Online Scheduling Algorithm Research Articles

Online Task Scheduling With Workers Variabilities in Crowdsourcing

Crowdsourcing system, which utilizes many workers to process computer-complexity tasks, has become an effective platform in today’s online labor markets. In a crowdsourcing system, maximizing the total utility is one key design goal. This goal is extremely hard because a computer-complexity task can be a multi-dimensional large-scale task that contains thousands or millions of atomic tasks. In online situation, we need to consider both the varying service of workers and future unknown task arrivals. As we know, none of the previous work considers a multi-dimensional large-scale task assignment for utility maximization. In this paper, an online framework is proposed to solve this optimization problem by running atomic tasks in parallel on workers. To estimate worker service rates, we consider each varying worker as an arm for a multi-armed bandit in crowdsourcing system. We design the online scheduling algorithm from a bandit perspective by Online Convex Optimization (OCO) techniques. We prove that our designed algorithm can yield a sublinear regret bound. Finally, we show that our designed algorithm is better than the baseline algorithms by nearly 10% for the total utility achieved.

Fair and efficient DWBA algorithm based on SLA differentiated polling interleaved scheduling for NG-EPON

This study, based on the Next Generation Ethernet Passive Optical Network (NG-EPON) facing more complex and changeable network, develops a flexible and efficient resource allocation algorithm. First, a differentiated polling interleaved scheduling (DPIS) framework is designed based on dynamic awareness, significantly reducing idle time slots, improving channel upstream utilization, and performing service-level-agreement (SLA) support and Optical Network Unit (ONU) overall scheduling. Subsequently, a dynamic wavelength and bandwidth allocation (DWBA) scheme is presented, which allocates fair bandwidth between and in subcycles and ensures the timely transmission of large traffic ONUs. As revealed from the simulation results, the developed DPIS-DWBA algorithm exhibits high utilization and low latency performance of the online scheduling algorithm; it can also achieve the offline scheduling to support SLA and fairness and offers more effective delay differentiation and bandwidth fair service.

Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals

Age of information is a new network performance metric that captures the freshness of information at end-users. This paper studies the age of information from a scheduling perspective. To that end, we consider a wireless broadcast network where a base-station (BS) is updating many users on random information arrivals under a transmission capacity constraint. For the offline case when the arrival statistics are known to the BS, we develop a structural MDP scheduling algorithm and an index scheduling algorithm, leveraging Markov decision process (MDP) techniques and the Whittle's methodology for restless bandits. By exploring optimal structural results, we not only reduce the computational complexity of the MDP-based algorithm, but also simplify deriving a closed form of the Whittle index. Moreover, for the online case, we develop an MDP-based online scheduling algorithm and an index-based online scheduling algorithm. Both the structural MDP scheduling algorithm and the MDP-based online scheduling algorithm asymptotically minimize the average age, while the index scheduling algorithm minimizes the average age when the information arrival rates for all users are the same. Finally, the algorithms are validated via extensive numerical studies.

Less is More: Service Profit Maximization in Geo-Distributed Clouds

Nowadays cloud providers purchase a good deal of bandwidth from Internet service providers to satisfy the growing requests from corporate customers for the exclusive use of inter-datacenter bandwidth. For exclusive bandwidth services, neither maximizing the revenue nor minimizing the cost can bring the maximal profit to cloud providers. The diversity of bandwidth prices and the random arrival time of user requests further increase the difficulty in economically scheduling the services to meet user requests from cloud providers. In this article, we propose to help cloud providers maximize their service profits by properly selecting user requests to serve rather than satisfying them all. We formulate the problem of service profit maximization and prove its NP-hardness. To handle offline request submission, we propose a solution that maximizes the service profit by alternately maximizing the service revenue and minimizing the service cost. To maximize service profit under online request submission, we propose an online scheduling algorithm that carefully handles the risk of not being able to pay off the incremental service cost and makes scheduling decisions in real time. Our extensive evaluations demonstrate that our solutions can achieve more than 1.6x the service profits of existing solutions.

Pricing EV charging service with demand charge

Pricing electric vehicle (EV) charging services is difficult when the electricity tariff includes both time-of-use energy cost and demand charge based on peak power draw. In this paper, we propose a pricing scheme that assigns a session-specific energy price to each charging session at the end of the billing period. The session price precisely captures the costs of energy, demand charge, and infrastructure congestion for which that session is responsible in that month while optimizing the trade-off between inexpensive time-of-use pricing and peak power draw. While our pricing scheme is calculated offline at the end of the billing period, we propose an online scheduling algorithm based on model predictive control to determine charging rates for each EV in real-time. We provide theoretical justification for our proposal and support it with simulations using real data collected from charging facilities at Caltech and JPL. Our simulation results suggest that the online algorithm can approximate the offline optimal reasonably well, e.g., the cost paid by the operator in the online setting is higher than the offline optimal cost by 9.2% and 6.5% at Caltech and JPL respectively. In the case of JPL, congestion rents are enough to cover this increase in costs, while at Caltech, this results in a negligible average loss of $18 per month.

Delay-Aware Wireless Network Coding in Adversarial Traffic

We analyze a wireless line network employing wireless network coding. The two end nodes exchange their packets through relays. While a packet at a relay might not find its coding pair upon arrival, a transmission cost can be reduced by waiting for coding with a packet from the other side. To strike a balance between the reduced transmission cost and the cost incurred by the delay, a scheduling algorithm determining either to transmit an uncoded packet or to wait for coding is needed. Because of highly uncertain traffic injections, scheduling with no assumption of the traffic is critical. This paper proposes a randomized online scheduling algorithm for a relay in arbitrary traffic, which can be non-stationary or adversarial . The expected total cost (including a transmission cost and a delay cost) incurred by the proposed algorithm is at most $\frac {e}{e-1} \approx 1.58$ times the minimum achievable total cost. In particular, the proposed algorithm is universal in the sense that the ratio is independent of the traffic. With the universality, the proposed algorithm can be implemented at each relay distributedly (in a multi-relay network) with the same ratio. Moreover, the proposed algorithm turns out to generalize the classic ski-rental online algorithm.

Joint Optimal Power Flow Routing and Vehicle-to-Grid Scheduling: Theory and Algorithms

In a smart grid integrated with vehicle-to-grid (V2G) technique, electric vehicles (EVs) fleets under effective coordination can be considered as a massive aggregated power storage to provide frequency regulation service. In this article, we propose a hierarchical system model to jointly optimize power flow routing and V2G scheduling for providing regulation service. First of all, by installing power flow routers (PFRs) inside the power grid, we formulate the problem of optimal power flow (OPF) routing at the grid level. Through the utilization of the semidefinite programming (SDP) relaxation, we can transform the original non-deterministic polynomial-time hard (NP-hard) problem into a convex problem. The tree decomposition method is then used to further reduce the complexity of the system network. After solving the grid-level OPF routing problem, a forecast-based scheduling problem is formulated at the EV level to coordinate EVs by providing the V2G regulation service. To cope with the forecast uncertainties, an online scheduling problem is in turn formulated. In order to solve these problems in a scalable manner, decentralized algorithms are then devised to control the EV schedules. The simulation results show that the devised online scheduling algorithm can outperform the existing algorithms, which is able to flatten the power fluctuations at the buses with EVs attached. Additionally, we show that grid stability issue can be alleviated through the proposed model. Finally, for different power systems, the uses of PFRs can reduce the system power loss in a great manner while providing voltage regulation.

Online battery scheduling for grid-connected photo-voltaic systems

Abstract We study the problem of determining optimum policy for managing battery energy storage system (BESS) in grid-connected photo-voltaic (PV) systems, where the stochastic electricity demands from the load are met from three sources: grid, PV energy, and BESS. BESS is used either to store excess energy generated from PV systems for later use, or to purchase energy from the grid when the time-of-use (TOU) pricing is lower. The objective is to identify the optimum charging/discharging schedule of BESS so that the long-term cost of energy purchased from the grid is minimized. The stochastic variabilities in loads and PV energy are captured by employing probabilistic models of periodic stochastic process with parameters estimated using historical data. The optimization problem is formulated under the framework of periodic discounted Markov decision process (MDP), and the problem formulation includes the aging effects of batteries and solar panels. The online optimization problem is solved by adopting a policy iteration approach tailored for periodic MDP. The proposed online scheduling algorithm provides periodic policies for a period of 24-hour, where the system model is updated every day based on load and PV energy from the previous day in a rolling horizon fashion. Simulation results demonstrate that the proposed algorithm can achieve a 41.6% reduction in annual utility bills compared to conventional systems without PV and BESS, thus ascertaining the values of installing BESS and PV systems.

Schedule Execution for Two-Machine Job-Shop to Minimize Makespan with Uncertain Processing Times

This study addresses a two-machine job-shop scheduling problem with fixed lower and upper bounds on the job processing times. An exact value of the job duration remains unknown until completing the job. The objective is to minimize a schedule length (makespan). It is investigated how to best execute a schedule, if the job processing time may be equal to any real number from the given (closed) interval. Scheduling decisions consist of the off-line phase and the on-line phase of scheduling. Using the fixed lower and upper bounds on the job processing times available at the off-line phase, a scheduler may determine a minimal dominant set of schedules (minimal DS), which is based on the proven sufficient conditions for a schedule dominance. The DS optimally covers all possible realizations of the uncertain (interval) processing times, i.e., for each feasible scenario, there exists at least one optimal schedule in the minimal DS. The DS enables a scheduler to make the on-line scheduling decision, if a local information on completing some jobs becomes known. The stability approach enables a scheduler to choose optimal schedules for most feasible scenarios. The on-line scheduling algorithms have been developed with the asymptotic complexity O(n2) for n given jobs. The computational experiment shows the effectiveness of these algorithms.

Online scheduling of distributed Earth observation satellite system under rigid communication constraints

This paper focused on online scheduling of distributed Earth observation satellite system in a dynamic environment. The objective was to maximize the total profit of the overall system by efficiently coordinating the different satellites with stochastic arrival of urgent tasks, subject to rigid communication and observation time window constraints. We formulated this problem as a single-task, single-robot, time-extended assignment problem with intra-schedule dependency using the multi-robot task assignment taxonomy and formulated the sub-problem after releasing urgent tasks in a mixed-integer linear programming model. We first described the online scheduling algorithm for a single satellite, then we proposed the modified consensus-based bundle algorithm (m-CBBA) and modified asynchronous consensus-based bundle algorithm (m-ACBBA) with synchronous and asynchronous communication, respectively. Compared with initial versions of CBBA and ACBBA, the modified versions added the communication loop prediction phases to efficiently utilize scarce communication opportunities and reduce the communication requirements. Additionally, we introduce two contract net protocol (CNP)-based algorithms for comparison, respectively SingleItem-CNP-based (SI-CNP) algorithm and Batch-CNP-based (BA-CNP) algorithm. Computational experiments indicated that both the total profit and percentage of scheduled urgent tasks achieved by the m-ACBBA and m-CBBA algorithms were much higher than those achieved by both SI-CNP and BA-CNP. Additionally, the number of communications needed by either m-ACBBA or m-CBBA algorithm was lower than that by SI-CNP. When the communication cost in the system is high, the m-CBBA algorithm is preferred because it balances the profit and the required number of communications. When the communication cost is low, the m-ACBBA algorithm is preferred because it achieves high total profit and high percentage of scheduled urgent tasks.

Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing. Some latency-sensitive applications, such as cloud video, online game, and augmented reality, take advantage of the MEC system to provide fast and stable services. Several new network techniques, including the implementation of NFV (Network Function Virtualization), the placement of VNF (Virtual Network Function) and the scheduling of SFC (Service Function Chain), should be considered to be applied in the MEC system. In this paper, we focus on the research about the scheduling of SFC in the NFV enabled MEC system and propose a solution accordingly. First, we make reasonable assumptions on the settings of MEC systems and model the SFC scheduling problem into a flexible job-shop scheduling problem. Since minimizing the latency can significantly improve the quality of service (QoS) and increase the revenue of Internet Service Providers, our optimization goal is to minimize the overall scheduling latency. To solve this optimization problem, a deep reinforcement learning based algorithm DQS is proposed. DQS can detect the variation of the MEC system's environment and perform adaptive scheduling for SFC requests. As the results of the simulation indicate, DQS works better than the other off-the-shelf algorithms in two key indexes: overall scheduling latency and average resource usage. Moreover, DQS can shorten the decision time and schedule SFCs stably with high performance. It is suitable to be extended to an online scheduling algorithm.

Framework for studying online production scheduling under endogenous uncertainty

We propose a framework for studying online production scheduling in the presence of endogenous uncertainties. We address uncertainties in (i) processing times; (ii) batch yields; and (iii) unit operating status. First, we illustrate how uncertainty can result in infeasibilities in the incumbent schedule and propose a model for systematic schedule adjustment to restore feasibility in the absence of new scheduling inputs. In this model, we define variables to track and penalize changes between the new and old schedule. Second, we discuss the different probability distributions for the three uncertainties that we consider in this work and how the parameters for these distributions change with sampling frequency. Third, we present a formal procedure for carrying out closed-loop simulations and evaluating closed-loop performance in the presence of these uncertainties. Finally, using this framework we draw useful insights for the design of online scheduling algorithms in the presence of the above three uncertainties.

Real-Time Workflows Oriented Hybrid Scheduling Approach With Balancing Host Weighted Square Frequencies in Clouds

High energy consumption in cloud data centers has become one of the main obstacles to green cities, and an urgent problem to be solved. So far, a large number of scheduling algorithms have been developed to reduce energy consumption for executing workflows. However, most existing algorithms have obvious defects in energy and resource efficiency, because they schedule workflow tasks to hosts directly and ignore that the host is so powerful that a single workflow task cannot make full use of its resources. To resolve the issue, a new scheduling architecture is designed for cloud data centers. Then, two principles are derived: one suggests that hybrid scheduling tasks from different workflows is helpful for improving resource utilization; the other one deduces that balancing host weighted square frequencies can minimize total power consumption of active hosts under a given resource requirement. On the basis of the scheduling architecture and the two principles, an oNline schEduling AlgoriThm, namely NEAT, is proposed to schedule dynamic workflows with deadlines. Furthermore, three strategies for dynamically adjusting the available virtual machines (VMs) and active hosts are proposed and integrated into NEAT to improve the energy and resource efficiency for cloud data centers. Finally, the proposed NEAT is compared with three existing algorithms using real-world workflow traces to demonstrate its superior performance with respect to energy and resource efficiency while guaranteeing the timing requirements of workflows. Compared with baseline algorithms, NEAT is capable of reducing energy consumption for cloud data centers by an average of 40% in the range from 34.0% to 45.67%.

Online EV Charging Scheduling With On-Arrival Commitment

The rapid proliferation of electric vehicles has resulted in a drastic increase in the total energy demand of EVs. Given the limited charging rate capacity of charging stations and uncertainty of EV arrivals, the aggregate demand might go beyond the charging station capacity, even with proper scheduling. This paper formulates a social welfare maximization problem for EV charging scheduling with charging capacity constraint. Even though the underlying problem is linear, it is difficult to tackle since the input to the problem, i.e., the charging profile of EVs, reveals in online fashion. We devise charging scheduling algorithms that not only work in the online scenario, but also provide the following two key features: 1) on-arrival commitment; respecting the capacity constraint may hinder fulfilling charging requirement of the deadline-constrained EVs entirely. Therefore, committing a guaranteed charging amount upon arrival of each EV is highly essential; 2) (group)-strategy-proofness as a salient feature to promote EVs to reveal their true type and do not collude with other EVs. Extensive simulations using real traces demonstrate the effectiveness of our online scheduling algorithms as compared to the optimal non-committed offline solution.

Online VNF Lifecycle Management in an MEC-Enabled 5G IoT Architecture

The upcoming fifth generation (5G) mobile communications urge software-defined networks (SDNs) and network function virtualization (NFV) to join forces with the multiaccess edge computing (MEC) cause. Thus, reduced latency and increased capacity at the edge of the network can be achieved, to satisfy the requirements of the Internet of Things (IoT) ecosystem. If not properly orchestrated, the flexibility of the virtual network functions (VNFs) incorporation, in terms of deployment and lifecycle management, may cause serious issues in the NFV scheme. As the service level agreements (SLAs) of the 5G applications compete in an environment with traffic variations and VNF placement options with diverse computing or networking resources, an online placement approach is needed. In this article, we discuss the VNF lifecycle management challenges that arise from such heterogeneous architecture, in terms of VNF onboarding and scheduling. In particular, we enhance the intelligence of the NFV orchestrator (NFVO) by providing: 1) a latency-based embedding mechanism, where the VNFs are initially allocated to the appropriate tier and 2) an online scheduling algorithm, where the VNFs are instantiated, scaled, migrated, and destroyed based on the actual traffic. Finally, we design and implement an MEC-enabled 5G platform to evaluate our proposed mechanisms in real-life scenarios. The experimental results demonstrate that our proposed scheme maximizes the number of served users in the system by taking advantage of the online allocation of edge and core resources, without violating the application SLAs.

Online packet scheduling under adversarial errors

We consider the problem of scheduling packets of different sizes via a directed communication link prone to errors, where dynamic packet arrivals and errors are modeled by an adversary. Packets arrive over time to be transmitted over a channel in which instantaneous errors occur at times not known to the algorithm in advance. We focus on estimating the competitive throughput of online scheduling algorithms defined as the ratio between the total size of packets successfully transmitted by an online algorithm and the largest total size of packets which can be transmitted for the same arrival and error patterns.First, we design two online algorithms with optimal competitive throughput in various scenarios. One algorithm works for any f≥1 channels and attains the competitive throughput 1/2 provided that sizes of packets satisfy the divisibility property (i.e., any larger size is divisible by any smaller). The other algorithm achieves the optimal competitive throughput in (1/3,1/2] for arbitrary sizes of packets on one communication channel, where the exact value of the competitive throughput depends on the sizes of packets.Second, we focus on algorithms working with speedup s≥1. In this setting, online algorithms transmit packets s times faster than the offline optimum solution they are compared against. We design an algorithm which attains the competitive throughput 1 if it works with speedup 2 in the case that sizes of packets satisfy the divisibility property and with speedup s∈[4,6) for arbitrary sizes of packets. This demonstrates that throughput of the best online fault-tolerant scheduling algorithms scales well with resource augmentation.

On the design of online production scheduling algorithms

We discuss a systematic way to design online scheduling algorithms to achieve better closed-loop performance. Specifically, we address short-term scheduling, and focus on determining the horizon length and re-optimization time-step, and explore objective-function modifications and added constraints. First, we show that finding open-loop and closed-loop schedules are two different problems and that there is considerable potential to improve the closed-loop performance, but this task is non-trivial. Second, we outline and quantitatively define three key production system characteristics that are pertinent to the design of online scheduling algorithms: (i) production load; (ii) order-size max-mean relative difference; and (iii) time-constants. Third, we discuss how these affect the choice of the re-optimization time-step and the horizon length. Fourth, we evaluate the role of demand uncertainty and how it can be mitigated through better tuning of the above algorithmic parameters. Finally, we present a framework that summarizes the design procedure for online scheduling algorithms.

Model based online scheduling of concurrent and equal batch process units: Sugar End industrial case study

An online scheduling algorithm for equal and concurrent batch process units embedded in a large process is shown in this paper. Its target is to calculate the waiting time intervals between consecutive cycles of each batch process unit to achieve a global stationary scheduling. This approach is based on nonlinear model predictive control with heuristic constraints for the particular process. The algorithm has been tested against an industrial problem simulation that consists of a set of batch sugar crystallizers which are part of a reduced sugar production process. Due to the large computational load of the optimization procedure, a reduced prediction model of the system was developed to obtain real time solutions.

Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer

We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single charging station (CS). The station has available cheaper, but limited, energy from renewable energy sources (RES). The EVs are capable of and willing to participate in vehicle-to-vehicle (V2V) energy transfers that are used to reduce the charging cost and increase the RES utilization. The algorithms are centralized and aim to minimize the total charging cost for the EVs. We formulate the problem as a mixed integer programming (MIP) one and we solve it optimally assuming full knowledge of the EV demand and energy generation. Later, we propose an online algorithm that iteratively calls the offline one and copes with unknown future interruptions by arriving the EVs and with the inability to predict accurately RES production. In addition, a novel technique called virtual demand is developed that increases the demand of already existing EVs, in order to store renewable energy and later transfer it via V2V to EVs that will arrive at the CS in the future. This technique is used for mitigating the inefficiency due to the uncertainty about future actions that real-time scheduling entails. In a setting with up to 150 EVs and using real data regarding the RES production, our algorithms are shown to have low execution times, while the use of virtual demand increases RES utilization by 12% and reduces cost by 3.3%.

Primal–Dual and Dual-Fitting Analysis of Online Scheduling Algorithms for Generalized Flow-Time Problems

We study online scheduling problems on a single processor that can be viewed as extensions of the well-studied problem of minimizing total weighted flow time. In particular, we provide a framework of analysis that is derived by duality properties, does not rely on potential functions and is applicable to a variety of scheduling problems. A key ingredient in our approach is bypassing the need for "black-box" rounding of fractional solutions, which yields improved competitive ratios. We begin with an interpretation of Highest-Density-First (HDF) as a primal-dual algorithm, and a corresponding proof that HDF is optimal for total fractional weighted flow time (and thus scalable for the integral objective). Building upon the salient ideas of the proof, we show how to apply and extend this analysis to the more general problem of minimizing $\sum_j w_j g(F_j)$, where $w_j$ is the job weight, $F_j$ is the flow time and $g$ is a non-decreasing cost function. Among other results, we present improved competitive ratios for the setting in which $g$ is a concave function, and the setting of same-density jobs but general cost functions. We further apply our framework of analysis to online weighted completion time with general cost functions as well as scheduling under polyhedral constraints.