Computer and Information Sciences - Publications

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Browsing Computer and Information Sciences - Publications by Author "Adhikari, Mainak"
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    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.
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    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.
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    Akka framework based on the Actor model for executing distributed Fog Computing applications
    ( 2021-04-01) Srirama, Satish Narayana ; Dick, Freddy Marcelo Surriabre ; Adhikari, Mainak
    Future Internet of Things (IoT)-driven applications will move from the cloud-centric IoT model to the hybrid distributed processing model, known as Fog computing, where some of the involved computational tasks (e.g. real-time data analytics) are partially moved to the edge of the network to reduce latency and improve the network efficiency. In recent times, Fog computing has generated significant research interest for IoT applications, however, there is still a lack of ideal approach and framework for supporting parallel and fault-tolerant execution of the tasks while collectively utilizing the resource-constrained Fog devices. To address this issue, in this paper, we propose an Akka framework based on the Actor Model for designing and executing the distributed Fog applications. The Actor Model was conceived as a universal paradigm for concurrent computation with additional requirements such as resiliency and scalability, whereas, the Akka toolkit is a reference implementation of the model. Further, to dynamically deploy the distributed applications on the Fog networks, a Docker containerization approach is used. To validate the proposed actor-based framework, a wireless sensor network case study is designed and implemented for demonstrating the feasibility of conceiving applications on the Fog networks. Besides that, a detailed analysis is produced for showing the performance and parallelization efficiency of the proposed model on the resource-constrained gateway and Fog devices.
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    Application deployment using containers with auto-scaling for microservices in cloud environment
    ( 2020-06-15) Srirama, Satish Narayana ; Adhikari, Mainak ; Paul, Souvik
    A microservice-based application is composed of a set of small services that run within their own processes and communicate with a lightweight mechanism. Processing the microservices efficiently with minimum processing time and cost, while utilizing the computing resources efficiently, is a challenging task in a cloud environment. To address this challenge, in this paper, we propose a new container-aware application scheduling strategy with an auto-scaling policy. The proposed strategy deploys the requested applications on the best-fit lightweight containers, with minimum deployment time, based on the resource requirements. Another important issue of the container-aware cloud environment is the cold start effect, which is solved using a rule-based policy in the proposed work for minimizing deployment time and cost of the applications. Furthermore, a dynamic bin-packing strategy is designed for deploying the applications to the minimum number of physical machines (PMs) with efficient utilization of the computing resources. Finally, a heuristic-based auto-scaling policy has been designed for minimizing the wastage of the computing resources in the cloud data center. Through numerical evaluation, we have shown the superiority of the proposed method over the existing state-of-the-art algorithms in terms of processing time, processing cost, resource utilization, and required numbers of PMs.
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    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.
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    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.
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    Cybertwin-driven Resource Provisioning for IoE Applications at 6G-enabled Edge Networks
    ( 2021-01-01) Adhikari, Mainak ; Munusamy, Ambigavathi ; Kumar, Neeraj ; Srirama, Satish Narayana
    Cybertwin leverages the capabilities of networks and serves in multiple functionalities, by identifying digital records of activities of humans and things, from the Internet of Everything (IoE) applications. Cybertwin emerges as a promising solution along with next-generation communication networks, i.e., 6G technology, however, it increases additional challenges at the edge networks. Motivated by the above-mentioned perspectives, in this paper, we introduce a new cybertwin-driven edge framework using 6G-enabled technology with an intelligent service provisioning strategy, for supporting a massive scale of IoE applications. The proposed strategy distributes the incoming tasks from IoE applications using the Deep Reinforcement Learning technique based on their dynamic service requirements. Besides that, an Artificial Intelligence-driven technique, i.e., the Support Vector Machines (SVM) classifier model is applied at the edge network to analyze the data and achieve high accuracy. The simulation results over the real-time financial datasets demonstrate the effectiveness of the proposed service provisioning strategy and SVM model over the baseline algorithms in terms of various performance metrics. The proposed strategy reduces the energy consumption by 15% over the baseline algorithms, while increasing the prediction accuracy by 12% over the classification models.
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    DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing
    ( 2020-07-01) Adhikari, Mainak ; Mukherjee, Mithun ; Srirama, Satish Narayana
    By providing the flexible and shared computing and communication resources along with the cloud services, the fog computing became an attractive paradigm to support delay-sensitive tasks in the Internet of Things (IoT). The existing researches for offloading delay-sensitive tasks in a hierarchical fog-cloud environment mostly focused on minimizing the overall communication delay. However, a fair offloading strategy selects a suitable computing device in terms of fog node or cloud server based on the resource requirements of the task while meeting the deadline. In this article, we design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices. The proposed strategy assigns a priority on each task based on its deadline and assigns it to a suitable multilevel-feedback queue. This schema reduces the waiting time of the delay-sensitive tasks on the queue and minimizes the starvation problem of the low priority tasks. Moreover, the DPTO strategy selects an optimal computing device for each task based on its resource availability and transmission time from the IoT device. This strategy minimizes the overall offloading time of the tasks while meeting the deadlines. Finally, the extensive simulation results with various performance parameters show the effectiveness of the proposed strategy over the existing baseline algorithms.
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    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.
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    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.
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    Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment
    ( 2019-07-01) Adhikari, Mainak ; Srirama, Satish Narayana
    Over the last decades, cloud computing leverages the capability of Internet-of-Thing (IoT)-based applications by providing computational power as a form of a container or virtual machines (VMs). Most of the existing scheduling strategies deploy the VM instances for each task which require maximum start-up time and consumes maximum energy for processing the tasks. However, containers are a lightweight process and start in less than a second. In this paper, we develop a new energy-efficient container-based scheduling (EECS) strategy for processing various types of IoT and non-IoT based tasks with quick succession. The proposed method use accelerated particle swarm optimization (APSO) technique for finding a suitable container for each task with minimum delay. Resource scheduling is another important objective in a cloud environment for better utilization of the resources in the cloud servers. The EECS strategy can deploy the containers on an optimal cloud server with an optimal scheduling strategy. The main objectives of EECS are to minimize the overall energy consumptions and computational time of the tasks with efficient resource utilization. The effect of the control parameters of the APSO technique is investigated thoroughly. Through comparisons, we show that the proposed method performs better than the existing ones in terms of various performance metrics including computational time, energy consumption, CO 2 emission, Temperature emission, and resource utilization.
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    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.
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    Resource Management for Processing Wide Area Data Streams on Supercomputers
    ( 2020-08-01) Chung, Joaquin ; Adhikari, Mainak ; Srirama, Satish Narayana ; Jung, Eun Sung ; Kettimuthu, Rajkumar
    Modern scientific instruments generate enormous amount of data. Typically, the data collected from the instruments are stored in one or more files that are then moved to a distant supercomputer for processing. The final results are sent back to the user. In order to make effective use of the time on expensive instruments, experimenters want to process the data as they are generated. They want to stream the data from instruments' memory directly to a supercomputer's memory for analysis. Since the compute nodes in a supercomputer are not connected directly to the wide area network, the data streams need to be passed through intermediate gateway nodes. As opposed to the best effort file transfers, data streaming applications require resources at a specific time for a specific period. In this paper, we present a system model for enabling data streaming through gateway nodes and an algorithm to efficiently allocate gateway node resources along with compute nodes. We evaluate the algorithm using real-world traces on the Chameleon Cloud. The results show that our system can schedule compute and gateway resources efficiently for streaming analysis.
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    Service Deployment Strategy for Predictive Analysis of FinTech IoT Applications in Edge Networks
    ( 2021-01-01) Munusamy, Ambigavathi ; Adhikari, Mainak ; Balasubramanian, Venki ; Khan, Mohammad Ayoub ; Menon, Varun G. ; Rawat, Danda ; Srirama, Satish Narayana
    The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for Financial Technology (FinTech). The emergence of the Next-Generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyse the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this paper, we develop a ranked-based service deployment strategy and an Artificial Intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users & #x2019; satisfaction ratio. Besides that, an efficient service deployment strategy is developed using Hall & #x2019;s theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVM) algorithm is used at edge networks for analysing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.
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    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.