http://localhost:8080/xmlui/handle/123456789/1060 2026-04-26T02:01:20Z http://localhost:8080/xmlui/handle/123456789/3512 Title: Not All Clouds Are Transparent: Handling Unavailable Attributes in CSP Selection Authors: Navya, P; Panda, Sanjaya Kumar; Rout, Rashmi Ranjan Abstract: Selecting a cloud service provider (CSP) is a complex task for enterprises, as each CSP offers a distinct set of quality of service (QoS) attributes with varying values [1]. These attributes include durability, response time, best practices, throughput, availability, compliance, latency, reliability, and successability [2]. Traditionally, researchers have employed a multi-attribute decision-making (MADM) algorithm to select a suitable CSP, operating under the assumption that complete QoS attribute values are available. However, certain QoS attribute values may be unavailable in many CSPs. This lack of values makes the selection process non-transparent for enter prises, hindering their ability to identify the most suitable CSP. Consequently, the unavailable values can be imputed using either simple or advanced techniques to facilitate the selection of a suitable CSP. Common imputation techniques include mean, minimum, maximum, regression, k-nearest neighbour, and rough set theory. After the imputation process, an MADM algorithm can then be applied to identify the most suitable CSP. Such MADM algorithms include the technique for order preference by similarity to ideal solution (TOPSIS), the best holistic adaptable ranking of attributes technique (BHARAT), and multi-objective optimization on the basis of ratio analysis Description: NITW 2025-12-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3511 Title: Delay-Aware Task Offloading for Mobile Fog Nodes in Smart Cities: A Fuzzy and Q-Learning Approach Authors: Asif Thanedar, Md; Panda, Sanjaya Kumar Abstract: Vehicular services, such as remote intelligent control, ve hicular video streaming and virtual reality-based driving assis tance, are delay-sensitive tasks [1]. These tasks request more computational powers and consume more energy for process ing. Thus, these tasks are offloaded to the fog node (FN) in the fog computing (FC) infrastructure. Therefore, integrating vehicular networks and FC leads to fog computing-based vehicular ad-hoc networks (FCVANETs) [2]. In FCVANETs, vehicular services are disrupted when the FN’s battery is depleted, particularly in remote areas lacking consistent power, such as forests and mountainous terrain. Reviving the FNs necessitates either solar energy or human intervention, and providing continuous services until the next recharge cycle without exhausting energy is challenging. As a result, we consider that FNs are equipped with powerful batteries that are periodically recharged but lack a permanent power source. Further, we consider the FNs coverage regions overlapping with neighbouring FNs and delivering services to the re quested vehicles in the overlapping regions. However, vehicles without service requests and ample computational capabilities are designated as mobile fog nodes (MFNs). We introduce a task-offloading approach called fuzzy and reinforcement learning task offloading (FRLTO) to increase the lifetime of FCVANETs. The existing work [2] addresses FN factors, such as energy consumption, latency, delay constraints, and relia bility, but overlooks the challenges that arise in FCVANETs when the coverage areas of FNs intersect with neighbouring FNs. Conversely, the task offloading approach presented in this work considers factors such as task data size, delay constraints, and MFNs’ sojourn time within the overlapping region. Description: NITW 2023-12-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3353 Title: A Quality of Service-Aware Least Utilization First Algorithm for Smart On-Street Parking Management Authors: Panda, Sanjaya Kumar; Shinde, Dhiraj Abstract: The rapid growth in the number of vehicles and limited parking slots has made on-street parking management an increasingly complex challenge. Intelligent transportation systems (ITS) have emerged as a key solution, enhancing the efficiency of smart on-street parking management by delivering real-time information on slot availability, optimizing space utilization, and significantly reducing the time drivers spend searching for parking. Researchers have developed various pricing strategies to map the parking requests to the parking slots, maximizing revenue generation. However, most strategies only consider the provider’s perspective to improve the utilization of parking slots without considering the user’s perspective, leading to dissatisfaction among the users. Therefore, we model the user’s perspective using the quality of service (QoS) and introduce a QoS-aware least utilization first (LUF) algorithm for smart on-street parking management. LUF aims to maximize revenue generation through a dynamic pricing strategy without compromising user satisfaction. It satisfies the QoS of the parking requests submitted by the users, thereby selecting the less-utilized parking slots. The performance of LUF is evaluated by taking the Seattle city dataset and compared using three algorithms, namely first come, first serve (FCFS), round robin (RR), and highest price first (HPF), in terms of number of accepted requests (Racc), number of rejected requests (Rrej ), average price (AV) and revenue (RV). The simulation results across ten areas of the Seattle city dataset show that LUF outperforms in al the performance metrics compared to FCFS, RR, and HPF. Description: NITW 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3351 Title: HEFT-IPF: A Three-Phase Scheduling Algorithm for Heterogeneous Multi-Cloud Environment Authors: Panda, Sanjaya Kumar; Jadhav, Amey Abstract: Workflow scheduling (WFS) in a heterogeneous multi-cloud (HMC) environment is a critical problem, aiming to minimize overall completion time (i.e., makespan) and maximize resource utilization. Numerous heuristic and metaheuristic algorithms have been developed to address the problem of WFS. One well-known and benchmark algorithm is called heterogeneous earliest finish time (HEFT). This algorithm allocates the precedence-constrained workflow tasks to the clouds by calculating the task prioritization, followed by the cloud selection. However, it does not consider the task characterization phases, namely initialization, processing, and finalization (IPF), which leads to poor makespan and resource utilization. Therefore, this paper introduces a WFS algorithm called HEFT-IPF to enhance the HEFT algorithm’s performance by considering task characterization. HEFT-IPF algorithm overlaps the execution of tasks by executing their initialization and finalization phases while strictly preserving their precedence constraints. The HEFTIPF algorithm performance is compared with that of the HEFT algorithm by considering various scientific workflows, namely epigenomics, laser interferometer gravitational-wave observatory (LIGO), cybershake, sRNA identification protocol using highthroughput technology (SIPHT), and montage. Two performance measures, makespan and resource utilization, are used to compare with HEFT and HEFT-IPF algorithms. Simulation results show that the HEFT-IPF algorithm outperforms the HEFT algorithm, achieving a 28.36% average reduction in makespan and a 23.33% average improvement in resource utilization. Description: NITW 2025-01-01T00:00:00Z