Latency (engineering)

About: Latency (engineering) is a research topic. Over the lifetime, 7278 publications have been published within this topic receiving 115409 citations. The topic is also known as: lag.

Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing.

Abstract: The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks

Abstract: Data collection is a major operation in Wireless Sensor Networks (WSNs) and minimizing the delay in transmitting the collected data is critical for a lot of applications where specific actions depend on the required deadline, such as event-based mission-critical applications. Scheduling algorithms such as Time Division Multiple Access (TDMA) are extensively used for data delivery with the aim of minimizing the time duration for transporting data to the sink. To minimize the average latency and the average normalized latency in TDMA, we propose a new efficient scheduling algorithm (ETDMA-GA) based on Genetic Algorithm(GA). ETDMA-GA minimizes the latency of communication where two dimensional encoding representations are designed to allocate slots and minimizes the total network latency using a proposed fitness function. The simulation results show that the performance of the proposed algorithm outperforms the existing state-of-the-art approaches such as Rand-LO, Depth-LO, DepthRe-LO, IDegRe-LO, and IDeg-LO in terms of average latency, average normalized latency, and average schedule length.

Measurement and analysis of LDAP performance

Abstract: The Lightweight Directory Access Protocol (LDAP) is being used for an increasing number of distributed directory applications. We describe a tool to analyze the performance of LDAP directories, and study the performance of a LDAP directory under a variety of access patterns. In the experiments, we use a LDAP schema proposed for the administration of Service Level Specifications (SLSs) in a differentiated services network. Individual modules in the server and client code are instrumented to obtain a detailed profile of their contributions to the overall system latency and throughput. We first study the performance under our default experiment setup. We then study the importance of the factors in determining scalability, namely front-end versus back-end processes, CPU capability, and available memory. At high loads, the connection management latency increases sharply to dominate the response in most cases. The TCP Nagle algorithm is found to introduce a very large additional latency, and it appears beneficial to disable it in the LDAP server. The CPU capability is found to be significant in limiting the performance of the LDAP server, and for larger directories, which cannot be kept in memory, data transfer from the disk also plays a major role. The scaling of server performance with the number of directory entries is determined by the increase in back-end search latency, and scaling with directory entry size is limited by the front-end encoding of search results, and, for out-of-memory directories, by the disk access latency. We investigate different mechanisms to improve the server performance.