Zhi Ma

Bio: Zhi Ma is an academic researcher from Nanjing University. The author has contributed to research in topics: Job shop scheduling & Sensor node. The author has an hindex of 2, co-authored 6 publications receiving 13 citations.

Towards Revenue-Driven Multi-User Online Task Offloading in Edge Computing

Abstract: Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 ( 1 + ξ ) ln ( d + 1 ) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms.

Prolonging WSN lifetime with an actual charging model

Abstract: Recent breakthroughs in wireless power transfer make it possible to charge sensors over a long distance. Existing works have mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a linear superposition charging model, which may not be accurate in real life situations. We use the actual charging model, which has a nonlinear super-position and we consider the charging scheduling problem (CSP): given multiple chargers and a group of sensor nodes, how can the chargers be optimally scheduled so that the total charging time is minimized and each sensor node has at least energy E? We prove that CSP is NP-hard, and propose a weight-greedy algorithm to solve the problem. Unlike the algorithm proposed before, ours does not need to calculate all charger groups utility in advance, which reduces the complexity. Extensive simulations demonstrate that the performance of our algorithm with sparse network is almost as good as the optimal algorithm. In general cases, our algorithm outperforms the random algorithm. Furthermore, our algorithm obtains the best solution in two special cases.

Fast Interference-Aware Scheduling of Multiple Wireless Chargers

Abstract: Nowadays, breakthroughs in wireless power transfer make it possible to transfer energy over a long distance. Existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate in a real life situation. We use a concurrent charging model, which has a nonlinear superposition, and we consider the Fast Charging Scheduling problem (FCS): given multiple chargers and a group of sensor nodes, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor node has at least energy E? We prove that FCS is NP-complete and propose algorithms to solve the problem in 1D line and 2D plane respectively. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. We obtain a bound in 2D cases when chargers and sensors are uniformly distributed. Extensive simulations demonstrate that the performance of our algorithm is almost as good as the optimal algorithm when the distribution of chargers is not very dense.

Fast Charging Scheduling under the Nonlinear Superposition Model with Adjustable Phases

Abstract: Wireless energy transfer has been widely studied in recent decades, with existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate. We apply a nonlinear superposition model, and we consider the Fast Charging Scheduling problem (FCS): Given multiple chargers and a group of sensors, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor has at least energy E? We prove that FCS is NP-complete and propose a 2-approximation algorithm to solve it in one-dimensional (1D) line. In a 2D plane, we first consider a special case of FCS, where the initial phases of all chargers are the same, and propose an algorithm to solve it, which has a bound. Then we propose an algorithm to solve FCS in a general 2D plane. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. Extensive simulations demonstrate that the performance of our algorithm performs almost as good as the optimal algorithm.

Bundle Charging: Wireless Charging Energy Minimization in Dense Wireless Sensor Networks

Abstract: Using a mobile charger to wirelessly charge sensors is a promising yet not well-solved technique. Existing trajectory planning schemes for wireless charger either (1) fail to optimize the one-to-many characteristic of wireless charging or (2) fail to jointly optimize the charger movement cost and the charging cost. The objective of this paper is to find the optimal trajectory planning for a mobile charger in terms of energy minimization in the quadratic attenuation charging model. There exists a trade-off between charging efficiency and trajectory distance. If the mobile charger comes close to sensors, the charging efficiency is high, but the entire charging trajectory of the charger will be long and vice versa. To address this trade-off, we propose the idea of charging bundle and optimize the charger's trajectory based on the charging bundle rather than each sensor. The optimal charging bundle generation problem and the bundle trajectory optimization problem are discussed gradually. Both of them are proven to be NP-hard. Then, we first propose a greedy bundle generation algorithm with an approximation ratio of lnn, where n is the number of sensors. After that, we propose a TSP-based solution and further optimize the TSP-trajectory by jointly considering the adjacent charging locations. Theorems are proposed to effectively find the optimal location. Extensive experiments show that our scheme achieves a much better performance than traditional schemes.

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Abstract: Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This article presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.

Power Efficient Algorithms for Wireless Charging under Phase Shift in the Vector Model

Abstract: Recent technological advances in the domain of Wireless Power Transfer (WPT) have enabled the employment of previously unrealistic methods for power management in wireless systems. At the same time, some of the classical scalar models have proved incapable of capturing the multi-dimensional aspects of WPT that are similar to the superposition of wave functions. In this work, we consider the vector model which is by now a widely accepted model for WPT and its validity has been confirmed experimentally in the literature. Under the vector model, we study the problem of power maximization in a wireless network consisting of wireless chargers. We take the state of the art one step further by assuming that chargers can use phase-shifting to adjust their output in order to improve the total power provided by the network of chargers at selected points in the network area. Even though the technology for phase-shifting already exists, researchers have only recently tried to study it from an algorithmic perspective and algorithmic solutions are nearly inexistent. In this paper, we provide a rigorous formulation for the problem of power maximization as a semi-definite program with rank constraints and we present efficient centralized and distributed solutions, and also heuristics where only local information is available.

Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA satisfaction

Abstract: Task scheduling helps to improve the resource efficiency and the user satisfaction for Device-Edge-Cloud Cooperative Computing (DE3C), by properly mapping requested tasks to hybrid device-edge-cloud resources. In this paper, we focused on the task scheduling problem for optimizing the Service-Level Agreement (SLA) satisfaction and the resource efficiency in DE3C environments. Existing works only focused on one or two of three sub-problems (offloading decision, task assignment and task ordering), leading to a sub-optimal solution. To address this issue, we first formulated the problem as a binary nonlinear programming, and proposed an integer particle swarm optimization method (IPSO) to solve the problem in a reasonable time. With integer coding of task assignment to computing cores, our proposed method exploited IPSO to jointly solve the problems of offloading decision and task assignment, and integrated earliest deadline first scheme into the IPSO to solve the task ordering problem for each core. Extensive experimental results showed that our method achieved upto 953% and 964% better performance than that of several classical and state-of-the-art task scheduling methods in SLA satisfaction and resource efficiency, respectively.

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Abstract: Device-edge-cloud cooperative computing is increasingly popular as it can effectively address the problem of the resource scarcity of user devices. It is one of the most challenging issues to improve the resource efficiency by task scheduling in such computing environments. Existing works used limited resources of devices and edge servers in preference, which can lead to not full use of the abundance of cloud resources. This article studies the task scheduling problem to optimize the service level agreement satisfaction in terms of the number of tasks whose hard-deadlines are met for device-edge-cloud cooperative computing. This article first formulates the problem into a binary nonlinear programming, and then proposes a heuristic scheduling method with three stages to solve the problem in polynomial time. The first stage is trying to fully exploit the abundant cloud resources, by pre-scheduling user tasks in the resource priority order of clouds, edge servers, and local devices. In the second stage, the proposed heuristic method reschedules some tasks from edges to devices, to provide more available shared edge resources for other tasks cannot be completed locally, and schedules these tasks to edge servers. At the last stage, our method reschedules as many tasks as possible from clouds to edges or devices, to improve the resource cost. Experiment results show that our method has up to 59% better performance in service level agreement satisfaction without decreasing the resource efficiency, compared with eight of classical methods and state-of-the-art methods.