Detection of object boundaries and significant features within an image is one of the most important processes in image processing and computer vision, as it allows the identification of object boundaries and significant features within an image. In applications such as autonomous vehicles, surveillance systems, and medical imaging, real-time processing has become increasingly important, which requires hardware accelerators. In this paper, the improved Sobel edge detection algorithm was implemented using Verilog as an FPGA-based algorithm designed to perform real-time image processing under the Sobel edge detection algorithm for specially RGB images. The proposed design proposes an application of horizontal and vertical Sobel kernels in parallel in order to compute the gradient magnitudes for 1028 × 720 RGB images by taking the gradient magnitudes of 3 × 3 pixel windows. This work focuses on algorithmic complex reduction by using eight directional approaches, and parallel processing leads to reducing the architectural utilization.

Load balancing is a critical problem within storage clusters. Existing algorithms often require high communication overhead because they have to collect sufficient information that they can then use to dispatch requests for hotspot data fairly. We propose an efficient scheme to achieve approximately optimal load balancing while keeping communication overhead low, namely self-adaptive replication management (SARM). Our approach estimates the access strength of hotspot data and establishes adequate number of replicas on nodes based on their load conditions. Each node uses a dynamic scheduling algorithm to address requests for hotspot data. If the load conditions of all dispatched nodes exceed the fair load estimate, a minimum scheduling algorithm is used to dispatch the requests; otherwise, a probabilistic scheduling algorithm is adopted instead. In another word, SARM automatically switches the scheduling algorithms according to fair load estimates and the load conditions on nodes. Consequently, it eliminates request burstiness while achieving stable load balancing. To avoid excessive communication overhead, the fair load estimates are updated within a fixed time interval. Moreover, when the load variations in a node exceed a specific threshold, their load conditions are dynamically updated to other nodes. Finally, we also consider data availability in SARM. We present simulations and analysis on the performance of our approach compared with other schemes under a variety of load conditions.

Many algorithms can uniformly distribute data to storage nodes in a storage system. However, it cannot avoid load imbalance because data has different popularity. To resolve this issue, we propose a novel dynamic replication scheme, namely, Active Replica Management (ARM). ARM actively establishes optimal number of copies for hotspot data according to data access behaviors and then efficiently distributes copies to other storage nodes based on current amount of copies related to hotspot data. To improve storage utilization, ARM automatically and gradually dereplicates the useless copies of hotspot data when they become nonhotspot data. ARM resolves load imbalance by allocating dynamic copies to adequate storage nodes, and hence it can prevent partial storage nodes from overburdening. Simulation results demonstrate that ARM is an efficient scheme with excellent performance on load balancing, significantly closer to Optimal Load Balancing (OLB). In addition, ARM’s performance outperforms both Static Load Balancing (SLB) and No Replica schemes.

Load balancing is one of the critical issues, which dominates system performance in a storage cluster. To overcome load imbalance, many algorithms have to maintain real-time load conditions that often lead to high-communication overheads. Furthermore, they may require many replicas that significantly decrease storage utilization. In this paper, we present a novel algorithm called intelligent offload detection (IOD) that not only achieves approximately optimal load balancing but also contributes to improve communication overheads and storage utilization. Once non-hotspot data become hotspot data, IOD automatically establishes new partial replicas and replicates them to nodes with minimum load levels in sequence. Next, the arriving requests may be divided into a different number of sub-requests based on the load levels of related nodes. The IOD algorithm works with a partial replica policy so that it is effective to overcome skewed data access originated from hotspot data. Moreover, this policy enhances storage utilization. By classifying the loads into corresponding load levels, IOD is unnecessary for frequent load updates that decrease communication overheads. Simulation results demonstrate that the load balancing in IOD outperforms self-adaptive replication management (SARM) and approaches to optimal load balancing (OLB) under various network conditions. Besides, the amount of required replicas in IOD is relatively smaller than that in SARM and OLB.

In this study, an efficient queue management scheme is proposed to achieve fair bandwidth sharing by rotating preference queues (RPQ). RPQ consists of a set of first-in-first-out (FIFO) output queues that are dynamically rotated. In addition, RPQ selects accepted packets and dispatches them to adequate output queues depending on packet distribution and preference. Using simulations, we show that RPQ tends to approach DRR, and it significantly outperforms CSFQ, DDE, CHOKe and FIFO in terms of fairness. RPQ has a complexity of O(1) and is simple to implement in high-speed networks.

In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks.