Application Partitioning Algorithm Research Articles

Research on the Construction Model of 5G Special Town Based on Edge Computing

This study conducts a study on edge computing technology, focusing on the advantages of edge computing technology and its applications, and delves into the edge computing architecture, which leads to an in-depth study on the construction of a 5G special town model. Based on the existing 5G characteristic town construction model, this study proposes a new model of edge computing and proposes an application partitioning algorithm based on this model by analyzing the edge computing scheduling scenarios to minimize the total time cost of the system. By analyzing the edge computing scheduling scenarios, an application partitioning algorithm based on this model is proposed, so that the total time cost of the system is minimized. This study analyzes and studies the current situation of infrastructure construction, and investment in the special town analyzes the significance and purpose of its construction and develops the research method and technical route of this study as a result. Then, the connotation of characteristic town and infrastructure is elaborated, and the value, characteristics, and role of infrastructure construction of characteristic town are analyzed; the influencing factors of infrastructure investment in characteristic town are analyzed; the development of the characteristic town has endless potential, and the development of characteristic town must meet the demand of social economy as well as town population for infrastructure support. However, there are more risk factors for infrastructure investment in characteristic towns, and the investment risk is larger than general projects, so it is necessary to strengthen risk management and do a good job of investment protection.

A quad-form clustered mapping approach for large-scale applications of reconfigurable computing systems

Reconfigurable Computing (RC) systems with hardware implementation feature demonstrated a promising solution for the demand of enormous processing power. However, RC systems suffer from long compilation phases when preparing large-scale applications to execute on reconfigurable hardware. In this paper, a novel quad-form clustered approach has been introduced to manage the hardware resources with low overhead and acceptable quality. For this purpose, two quad-form resource clustering and application partitioning algorithms are proposed to solve the entire mapping problem in intra and inter-cluster parts. Moreover, an analytical distance estimation is derived to properly manage clustered resources in the reconfigurable hardware. Several extensive experimental scenarios on both synthetic and real applications have been conducted to evaluate the effectiveness of the proposed approach in comparison with the state-of-art methods and the results showed that significant improvements have been achieved in terms of quality and time overhead for large-scale applications.

An energy-efficient algorithm for multi-site application partitioning in MCC

Prolonging battery life for mobile device has been an urgent issue in mobile computing. Computation offloading is an effective approach to reduce energy consumption on mobile device. However, most previous work focuses on offloading computation from mobile to a single server, which cannot adopt to mobile cloud computing (MCC) where data and computation are commonly distributed to multiple sites. This paper proposes a multi-site application partitioning algorithm named cyclic random movement (CRM) based genetic algorithm (CRMGA), which considers the heterogeneity of multiple remote sites and aims to find the optimal partition efficiently by combining a local optimization algorithm with genetic search. Simulation results demonstrate that CRMGA can significantly reduce the energy consumption of mobile device as much as 90% to 98.5% and its performance also outperforms GA and hill-climbing heuristic.

Energy-aware dynamic application partitioning algorithm in mobile computing

Energy-aware dynamic application partitioning algorithm in mobile computing