Modelling, Simulation and Design - Publications

Permanent URI for this collection

https://dspace.uohyd.ac.in/handle/1/59

Browse

Browsing Modelling, Simulation and Design - Publications by Author "Amgoth, Tarachand"
  • Item
    A comprehensive survey on nature-inspired algorithms and their applications in edge computing: Challenges and future directions
    ( 2022-04-01) Adhikari, Mainak ; Srirama, Satish Narayana ; Amgoth, Tarachand
    Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve. These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.
  • Item
    A survey on scheduling strategies for workflows in cloud environment and emerging trends
    ( 2019-08-01) Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    Workflow scheduling is one of the challenging issues in emerging trends of the distributed environment that focuses on satisfying various quality of service (QoS) constraints. The cloud receives the applications as a form of a workflow, consisting of a set of interdependent tasks, to solve the large-scale scientific or enterprise problems. Workflow scheduling in the cloud environment has been studied extensively over the years, and this article provides a comprehensive review of the approaches. This article analyses the characteristics of various workflow scheduling techniques and classifies them based on their objectives and execution model. In addition, the recent technological developments and paradigms such as serverless computing and Fog computing are creating new requirements/opportunities for workflow scheduling in a distributed environment. The serverless infrastructures are mainly designed for processing background tasks such as Internet-of-Things (IoT), web applications, or event-driven applications. To address the ever-increasing demands of resources and to overcome the drawbacks of the cloud-centric IoT, the Fog computing paradigm has been developed. This article also discusses workflow scheduling in the context of these emerging trends of cloud computing.
  • Item
    Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization
    ( 2020-05-01) Adhikari, Mainak ; Srirama, Satish Narayana ; Amgoth, Tarachand
    Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.
  • Item
    Collaborative AI-enabled Intelligent Partial Service Provisioning in Green Industrial Fog Networks
    ( 2021-01-01) Hazra, Abhishek ; Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    With the evolutionary development of the latency-sensitive Industrial Internet of Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this paper, we introduce a new Deep Reinforcement Learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Further, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. Experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.
  • Item
    Intelligent Service Deployment Policy for Next-Generation Industrial Edge Networks
    ( 2021-01-01) Hazra, Abhishek ; Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    Edge Computing has appeared as a promising technology for realizing industrial computation data at the edge of the network. The fundamental challenge in edge-enabled industrial networks is how to deploy the service requests while utilizing the available edge resources efficiently. In this paper, we aim to design an intelligent service deployment strategy for simultaneously handling both Industrial Internet of Things (IIoT) generated dynamic service requests and edge resources in the next-generation industrial networks. Initially, we present the objective function as the mixed-integer nonlinear programming problem for optimizing the energy-delay trade-off in the edge environment. To accomplish this objective, we model a heuristic-based task execution strategy and exploit the advantage of Deep Reinforcement Learning (DRL) to make accurate decisions in industrial networks. The proposed DRL-based strategy can learn well to control the industrial networks from its own experience and guarantees to handle as many service requests as possible using the set of available resource constraint edge servers. Experimental analysis reveals that the proposed strategy is robust to network changes and achieves better performance than existing algorithms in terms of energy consumption up to 13%, delay minimization by 23%, and other Quality of Service (QoS) parameters.
  • Item
    Joint Computation Offloading and Scheduling Optimization of IoT Applications in Fog Networks
    ( 2020-10-01) Hazra, Abhishek ; Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    In recent times, fog computing becomes an emerging technology that can exhilarate the cloud services towards the network edge for increasing the speeds up of various Internet-of-Things (IoT) applications. In this context, integrating priority-Aware scheduling and data offloading allow the service providers to efficiently handle a large number of real-Time IoT applications and enhance the capability of the fog networks. But the energy consumption has become skyrocketing, and it gravely affects the performance of the fog networks. To address this issue, in this paper, we introduce an Energy-Efficient Task Offloading (EETO) policy combined with a hierarchical fog network for handling energy-performance trade-off by jointly scheduling and offloading the real-Time IoT applications. To achieve this objective, we formulate a heuristic technique for assigning a priority on each incoming task and formulate a stochastic-Aware data offloading issue with an efficient virtual queue stability approach, namely the Lyapunov optimization technique. The proposed technique utilizes the current state information for minimizing the queue waiting time and overall energy consumption while meeting drift-plus-penalty. Furthermore, a constraint restricted progressive online task offloading policy is incurred to mitigate the backlog tasks of the queues. Extensive simulation with various Quality-of-Service (QoS) parameters signifies that the proposed EETO mechanism performs better and saves about 23.79% of the energy usage as compared to the existing ones.
  • Item
    Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach
    ( 2020-08-01) Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    Cloud computing is a distributed computing paradigm, that provides infrastructure and services to the users using the pay-as-you-use billing model. With the increasing demands and diversity of the scientific workflows, the cloud providers face a fundamental issue of resource provisioning and load balancing. Although, the workflow scheduling in the cloud environment is extensively studied, however, most of the strategies ignore to consider the multiple conflicting objectives of the workflows for scheduling and resource provisioning. To address the above-mentioned issues, in the paper, we introduce a new workflow scheduling strategy using the Firefly algorithm (FA) by considering multiple conflicting objectives including workload of cloud servers, makespan, resource utilization, and reliability. The main purpose of the FA is to find a suitable cloud server for each workflow that can meet its requirements while balancing the loads and resource utilization of the cloud servers. In addition, a rule-based approach is designed to assign the tasks on the suitable VM instances for minimizing the makespan of the workflow while meeting the deadline. The proposed scheduling strategy is evaluated over Google cluster traces using various simulation runs. The control parameters of the FA are also thoroughly investigated for better performance. Through the experimental analysis, we prove that the proposed strategy performs better than the state-of-the-art-algorithms in terms of different Quality-of-Service (QoS) parameters including makespan, reliability, resource utilization and loads of the cloud servers.
  • Item
    Stackelberg game for service deployment of iot-enabled applications in 6g-aware fog networks
    ( 2021-04-01) Hazra, Abhishek ; Adhikari, Mainak ; Amgoth, Tarachand ; Srirama, Satish Narayana
    Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%-20% service delay and 20%-25% of service cost over the standalone fog and cloud frameworks.
  • Item
    Survey on recent advances in IoT application layer protocols and machine learning scope for research directions
    ( 2022-01-01) Donta, Praveen Kumar ; Srirama, Satish Narayana ; Amgoth, Tarachand ; Annavarapu, Chandra Sekhara Rao
    The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT's foremost task is ensuring that there is proper communication between different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles have focused on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.