Conference Papers

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    MatchCloud: Service Matching for Multi Cloud Marketplace
    (Institute of Electrical and Electronics Engineers Inc., 2021) Chakma, A.; Kumar, S.; Mahato, P.K.; Satpathy, A.; Addya, S.K.
    The modern applications execute in the cloud via independent executable entities called virtual machines (VMs). In a typical multi-SP market with variable pricing and heterogeneous resource demands of VMs, resource allocation/placement is particularly challenging. To maximize the social welfare of the multi-SP markets, in this paper, we propose a resource allocation technique called MatchCloud formulated as a one-to-many matching game. Owing to the in-applicability of the classical deferred acceptance algorithm (DAA) due to size heterogeneity, we adopt a modified version of the algorithm. Moreover, preference generation is crucial for matching markets. Hence, we also present a simple yet efficient technique to assign preferences to two different stakeholders, i.e., VMs and SPs. Simulation results show that VM proposing RDA performs better compared to when SPs propose. © 2021 IEEE.
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    MatchVNE: A Stable Virtual Network Embedding Strategy Based on Matching Theory
    (Institute of Electrical and Electronics Engineers Inc., 2023) Keerthan Kumar, T.G.K.; Srivastava, A.; Satpathy, A.; Addya, S.K.; Koolagudi, S.G.
    Network virtualization (NV) can provide greater flexibility, better control, and improved quality of service (QoS) for the existing Internet architecture by enabling heterogeneous virtual network requests (VNRs) to share the substrate network (SN) resources. The efficient assignment of the SN resources catering to the demands of virtual machines (VMs) and virtual links (VLs) of the VNRs is known as virtual network embedding (VNE) and is proven to be NP-Hard. Deviating from the literature, this paper proposes a framework MatchVNE that is focused on maximizing the revenue-to-cost ratio of VNRs by considering a blend of system and topological attributes that better capture the inherent dependencies among the VMs. MatchVNE performs a stable VM embedding using the deferred acceptance algorithm (DAA). The preference of the VMs and servers are generated using a hybrid entropy, and the technique for order of preference by similarity to ideal solution (TOPSIS) based ranking strategy for VMs and servers. The attribute weights are determined using entropy, whereas the server and VM ranking are obtained via TOPSIS. The shortest path, VL-embedding, follows VM-embedding. The simulation results show that MatchVNE outperforms the baselines by achieving a 23% boost in the average revenue-to-cost-ratio and 44% improvement in the average acceptance ratio. © 2023 IEEE.