Faculty Publications
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Publications by NITK Faculty
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Item Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center(Institute of Electrical and Electronics Engineers, 2019) Sharma, N.K.; Guddeti, R.M.R.Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. © 2019 IEEE.Item An Iterative-Based Optimum Power and Resource Allocation in Application-Dependent Scenarios for One-to-One D2D Communication(Institute of Electrical and Electronics Engineers Inc., 2024) Raghu, T.V.; Manjappa, M.Efficient and timely sharing of critical information is crucial for Public Safety (PS) communications, which can be fulfilled using one of the cutting-edge technologies, Device-to-device (D2D) communication. During an emergency, the PS applications should be prioritized over other applications, ensuring the emergency messages reach the first responders in time. Due to its inherent characteristics, the evolved Node Base station will not prioritize or categorize the D2D communication based on its application type, thus treating all applications equally. Further, D2D communication introduces significant interference to cellular users and vice-versa while sharing resources, and it is vital to reduce the impact of these interferences to ensure the Quality of Service for all users in the network. Hence, this article proposes a novel interference management approach to increase the overall sum rate of the system. In addition, the proposed approach also allows more D2D communication in general, particularly PS application-based D2D communication, to be active in the network. As the formulated problem is a Mixed-Integer Non-Linear Programming (MINLP) type of problem, it is split into two sub-problems, namely, Iterative Resource Allocation and Sharing and Iterative Power Optimization to achieve a polynomial time complexity. The theoretical proofs adequately explain the algorithm's time complexity and convergence property. The simulation results show that the proposed system enhances the overall sum rate by allowing more active PS D2D applications in the network. © 2013 IEEE.Item An Efficient Application based Many-to-Many Resource Allocation and Sharing with Power Optimization for D2D Communication - A Clustered Approach(Korean Institute of Communications and Information Sciences, 2024) Raghu, R.T.; Manjappa, K.This study aims to give an edge to public safety applications over commercial applications in an underlay cellular-assisted device-to-device (D2D) communication. The proposed framework introduces two frameworks: Cluster-based many-to-many resource allocation and resource sharing framework (CMMRARS) and constant time power control algorithm (CTPCA). The RB assigned to a CUE can share with multiple DUE pairs, and the DUE pairs can also use RB assigned to multiple CUEs under the many-to-many strategy. The CMMRARS framework is responsible for resource allocation and resource sharing and accordingly, it is further divided into three sub-problems. The CTPCA framework is divided into two sub-problems and used to find optimal power for cellular users and D2D transmitters to avoid cross-tier and co-tier interference. The K-means clustering algorithm is employed to form application-specific clusters, and it ensures that more cellular users fall into the public safety clusters so that the D2D users will get more resource-sharing options. Cellular users use a weighted bipartite graph to form a priority list of D2D users for resource sharing. The main objective of the proposed work is to enhance the system’s sum rate by simultaneously reusing the same resource by multiple D2D pairs and safeguarding the Quality of Services provided to all kinds of network users. A theoretical justification is presented to ensure that the proposed frameworks terminate after a certain number of runs and congregate to a consistent matching. Simulation results show that the proposed method influences the overall system’s sum rate and provides a preference for public safety applications over commercial applications. © 2024 KICS.Item Energy- and Reliability-Aware Provisioning of Parallelized Service Function Chains With Delay Guarantees(Institute of Electrical and Electronics Engineers Inc., 2024) Chintapalli, V.R.; Killi, B.R.; Partani, R.; Tamma, B.R.; Siva Ram Murthy, C.Network Functions Virtualization (NFV) leverages virtualization and cloud computing technologies to make networks more flexible, manageable, and scalable. Instead of using traditional hardware middleboxes, NFV uses more flexible Virtual Network Functions (VNFs) running on commodity servers. One of the key challenges in NFV is to ensure strict reliability and low latency while also improving energy efficiency. Any software or hardware failures in an NFV environment can disrupt the service provided by a chain of VNFs, known as a Service Function Chain (SFC), resulting in significant data loss, delays, and wasted resources. Due to the sequential nature of SFC, latency increases linearly with the number of VNFs. To address this issue, researchers have proposed parallelized SFC or VNF parallelization, which allows multiple independent VNFs in an SFC to run in parallel. In this work, we propose a method to solve the parallelized SFC deployment problem as an Integer Linear Program (ILP) that minimizes energy consumption while ensuring reliability and delay constraints. Since the problem is NP-hard, we also propose a heuristic scheme named ERASE that determines the placement of VNFs and routes traffic through them in a way that minimizes energy consumption while meeting capacity, reliability, and delay requirements. The effectiveness of ERASE is evaluated through extensive simulations and it is shown to perform better than benchmark schemes in terms of total energy consumption and reliability achieved. © 2017 IEEE.Item Energy efficient and delay aware deployment of parallelized service function chains in NFV-based networks(Elsevier B.V., 2024) Chintapalli, V.R.; Partani, R.; Tamma, B.R.; Siva Ram Murthy, C.Network Functions Virtualization (NFV) replaces traditional hardware-based network equipment and middleboxes with flexible Virtualized Network Functions (VNFs) in order to reduce costs and improve agility and scalability. The VNFs are logically arranged in a specific sequence to form a Service Function Chain (SFC) which ensures that the traffic is processed according to the desired service requirements. However, the inherent length of SFCs leads to an undesirable increase in end-to-end delay experienced by the packets. Parallelized SFC (PSFC) addresses this problem by trying to allow multiple VNFs of the SFC to process packets in parallel by co-locating parallelizable VNFs on the same server. The energy-efficient deployment of PSFCs while considering the impact of contention for the shared resources on the server is unexplored in the literature. Hence, in this work, we formulate the PSFC deployment problem as an Integer Linear Program (ILP) that minimizes energy consumption while considering the impact of shared resource contentions without violating end-to-delay constraints. Since the ILP is NP-hard, we also propose a heuristic scheme named EPSFC, which provides flexible resource allocation-based deployment that minimizes the total energy consumption and ensures end-to-end delay requirements while considering the effects of shared resource contentions on the end-to-end delay. The effectiveness of EPSFC is evaluated through extensive simulations, and the results show a significant reduction in energy consumption while improving the PSFC acceptance ratio as compared to state-of-the-art schemes. © 2024 Elsevier B.V.Item FASE: fast deployment for dependent applications in serverless environments(Springer, 2024) Saha, R.; Satpathy, A.; Addya, S.K.Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29× speedup compared to the baselines. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.Item EFraS: Emulated framework to develop and analyze dynamic Virtual Network Embedding strategies over SDN infrastructure(Elsevier B.V., 2024) Keerthan Kumar, K.K.; Tomar, S.; Addya, S.K.; Satpathy, A.; Koolagudi, S.G.The integration of Software-Defined Networking (SDN) into Network Virtualization (NV) significantly enhances network management, isolation, and troubleshooting capabilities. However, it brings forth the intricate challenge of allocating Substrate Network (SN) resources for various Virtual Network Requests (VNRs), a process known as Virtual Network Embedding (VNE). It encompasses solving two intractable sub-problems: embedding Virtual Machines (VMs) and embedding Virtual Links (VLs). While the research community has focused on formulating embedding strategies, there has been less emphasis on practical implementation at a laboratory scale, which is crucial for comprehensive design, development, testing, and validation policies for large-scale systems. However, conducting tests using commercial providers presents challenges due to the scale of the problem and associated costs. Moreover, current simulators lack accuracy in representing the complexities of communication patterns, resource allocation, and support for SDN-specific features. These limitations result in inefficient implementations and reduced adaptability, hindering seamless integration with commercial cloud providers. To address this gap, this work introduces EFraS (Emulated Framework for Dynamic VNE Strategies over SDN). The goal is to aid developers and researchers in iterating, testing, and evaluating VNE solutions seamlessly, leveraging a modular design and customized reconfigurability. EFraS offers various functionalities, including generating real-world SN topologies and VNRs. Additionally, it integrates with a diverse set of evaluation metrics to streamline the testing and validation process. EFraS leverages Mininet, Ryu controller, and OpenFlow switches to closely emulate real-time setups. Moreover, we integrate EFraS with various state-of-the-art VNE schemes, ensuring the effective validation of embedding algorithms. © 2024 Elsevier B.V.Item TReB: Task dependency aware-Resource allocation for Internet of Things using Binary offloading(Elsevier B.V., 2025) Soni, P.; Hajare, A.G.; Keerthan Kumar, K.K.; Addya, S.K.The rapid growth of Internet of Things (IoT) applications in domains such as healthcare, smart homes, and autonomous vehicles has led to an exponential increase in data generated by compute intensive tasks. Efficiently offloading these tasks to nearby computational resources in fog environments remains a significant challenge due to the inherent heterogeneity and constrained resources of Fog Nodes (FNs). Most of the existing approaches fail to address the trade-offs between latency, energy, and resource utilization, particularly when managing dependent and independent task workloads. Moreover, establishing an offloading strategy within a densely interconnected IoT network is known to be NP-hard. To overcome these limitations, in this work, we propose a Task dependency-Aware Resource allocation for IoT using Binary offloading (TReB) framework by considering both independent and dependent tasks of IoT applications. The TReB utilizes the Analytic Hierarchy Process (AHP) technique to generate the preferences of FNs and tasks by considering diverse attributes. With preferences established, a binary offloading is handled through a one-to-many matching procedure, utilizing a Deferred Acceptance Algorithm (DAA). It allows TReB to jointly minimize system energy consumption, latency, and the number of outages in an IoT network. We evaluated the effectiveness of TReB through simulation experiments, and results show that the proposed approach achieves a 49.1%, 62.4%, and 41.7% minimization in overall system latency, energy, and outages compared to the existing baselines. © 2025 Elsevier B.V.Item Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks(Academic Press, 2025) S.a, S.; E, M.; Addya, S.K.; Rahman, S.; Pal, S.; Karmakar, C.Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline. © 2025 The Authors