Delay-tolerant Tasks Research Articles

Spatio-temporal load migration potential of data centers: Evaluation and application

The wide distribution of data centers and the delay tolerance of computing tasks endow data center loads with adjustable characteristics in both temporal and spatial dimensions. Due to the characteristics of abundant spatiotemporal flexibility, data centers can participate in the optimization of power system operation and regulation. To quantify this flexibility, this paper proposes a spatiotemporal load migration potential evaluation model. Meanwhile, a data center energy management strategy is combined with the characteristics of spatiotemporal load migration, which deeply exploits the migration potential through the spatiotemporal redistribution of delay-tolerant tasks, as well as server ON/OFF scheduling and CPU operating frequency scaling across different spatial locations. A case study demonstrates that adopting the proposed approach considering an energy management strategy can effectively improve the load migration potential of data centers. The migration characteristics of data centers have great application prospects in reducing carbon emissions and enhancing operational flexibility.

Tasks-oriented joint resource allocation scheme for the Internet of vehicles with sensing, communication and computing integration

With the development of artificial intelligence (AI) and 5G technology, the integration of sensing, communication and computing in the Internet of Vehicles (IoV) is becoming a trend. However, the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems. In view of the above challenges, this paper proposes a tasks-oriented joint resource allocation scheme (TOJRAS) in the scenario of IoV. First, this paper proposes a system model with sensing, communication, and computing integration for multiple intelligent tasks with different requirements in the IoV. Secondly, joint resource allocation problems for real-time tasks and delay-tolerant tasks in the IoV are constructed respectively, including communication, computing and caching resources. Thirdly, a distributed deep Q-network (DDQN) based algorithm is proposed to solve the optimization problems, and the convergence and complexity of the algorithm are discussed. Finally, the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme, compared to the existing ones. The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%, and our proposed resource allocation scheme improves the mAP performance by about 0.15 under resource constraints.

Fairness and Utilitarianism in Allocating Energy to EVs During Power Contingencies Using Modified Division Rules

In order to maximize utilitarianism and fairness in allocating energy to electric vehicles (EVs) during outages, this study proposes an optimization method based on modified division rules. The idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is introduced in this paper to maximize the number of EVs that are served during power contingencies. For EVs carrying out the time-critical task(s), the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is defined as the amount of energy required by EVs to accomplish their upcoming task(s). For EVs carrying out delay-tolerant tasks, it is defined as the amount of energy required by EVs to travel to a nearby healthy charging station. To this end, an EV ranking mechanism is devised considering the full and essential energy demands along with the urgency of EVs. Subsequently, EVs with higher ranks are prioritized during energy allocation. The performance of the proposed method is compared with four existing division rules, i.e., proportional, constrained equal awards, constrained equal losses, and sequential priority rules. A utilitarianism index is proposed to analyze the performance of these methods and fairness is evaluated using existing indices, such as Jain's fairness index and cost of fairness index. The proposed method has outperformed existing division rules in both utilitarianism and fairness for the essential energy demand. Finally, a sensitivity analysis of uncertain parameters such as EV fleet size, available/required energy, and weight factors is carried out to analyze the performance of the proposed method under various conditions.

Storage-assisted optical upstream transport scheme for task offloading in multi-access edge computing

Multi-access edge computing (MEC) applications are often implemented in the form of task offloading, which results in an unprecedented demand for data transfers among MEC servers. However, the combination of expensive and limited bandwidth, growing peak demand, and heterogeneous requirements of mixed traffic has posed a great challenge in terms of task offloading. In this study, we present a storage-assisted optical upstream transport scheme (SOUT) to overcome this challenge. Latency-critical (LC) tasks are given preemptive priority over delay-tolerant (DT) tasks. To reduce peak demand, the storage of an MEC server is introduced to temporarily store DT tasks. Resource partitioning is performed with an adjustable boundary based on traffic fluctuation. Analytic models are presented to investigate the interplay between SOUT and the performance of tasks. Our key findings reveal that there exist two trade-offs to be considered in SOUT. To balance the trade-offs, we formulate the spectrum partitioning and storage assignment problem as an optimization model and solve it using a heuristic approach. Studies show that SOUT provides lower blocking probability for both LC and DT tasks at the cost of slight preemption and limited storage usage when compared with two state-of-the-art optical transport schemes. We further show that 60% of network expenditures can be saved by trading cost-efficient storage for expensive link spectrum resources under a certain network scenario. Overall, this study aims to provide useful insights into task offloading over elastic optical links.

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

Profit maximization based task scheduling in hybrid clouds using whale optimization technique

ABSTRACTIn cloud computing system, task scheduling plays an important key role. The tasks provided by the user to allocate in cloud have to pay for the share of resources that are used by them. The requirement of task scheduling in the cloud environment has become more and more complex, and the amount of resources and tasks is growing rapidly. Therefore, an efficient task-scheduling algorithm is necessary for allocating the task efficiently in the cloud, which can achieve minimum resource utilization, minimum processing time, high efficiency, and maximum profit. In hybrid clouds to maximize the profit of a private cloud while guaranteeing the service delay bound of delay-tolerant tasks is studied in this article. Here, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the task-scheduling problem. Then WOA algorithm is compared with existing algorithms such as artificial bee colony algorithm (ABC) and Genetic algorithm (GA). The experimental result shows that the proposed WOA algorithm greatly increases the efficiency and achieves maximum profit for the private cloud.

Online auction for scheduling concurrent delay tolerant tasks in crowdsourcing systems

Crowdsourcing is an emerging paradigm for human-powered problem solving in the Internet of Things (IoT) era. In this paper, we consider the online task allocation problem in crowdsourcing systems. Instead of considering real-time tasks that need to be completed immediately, we allow delay tolerant tasks which workers can complete at their own pace but within given deadlines. An online auction scheme (SOIL) is proposed for parallel allocation of delay tolerant tasks in practical crowdsourcing systems with stochastic requests from task owners and dynamic availabilities of workers. On one hand, SOIL provides a way for workers to evaluate their true cost for accepting new tasks with the given deadlines, and come up with optimal bidding strategies to maximize their utility. On the other hand, SOIL offers strategies for the system manager to design an online auction mechanism to maximize the system-wide utility and maintain a regulated crowdsourcing market. We prove that SOIL achieves a near-optimal system-wide utility with task deadline constraints. Moreover, SOIL is truthful, individually rational and budget balance. Through comprehensive simulations and rigorous theoretical analysis, we demonstrate the effectiveness of SOIL.

Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy

With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents’ energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical data.

A load-aware resource allocation and task scheduling for the emerging cloudlet system

Cloudlet-assisted mobile cloud computing (MCC) emerges as a vital paradigm to address the problems of mobile services such as application time-out, data caching and traffic congestion in wireless network. The cloudlet has adequate resources to process multiple mobile requests simultaneously, but it is not as sufficient as a remote cloud data center. Currently the performance of MCC system is a subject to the lengthy network transmission latency due to the long distance between cloudlet and remote cloud. In this article, we focus on the variable user’s QoS requirements and budget of cloudlet provider, design a load-aware resource allocation and task scheduling (LA-RATS) strategy which adaptively allocates resource in MCC system for delay-tolerant and delay-sensitive mobile applications according to cloudlet’s load profile. Subsequently, a tree generation based task backfilling algorithm is proposed to raise the utilization of the cloudlet. Particularly, when cloudlet is overloaded, the restrictions of delay-sensitive applications’ deadlines are satisfied through further offloading the allocated delay-tolerant tasks in the cloudlet to distant cloud. From several systematic evaluations, it is shown that our strategy can significantly reduce the cloudlet’s monetary cost and turnaround time for delay-tolerant applications, and increase the deadline satisfaction rate of delay-sensitive applications.

Temporal Task Scheduling With Constrained Service Delay for Profit Maximization in Hybrid Clouds

As cloud computing is becoming growingly popular, consumers’ tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.