With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today's providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected-flows by KP-PSO, and the non-selectedflows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70% load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.

SummaryNowadays data center networks (DCNs) should handle the ever‐growing load generated by diverse applications. It particularly occurs under concurrent flow requests and so‐called mice flows (MFs): elephant flows (EFs) ratio. Concentrating on a binary vision over flow size classification (EF or MF) results in subsequent unpredicted load imbalance due to neglecting EF's distinctions including a wide range of sizes. As a result, some EFs might utilize a path owing qualities beyond the given EF's demands, while another EF with higher requirements is attending to use an over‐utilized path. This article proposes FMap, a fuzzy map for scheduling EFs through our proposed variant of traveling salesperson problem (TSP) toward DCNs. FMap represents a novel EF scheduling scheme that integrates flow prioritization and routing decisions under the event pf parallel incoming flows besides the cooperation of the controller and OpenFlow switches in software‐defined networking (SDN) paradigm. FMap adopts fuzzy inference process to overcome the vagueness over EF's resource allocation. Mainly, FMap proposes a new variant of TSP (optimized by genetic algorithm) which enables EF's group forwarding with a minimum cost. FMap reduces the total hop count of EFs through considering a single optimal path for delivering groups of EFs that contain a same tag (priority). The outstanding results represent a major improvement as compared with equal cost multiple path, Hedera, Sonoum, and Size‐KP‐PSO. Particularly, the results illustrate an outperforming by 3.76×, 0.21×, 0.15×, 0.03×, and 0.03× in terms of total hop count, EFs FCT, packet loss, goodput, and received packets as compared with Size‐KP‐PSO, respectively.