Tayebeh Bahreini

Bio: Tayebeh Bahreini is an academic researcher from Wayne State University. The author has contributed to research in topics: Enhanced Data Rates for GSM Evolution & Edge computing. The author has an hindex of 7, co-authored 18 publications receiving 175 citations. Previous affiliations of Tayebeh Bahreini include Shahed University.

Efficient placement of multi-component applications in edge computing systems

Abstract: Mobile Edge Computing (MEC) is a new paradigm which has been introduced to solve the inefficiencies of mobile cloud computing technologies. The key idea behind MEC is to enhance the capabilities of mobile devices by forwarding the computation of applications to the edge of the network instead of to a cloud data-center. One of the main challenges in MEC is determining an efficient placement of the components of a mobile application on the edge servers that minimizes the cost incurred when running the application. In this paper, we address the problem of multi-component application placement in edge computing by designing an efficient heuristic on-line algorithm that solves it. We also present a Mixed Integer Linear Programming formulation of the multi-component application placement problem that takes into account the dynamic nature of users' location and the network capabilities. We perform extensive experiments to evaluate the performance of the proposed algorithm. Experimental results indicate that the proposed algorithm has very small execution time and obtains near optimal solutions.

Energy-Aware Application Placement in Mobile Edge Computing: A Stochastic Optimization Approach

Abstract: The Quality of Service (QoS) in Mobile Edge Computing (MEC) systems is significantly dependent on the application offloading and placement decisions. Due to the movement of users in MEC networks, an optimal application placement might turn into the least efficient placement in few minutes. Thus, it is crucial to take the dynamics of the system into account when designing application placement mechanisms. On the other hand, energy consumption of servers is a significant component of the cost of services in MEC systems and must also be considered in the design of the mechanisms. In this article, we model the problem of energy-aware application placement in edge computing systems as a multi-stage stochastic program. The objective is to maximize the QoS of the system while taking into account the limited energy budget of the edge servers. To solve the problem, we design a novel parallel Sample Average Approximation (SAA) algorithm. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real-world trace data.

An Envy-Free Auction Mechanism for Resource Allocation in Edge Computing Systems

Abstract: One of the major challenges in Mobile Edge Computing~(MEC) systems is to decide how to allocate and price edge/cloud resources so that a given system's objective, such as revenue or social welfare, is optimized. One promising approach is to allocate these resources based on auction models, in which users place bids for using a certain amount of resources. In this paper, we address the problem of resource allocation and pricing in a two-level edge computing system. We consider a system in which servers with different capacities are located in the cloud or at the edge of the network. Mobile users compete for these resources and have heterogeneous demands. We design an auction-based mechanism that allocates and prices edge/cloud resources. The proposed mechanism is novel in the sense that it handles the allocation of resources available at the two-levels of the system by combining features from both position and combinatorial auctions. We show that the proposed mechanism is individually-rational and produces envy-free allocations. The first property guarantees that users are willing to participate in the mechanism, while the second guarantees that when the auction is finished, no user would be happier with the outcome of another user. We evaluate the performance of the proposed mechanism by performing extensive experiments. The experimental results show that the proposed mechanism is scalable and obtains efficient solutions.

Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems

Abstract: In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem ( ${{\sf ERAP}}$ ERAP ) as a Mixed-Integer Linear Program ( ${{\sf MILP}}$ MILP ) and prove that ${{\sf ERAP}}$ ERAP is ${{\sf NP}}$ NP -hard. To solve the problem efficiently, we propose two resource allocation mechanisms. First, we develop an auction-based mechanism and prove that the proposed mechanism is individually-rational and produces envy-free allocations . We also propose an ${{\sf LP}}$ LP -based approximation mechanism that does not guarantee envy-freeness, but it provides solutions that are guaranteed to be within a given distance from the optimal solution. We evaluate the performance of the proposed mechanisms by conducting an extensive experimental analysis on ${{\sf ERAP}}$ ERAP instances of various sizes. We use the optimal solutions obtained by solving the ${{\sf MILP}}$ MILP model using a commercial solver as benchmarks to evaluate the quality of solutions. Our analysis shows that the proposed mechanisms obtain near optimal solutions for fairly large size instances of the problem in a reasonable amount of time.

An MINLP Model for Scheduling and Placement of Quantum Circuits with a Heuristic Solution Approach

Abstract: Recent works on quantum physical design have pushed the scheduling and placement of quantum circuit into their prominent positions. In this article, a mixed integer nonlinear programming model is proposed for the placement and scheduling of quantum circuits in such a way that latency is minimized. The proposed model determines locations of gates and the sequence of operations. The proposed model is proved reducible to a quadratic assignment problem which is a well-known NP-complete combinatorial optimization problem. Since it is impossible to find the optimal solution of this NP-complete problem for large quantum circuits within a reasonable amount of time, a metaheuristic solution method is developed for the proposed model. Some experiments are conducted to evaluate the performance of the developed solution approach. Experimental results show that the proposed approach improves average latency by about 24.09p for the attempted benchmarks.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Abstract: With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

An Overview of Service Placement Problem in Fog and Edge Computing

Abstract: To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution, and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complicate the deployment problem. This article presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

Quantum computing with trapped ions

Abstract: Quantum information encoded in single trapped ions provides a promising avenue towards a scalable quantum computer. This contribution describes most of the necessary building blocks for such a device. Particular emphasis is given to the implementation of single-qubit and multi-qubit gate operations.

Energy-Aware Application Placement in Mobile Edge Computing: A Stochastic Optimization Approach

Abstract: The Quality of Service (QoS) in Mobile Edge Computing (MEC) systems is significantly dependent on the application offloading and placement decisions. Due to the movement of users in MEC networks, an optimal application placement might turn into the least efficient placement in few minutes. Thus, it is crucial to take the dynamics of the system into account when designing application placement mechanisms. On the other hand, energy consumption of servers is a significant component of the cost of services in MEC systems and must also be considered in the design of the mechanisms. In this article, we model the problem of energy-aware application placement in edge computing systems as a multi-stage stochastic program. The objective is to maximize the QoS of the system while taking into account the limited energy budget of the edge servers. To solve the problem, we design a novel parallel Sample Average Approximation (SAA) algorithm. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real-world trace data.