than approaches, which consider only one or none of these criteria. [ABSTRACT FROM AUTHOR]

Since the highly diversified consumer demands and changing market environment pose a great challenge for the automobile industry, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method to solve the dynamic scheduling problem of material handling for mixed-model assembly lines based on line-integrated supermarkets. First, the dynamic material handling scheduling problem is described and a mathematical model is established to maximize the weight sum of the throughput of the assembly line and the number of logistics workers under the condition of variable product ratio and weights of scheduling criteria, considering the random failure of the equipment and the instability of the cycle time. Subsequently, an extreme learning machine and knowledge base-based dynamic scheduling method is constructed, consisting of a knowledge base-based scheduling rule selection and an extreme learning machine (ELM)-based product mix matching method. Afterward, considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) is proposed to optimize the parameters of ELM. Elite opposition-based learning and self-adaptive operators are employed to the EOADE-ELM to improve the performance. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel dynamic bandwidth allocation (DBA) scheme for future generation passive optical networks (PON) in order to meet the beyond 5G (B5G) and sixth generation (6G) stringent requirements. Our proposed algorithm is a generalized multi-access DBA (GMA-DBA) that exploits both orthogonal and non-orthogonal multi-access (MA) resources, resulting in a radical enhancement in the network performance. The proposed GMA-DBA algorithm is modeled as an optimization problem for which a closed form solution is derived. Results show that the GMA-DBA is capable of accommodating a network throughput higher than 1 Tbps while securing a latency and jitter below 100 µs and 10 µs, respectively. It can also maintain reliability, simplicity and energy efficiency. Therefore, using a non-orthogonal MA layer seems to be inevitable to achieve the stringent performance needed for the B5G and 6G next generation systems. [ABSTRACT FROM AUTHOR]

Private 5G edge networks support secure and private service, spectrum flexibility, and edge intelligence. In this paper, we aim to design a dynamic scheduling policy to explore the spectrum flexibility for heterogeneous federated learning (FL) in private 5G edge networks. Particularly, FL is implemented with multiple communication rounds, in each of which the scheduled device receives the global model from the edge server, updates its local model, and sends the updated local model to the edge server for global aggregation. The heterogeneity in FL comes from unbalanced data sizes across devices and diverse device capabilities. In this regard, we start with the convergence analysis of FL to determine the role of unbalanced data sizes in the learning performance. Then, based on the fact that diverse device capabilities make the completion times of local updates asynchronous, we adopt the sequential transmission for global aggregation. On this basis, we formulate a heterogeneity-aware dynamic scheduling problem to minimize the global loss function, with the consideration of straggler and limited device energy issues. By solving the formulated problem, we propose a dynamic scheduling algorithm (DISCO), to make an intelligent decision on the set and order of scheduled devices in each communication round. Theoretical analysis reveals that under certain conditions, the learning performance and energy constraints can be guaranteed in the DISCO. Finally, we demonstrate the superiority of the DISCO through numerical and experimental results, respectively. [ABSTRACT FROM AUTHOR]

Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept of deterministic project scheduling conflicts with real situations, in which in many cases, some data on the activity’ s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations. Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us. We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters, that is, the scheduling process must be flexible to dynamic environment features. In this paper, we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations, in which we apply the proposed heuristic rules (PHR) for scheduling multi-mode resource-constrained projects. Five projects are considered test problems for the PHR. The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems. The results in many cases of the proposed priority rules are very promising, where they achieve better scheduling dates in many test case problems and the same results for the others. The proposed model is based on the dynamic features for project topography. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been considered as a promising paradigm to support the growing popularity of mobile devices (MDs) with similar capabilities as cloud computing. Most existing research focuses on MEC enabled by terrestrial base stations (BSs), which is unable to work in certain scenarios, e.g., disaster rescue and field operation. Some researchers have been making efforts on studying MEC assisted by unmanned-aerial-vehicles (UAVs) and developed lots of efficient scheduling algorithms. However, MEC assisted only by UAVs has limited capability and is unsuitable for heavy-computation applications. To address the issue, this paper proposes a novel UAV-and-BS hybrid enabled MEC system, where multiple UAVs and one BS are deployed to facilitate the provisioning of MEC services either directly from UAVs or indirectly from the BS. Considering maximizing the lifetime of all MDs, the energy-efficient scheduling problem is formulated as minimizing the energy consumption of all MDs by jointly optimizing UAV trajectories, task associations, computing-and-transmitting resource allocations. The optimization problem is further decomposed into three sub-problems and solved by the proposed hybrid heuristic and learning based scheduling algorithms to reduce the complexity. Experimental results show that the proposed algorithm can achieve promising performance improvements over baseline algorithms, including local-computing, random-offloading and greedy-offloading. [ABSTRACT FROM AUTHOR]

The electric power system is a complicated dynamic system with a range of operating states and parametric uncertainties, especially due to change of the network topology, load increment and generation scheduling. Under this circumstance, traditional power system transient stability analysis methods may not always be appropriate. This paper presents the development of a computational methodology for evaluating the effect of parametric uncertainties on the small-signal stability analysis of power systems. A probabilistic approach is applied as a metric for the dynamic performance of the damping ratio of critical eigenvalues. The method is based on a Monte Carlo simulation for the analysis of automatic control of generation. The methodology is used for the performance evaluation of three classical controller tuning techniques: Frequency Response, Approximate Method and Ziegler-Nichols. The results show that the methodology is valid and potentially useful for quantifying the effect of parametric uncertainties in power systems dynamics simulations. [ABSTRACT FROM AUTHOR]