Browsing by Author "Mashayekhy, Lena"
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Item A Game-Theoretic Approach to Energy-Efficient Elevator Scheduling in Smart Buildings(IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023-02-22) Maleki, Erfan Farhangi; Bhatta, Dixit; Mashayekhy, LenaBuildings, producing more carbon footprints than the transportation sector, account for a significant portion of the United States’ total energy consumption. By designing modern automation techniques, smart buildings can significantly reduce energy consumption, protect the environment, and consequently improve quality of life. This article focuses on the automation of elevator scheduling, which is an NP-Hard problem, to reduce energy usage in smart buildings and improve users’ quality of experience. We propose an optimal mathematical model for the elevator scheduling problem using integer programming. We then propose a novel game-theoretic approach that captures interactions within the elevator system to reduce energy consumption and enhance user experience. We propose a request coalition formation game, where nonoverlapping coalitions of user requests are served by elevators to minimize their movements and energy consumption while reducing service time and stops for users. We analyze the performance of our proposed approach using the optimal solution as a benchmark and Nearest Car and Fixed Sectoring algorithms as rivals. The experiments show that our approach is significantly efficient in terms of energy consumption and service time, making it suitable for smart buildings.Item A Bifactor Approximation Algorithm for Cloudlet Placement in Edge Computing(IEEE Transactions on Parallel and Distributed Systems, 2021-11-15) Bhatta, Dixit; Mashayekhy, LenaEmerging applications with low-latency requirements such as real-time analytics, immersive media applications, and intelligent virtual assistants have rendered Edge Computing as a critical computing infrastructure. Existing studies have explored the cloudlet placement problem in a homogeneous scenario with different goals such as latency minimization, load balancing, energy efficiency, and placement cost minimization. However, placing cloudlets in a highly heterogeneous deployment scenario considering the next-generation 5G networks and IoT applications is still an open challenge. The novel requirements of these applications indicate that there is still a gap in ensuring low-latency service guarantees when deploying cloudlets. Furthermore, deploying cloudlets in a cost-effective manner and ensuring full coverage for all users in edge computing are other critical conflicting issues. In this article, we address these issues by designing a bifactor approximation algorithm to solve the heterogeneous cloudlet placement problem to guarantee a bounded latency and placement cost, while fully mapping user applications to appropriate cloudlets. We first formulate the problem as a multi-objective integer programming model and show that it is a computationally NP-hard problem. We then propose a bifactor approximation algorithm, ACP, to tackle its intractability. We investigate the effectiveness of ACP by performing extensive theoretical analysis and experiments on multiple deployment scenarios based on New York City OpenData. We prove that ACP provides a (2,4)-approximation ratio for the latency and the placement cost. The experimental results show that ACP obtains near-optimal results in a polynomial running time making it suitable for both short-term and long-term cloudlet placement in heterogeneous deployment scenarios.Item Time-Constrained Service Handoff for Mobile Edge Computing in 5G(IEEE Transactions on Services Computing, 2022-09-22) Sharghivand, Nafiseh; Mashayekhy, Lena; Ma, Weibin; Dustdar, SchahramMany mobile device applications require low end-to-end latency to edge computing infrastructure when offloading their computation tasks in order to achieve real-time perception and cognition for users. User mobility brings significant challenges in providing low-latency offloading due to the limited coverage area of cloudlets. Virtual machine (VM)/container handoff is a promising solution to seamlessly transfer services from one cloudlet to another to maintain low latency as users move. However, an inefficient path planning for the handoff can result in system congestion and consequently poor quality of service (QoS). The situation can even worsen by selfish users who intentionally lie about their true parameters to achieve better service at the cost of degrading the whole system's performance. To fill this research gap, we propose an Online Service Handoff Mechanism (OSHM) to provide an efficient path dynamically for transferring VM/container from the current serving cloudlet to a nearby cloudlet at the destination of a mobile user. Our proposed path planning algorithm is based on a label correction methodology, leading to polynomial time complexity. OSHM is accompanied by our proposed payment determination function to discourage misreporting of unknown parameters. We discuss the theoretical properties of our proposed mechanism in implementing a system equilibrium and ensuring truthfulness. We also perform a comprehensive assessment through extensive experiments which show the efficiency of OSHM in terms of workload, handoff time, consumed energy, and other metrics compared to several benchmarks. Experimental results show that OSHM outperforms other algorithms, reducing at least 61% in average workload, 33% in average handoff time, and 29% in average energy consumption.