Deadline Constraints Research Articles

Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

FMMEHO Based Workflow Scheduling in Virtualized Cloud Environment for Smart Irrigation System

Extensive and exhaustive water utilization for agriculture, industries and ground water consumption for domestic purposes has heavily deterioted the water bodies. Cloud and sensor technology is widely deployed in a several real-time applications, especially in agriculture. The transformation of data obtained from large sensor networks into a valuable knowledge and assests for applications can effectively leverage the techniques like Cloud Computing (CC). In CC, scheduling the workflow is the major concern that focuses on comprehensive execution of workflows without compromising the Quality of Service (QoS). But workflow scheduling augmented with resource allocation is extremely challenging task because of its inherent computational intensity, task dependencies, and heterogeneous cloud resources. In this article, a novel Optimum Energy and Resource Aware Workflow Scheduling (OERES) scheme that is motivated by popular Fuzzy Membership Mutation Elephant Herding Optimization (FMMEHO) algorithm is proposed, that aims to schedule the task workflow to Virtual Machines (VMs) that are involved in computation. This also concentrates on dynamically deploying and un-deploying the VMs pertaining to the task requirements. The FMMEHO algorithm is a popular nature inspired technique, which is rooted on herding patterns of the giant mammals, the elephants. This algorithm employs a clan operator that updates the location and distance of elephants depending resource and energy usage of each clan in the context of matriarch elephant. The proposed OERES schema elevates the resource utilization and simultaneously mitigates the energy usage without compromising the dependency and deadline constraints. This work uses the famous Cloud Sim simulator to simulate the underlying cloud environment to investigate the effectiveness of proposed model. The efficacy of the scheduling methods is examined based on important parameters like mean Resource Utilization (RU), Energy utilization or Consumption/ Task (ECT), Total Energy Consumption (TEC), Makespan and Execution Time per Task (ETT). The results very well portray the effectiveness of proposed OERES algorithm against already existing methods.

Accelerating Federated Edge Learning via Topology Optimization

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.

Intelligent Latency-Aware Tasks Prioritization and Offloading Strategy in Distributed Fog-Cloud of Things

Offloading the dynamic tasks with fog computing is envisioned as a viable option for prolonging resource-limited constraints and improving the computational and communicational latency for delay-sensitive IoT applications. Besides, the priority of tasks and the target layers for offloading them to minimize the incurred service latency is a prime concern in layered computing architecture. To leverage the efficiency of the underlying computing nodes for the tasks’ heterogeneity and computational requirements with deadline constraints, this article presents a fuzzy logic technique to prioritize the tasks based on their resource requirements and associated deadline. For efficient scheduling, an elitism-based multipopulation Jaya is proposed to map these disparate groups of tasks to a cluster amalgamation of computational-rich heterogeneous computing nodes. Moreover, a compatibility-based heuristic offloading strategy is devised to determine compatible computing nodes to offload the computations considering the availability of resources and communicational time from the respective IoT devices. Finally, extensive simulations are carried out with conflicting scheduling parameters appraising the efficacy of the proposed strategy over existing algorithms. The percentages of improvements of the proposed algorithm over the compared algorithms are 35% and 28% for average waiting. time and average service latency, respectively.

Distributed job allocation using response threshold for heterogeneous robot team under deadline constraints

SummaryJob allocation among the robots is a significant challenge in a multi‐robot environment. This article proposes a distributed algorithm for job allocation with deadline constraints. It works utilizing the response threshold model inspired by job distribution among insect societies in nature. The team divides the jobs among themselves based on their threshold capacity. The proposed approach focus on maximizing the total jobs completed with the available resources. The static case where the robots never fail is considered first for evaluating the algorithm. Then it is run in a dynamic environment with robot failure to study the effects on job allocation. Two experimental studies are conducted with varying amounts of resources. In static and dynamic cases under experimental study I, the jobs completion percentage increased by an average of 37.6% and 35.8%, respectively, over the existing method. Under experimental study II, the proposed algorithm completed all the jobs with improved resource utilization. The results show that the proposed algorithm achieves better jobs completion. The algorithm is then tested in a real‐world experimental environment using FireBird V mobile robots under static and dynamic cases. The outcomes indicate that the proposed approach is efficient in job allocation in practical circumstances.

Gradient-Based Scheduler for Scientific Workflows in Cloud Computing

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton’s method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experiments are conducted on well-known scientific workflows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems.

PHH: Policy-Based Hyper-Heuristic With Reinforcement Learning

Hyper-heuristics have a high level of generality and adaptability, allowing them to effectively solve a wide range of complex optimization problems. With reinforcement learning, hyper-heuristics can use experience and knowledge gained to tackle unforeseen problems, allowing hyper-heuristics to adapt and improve over time. Our paper proposes a framework for using policy-based reinforcement learning to improve the performance of hyper-heuristics. The framework trains hyper-heuristic agents to select the best generalized constructive low-level heuristics to solve combinatorial optimization problems. The framework evaluation was performed using three benchmarking problems: traveling salesman, capacitated vehicle routing, and bin packing problems. The results showed that the proposed framework can outperform existing meta-heuristic and hyper-heuristic-based algorithms for all large problem instances in all problem domains. The proposed framework was also evaluated by applying it to a cost optimization problem for workflow scheduling on a hybrid cloud with a deadline constraint. Eight agents were trained on medium-sized workflows with two deadlines and tested against traditional meta-heuristic and hyper-heuristic methods to solve smaller and larger workflows with unforeseen deadlines. Four workflow applications, three workflow sizes, and three deadlines were used in the evaluation. The results showed that our proposed framework provided significantly better solutions of up to 98% for benchmarking problems, and up to 22% for cost optimization in workflow scheduling. Moreover, trained with small problem instances, the framework performed well for unforeseen larger problem instances implying its generalization. The proposed framework, thus, has the potential to improve both the generality and performance of solving large combinatorial optimization problems.

A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Cost effective hybrid genetic algorithm for scheduling scientific workflows in cloud under deadline constraint

Cloud has emerged as a convenient platform for executing complicated scientific applications from multiple disciplines by providing on-demand and scalable infrastructure on rental basis. Research and scientific community often opt for workflows to model these scientific applications Workflow scheduling has been extensively studied for decades with regard to grid and cluster computing, but few initiatives have been tailored for cloud. What's more, the previous work fails to incorporate the basic principles of IaaS clouds like pay-as-you-go model, elasticity, heterogeneity, dynamic provisioning and issues of VM's performance variation and acquisition delay besides other QoS requirements. This paper proposes a resource provisioning and scheduling strategy using genetic algorithm with the aim to optimise the overall execution cost while staying below the given deadline. The performance is further enhanced by using a high quality seed generated by predict earliest finish time (PEFT) algorithm which acts as a catalyst and helps the algorithm to converge faster. The proposed approach is simulated in WorkflowSim and evaluated using various well-known different sized realistic scientific workflows. The results validate the better performance of our approach over numerous state-of-art-algorithms.

Scheduling Bag-of-Tasks in Clouds Using Spot and Burstable Virtual Machines

Leading Cloud providers offer several types of Virtual Machines (VMs) in diverse contract models, with different guarantees in terms of availability and reliability. Among them, the most popular contract models are the on-demand and the spot models. In the former, on-demand VMs are allocated for a fixed cost per time unit, and their availability is ensured during the whole execution. On the other hand, in the spot market, VMs are offered with a huge discount when compared to the on-demand VMs, but their availability fluctuates according to the cloud's current demand that can terminate or hibernate a spot VM at any time. Furthermore, in order to cope with workload variations, cloud providers have also introduced the concept of burstable VMs which are able to burst up their respective baseline CPU performance during a limited period of time with an up to 20% discount when compared to an equivalent non-burstable on-demand VMs. In the current work, we present the Burst Hibernation-Aware Dynamic Scheduler (Burst-HADS), a framework that schedules and executes tasks of Bag-of-Tasks applications with deadline constraints by exploiting spot and on-demand burstable VMs, aiming at minimizing both the monetary cost and the execution time. Based on ILS metaheuristics, Burst-HADS defines an initial scheduling map of tasks to VMs which can then be dynamically altered by migrating tasks of a hibernated spot VM or by performing work-stealing when VMs become idle. Performance results on Amazon EC2 cloud with different applications show that, when compared to a solution that uses only regular on-demand instances, Burst-HADS reduces the monetary cost of the execution and meet the application deadline even in scenarios with high spot hibernation rates. It also reduces the total execution time when compared to a solution that uses only spot and non-burstable on-demand instances.

Optimizing Two-Truck Platooning With Deadlines

We study a transportation problem where two heavy-duty trucks travel across the national highway from separate origins to destinations, subject to individual deadline constraints. Our objective is to minimize their total fuel consumption by jointly optimizing path planning, speed planning, and platooning configuration. Such a two-truck platooning problem is pervasive in practice yet challenging to solve due to hard deadline constraints and enormous platooning configurations to consider. We first leverage a unique problem structure to significantly simplify platooning optimization and present a novel formulation. We prove that the two-truck platooning problem is weakly NP-hard and admits a Fully Polynomial Time Approximation Scheme (FPTAS). The FPTAS can achieve a fuel consumption within a ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+\epsilon)$ </tex-math></inline-formula> to the optimal (for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon &gt;0$ </tex-math></inline-formula> ) with a time complexity polynomial in the size of the transportation network and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/\epsilon $ </tex-math></inline-formula> . These results are in sharp contrast to the general multi-truck platooning problem, which is known to be APX-hard and repels any FPTAS. As the FPTAS still incurs excessive running time for large-scale cases, we design an efficient dual-subgradient algorithm for solving large-/national- scale instances. It is an iterative algorithm that always converges. We prove that each iteration only incurs polynomial-time complexity, albeit it requires solving an integer linear programming problem optimally. We characterize a condition under which the algorithm generates an optimal solution and derive a posterior performance bound when the condition is not met. Extensive simulations based on real-world traces show that our joint solution of path planning, speed planning, and platooning saves up to 24% fuel as compared to baseline alternatives.

Deep Reinforcement Learning‐Based UAV Path Planning for Energy‐Efficient Multitier Cooperative Computing in Wireless Sensor Networks

Benefiting from the progress of microelectromechanical system (MEMS) technology, wireless sensor networks (WSNs) can run a large number of complex applications. One of the most critical challenges for complex WSN applications is the huge computing demands and limited battery energy without any replenishment. The recent development of UAV‐assisted cooperative computing technology provides a promising solution to overcome these shortcomings. This paper addresses a three‐tier WSN model for UAV‐assisted cooperative computing, which includes several sensor nodes, a moving UAV equipped with computing resources, and a sink node (SN). Computation tasks arrive randomly at each sensor node, and the UAV moves around above the sensor nodes and provides computing services. The sensor nodes can process the computation tasks locally or cooperate with the UAV or SN for computing. In a life cycle of the UAV, we aim to maximize the energy efficiency of cooperative computing by optimizing the UAV path planning on the constraints of node energy consumption and task deadline. To adapt to the time‐varying indeterminate environment, a deep Q network‐ (DQN‐) based path planning algorithm is proposed. Simulation studies show that the performance of the proposed algorithm is better than the competitive algorithms, significantly improves the energy efficiency of cooperative computing, and achieves energy consumption balance.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

Energy Efficient Path and Trajectory Optimization of Manipulators With Task Deadline Constraints

Energy Efficient Path and Trajectory Optimization of Manipulators With Task Deadline Constraints

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI (Artificial Intelligence) workloads are proliferating in modern real-time systems. As the tasks of AI workloads fluctuate over time, resource planning policies used for traditional fixed real-time tasks should be re-examined. In particular, it is difficult to immediately handle changes in real-time tasks without violating the deadline constraints. To cope with this situation, this paper analyzes the task situations of AI workloads and finds the following two observations. First, resource planning for AI workloads is a complicated search problem that requires much time for optimization. Second, although the task set of an AI workload may change over time, the possible combinations of the task sets are known in advance. Based on these observations, this paper proposes a new resource planning scheme for AI workloads that supports the re-planning of resources. Instead of generating resource plans on the fly, the proposed scheme pre-determines resource plans for various combinations of tasks. Thus, in any case, the workload is immediately executed according to the resource plan maintained. Specifically, the proposed scheme maintains an optimized CPU (Central Processing Unit) and memory resource plan using genetic algorithms and applies it as soon as the workload changes. The proposed scheme is implemented in the open-source simulator SimRTS for the validation of its effectiveness. Simulation experiments show that the proposed scheme reduces the energy consumption of CPU and memory by 45.5% on average without deadline misses.

Single machine Pareto scheduling with positional deadlines and agreeable release and processing times

&lt;abstract&gt;&lt;p&gt;This paper studies the problem of scheduling $ n $ jobs on a single machine to minimize total completion time and maximum cost, simultaneously. Each job is associated with a positional deadline that indicates the largest ordinal number of this job in any feasible schedule. The jobs have agreeable release and processing times, meaning that jobs with larger release times also have larger processing times. The agreeability assumption is reasonable since both the single-criterion problems (without positional deadline constraints) of minimizing total completion time and maximum lateness on a single machine with arbitrary release and processing times are strongly NP-hard. An $ O(n^3) $-time Pareto optimal algorithm is presented. The previously known algorithms only solve two special cases of the agreeability assumption: either the case of equal release times in $ O(n^4) $ time, or the case of equal processing times (without positional deadline constraints) in $ O(n^3) $ time.&lt;/p&gt;&lt;/abstract&gt;

Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

SummaryThe elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.

Semi-Supervised Federated Learning Over Heterogeneous Wireless IoT Edge Networks: Framework and Algorithms

Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence for smart cities applications. However, most of the data collected by the Internet of Things devices in such applications is unlabeled, necessitating the use of semi-supervised learning. Existing studies have introduced solutions to run semi-supervised FL; however, they overlooked the inherent critical impacts of the wireless characteristics at the network edge. We fill this gap by proposing novel solutions to run semi-supervised FL over wireless network edge, considering the limited computation and communication resources and deadline constraints and realizing that unlabeled data can be automatically labeled during the training rounds to improve the performance of the global model. The problem is first formulated as an optimization problem followed by a two-phase solution. In the first phase, we propose a bisection-based algorithm to find the transmit power and local processing speed that optimally fit the new injected labeled data. In the second phase, we propose three algorithms to control the local updates and injected samples that meet the deadline constraint. We analyze the performance of each algorithm concerning the tradeoffs between learning performance, training time, and total energy consumption. Targeting two applications in smart cities, human activity recognition and object detection, we conduct extensive simulations using realistic federated data sets under nonindependent and identically distributed settings. Numerical results show that the proposed algorithms effectively utilize unlabeled samples while accounting for the characteristics of wireless edge networks in smart cities.

Optimal multi-user offloading with resources allocation in mobile edge cloud computing

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile device (SMD) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich central cloud. Compared to central cloud, edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. Therefore, how to obtain an optimal offloading policy under the constraints of mobility and limited resource remains a challenging issue. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computations from SMDs onto edge clouds in edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computation resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.

Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.