Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
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Item LitE: Load Balanced Virtual Data Center Embedding for Energy Efficiency in Data Centers(Association for Computing Machinery, Inc, 2025) Preetham, N.; Addya, S.K.; Keerthan Kumar, K.K.; Hegde, S.Network virtualization (NV) enables efficient management of physical network (PN) resources by partitioning them into virtual data center requests (VDCRs), consisting of interconnected virtual machines (VMs) and virtual links (VLs). A key challenge in NV is virtual data center embedding (VDCE), which allocates PN resources to VMs and VLs and is -hard problem. Existing VDCE strategies often fail to balance energy efficiency and resource distribution, leading to sub-optimal solutions with higher energy consumption in data centers (DCs). This work presents LitE, a load-balanced VDCE strategy focused on minimizing energy consumption in single-domain PN. LitE uses a resource management strategy that considers server utilization, overloading probability, and energy consumption to select suitable servers for VM embedding. It then applies Dijkstra's shortest path algorithm for VL embedding to optimize energy use. Experiments show LitE improves energy efficiency by compared to baseline methods through better resource utilization. © 2025 Copyright held by the owner/author(s).Item NORD: NOde Ranking-based efficient virtual network embedding over single Domain substrate networks(Elsevier B.V., 2023) Keerthan Kumar, T.G.; Addya, S.K.; Satpathy, A.; Koolagudi, S.G.Network virtualization (NV) allows the service providers (SPs) to partition the substrate resources in the form of isolated virtual networks (VNs) comprising multiple correlated virtual machines (VMs) and virtual links (VLs), capturing the dependencies. Though NV brought about multiple benefits, such as service isolation, improved quality-of-service (QoS), secure communication, and better utilization of substrate resources, it also introduced numerous research challenges. In this regard, one of the predominant challenges is assigning resources to the virtual components, i.e., VMs and VLs, also termed virtual network embedding (VNE). VNE comprises two closely related sub-problems, (i.) VM embedding and (ii.) VL embedding, and both the problems have been demonstrated to be NP-Hard. In the context of VNE, maximizing the revenue to cost ratio remains the focal point for the SPs as it not only boosts acceptance of VNRs but also effectively utilizes the substrate resources. However, the existing literature on VNE suffers from the following pitfalls: (i.) They only consider system resources or (ii.) limited topological attributes. However, both attributes are quintessential in accurately capturing the VNRs and the substrate network dependencies, thereby augmenting the revenue to cost ratio. This paper proposes an efficient VNE strategy called, NOde Ranking-based efficient virtual network embedding over single Domain substrate networks (NORD), to maximize the revenue to cost ratio. To address the problem of VM embedding, NORD utilizes a hybrid entropy and the technique for order of preference by similarity to ideal solution (TOPSIS) based ranking strategy for VMs and servers considering both system and topological attributes that effectively capture the dependencies. Once the ranking is generated, A greedy VM embedding followed by shortest path VL embedding completes the assignment. Simulation results confirm that NORD attains a 40% and 61% increment in average acceptance and revenue-to-cost ratios compared to the baselines. © 2023 Elsevier B.V.Item InDS: Intelligent DRL Strategy for Effective Virtual Network Embedding of an Online Virtual Network Requests(Institute of Electrical and Electronics Engineers Inc., 2024) Keerthan Kumar, T.G.K.; Addya, S.K.; Koolagudi, S.G.Network virtualization is a demanding feature in the evolution of future Internet architectures. It enables on-demand virtualized resource provision for heterogeneous Virtual Network Requests (VNRs) from diverse end users over the underlying substrate network. However, network virtualization provides various benefits such as service separation, improved Quality of Service, security, and more prominent resource usage. It also introduces significant research challenges. One of the major such issues is allocating substrate network resources to VNR components such as virtual machines and virtual links, also named as the virtual network embedding, and it is proven to be mathbb {N}mathbb {P} -hard. To address the virtual network embedding problem, most of the existing works are 1) Single-objective, 2) They failed to address dynamic and time-varying network states 3) They neglected network-specific features. All these limitations hinder the performance of existing approaches. This work introduces an embedding framework called Intelligent Deep Reinforcement Learning (DRL) Strategy for effective virtual network embedding of an online VNRs (InDS). The proposed InDS uses an actor-critic model based on DRL architecture and Graph Convolutional Networks (GCNs). The GCN effectively captures dependencies between the VNRs and substrate network environment nodes by extracting both network and system-specific features. In DRL, the asynchronous advantage actor-critic agents can learn policies from these features during the training to decide which virtual machines to embed on which servers over time. The actor-critic helps in efficiently learning optimal policies in complex environments. The suggested reward function considers multiple objectives and guides the learning process effectively. Evaluation of simulation results shows the effectiveness of InDS in achieving optimal resource allocation and addressing diverse objectives, including minimizing congestion, maximizing acceptance, and revenue-to-cost ratios. The performance of InDS exhibits superiority in achieving 28% of the acceptance ratio and 45% of the revenue-to-cost ratio by effectively managing the network congestion compared to other existing baseline works. © 2013 IEEE.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 SEDViN: Secure embedding for dynamic virtual network requests using a multi-attribute matching game(Academic Press Inc., 2025) Kumar, T.G.K.; Kumar, R.; Achal, A.M.; Satpathy, A.; Addya, S.K.Network virtualization (NV) has gained significant attention as it allows service providers (SP) to share substrate network (SN) resources. It is achieved by partitioning them into isolated virtual network requests (VNRs) comprising interrelated virtual machines (VMs) and virtual links (VLs). Although NV provides various advantages, such as service separation, enhanced quality-of-service, reliability, and improved SN utilization, it also presents multiple scientific challenges. In this context, one pivotal challenge encountered by the researchers is secure virtual network embedding (SVNE). The SVNE encompasses assigning SN resources to components of VNR, i.e., VMs and VLs, adhering to the security demands, which is a computationally intractable problem, as it is proven to be NP-Hard. In this context, maximizing the acceptance and revenue-to-cost ratios remains of utmost priority for SPs as it not only increases the revenue but also effectively utilizes the large pool of SN resources. Though VNE is a well-researched problem, the existing literature has the following flaws: (i.) security features of VMs and VLs are ignored, (ii.) limited consideration of topological attributes, and (iii.) restricted to static VNRs. However, SPs need to develop an embedding framework that overcomes the abovementioned pitfalls. Therefore, this work proposes a framework Secure Embedding for Dynamic Virtual Network requests using a multi-attribute matching game (SEDViN). In SedViN, the deferred acceptance algorithm (DAA) based matching game is used for effective embedding. SEDViN operates primarily in two steps to obtain a secure embedding of dynamic VNRs. Firstly, it generates a unified ranking for VMs and servers using a combination of entropy and a technique for order of preference by similarity to the ideal solution (TOPSIS), considering network, security, and system attributes. Taking these as inputs, in the second step, VNR embedding is conducted using the deferred acceptance approach based on a one-to-many matching strategy for VM embedding and VL embedding using the shortest path algorithm. The performance of SEDViN is evaluated through simulations and compared against different baseline approaches. The simulation outcomes exhibit that SEDViN surpasses the baselines with a gain of 56% in the acceptance and 44% in the revenue-to-cost ratios. © 2025 Elsevier Inc.