Cache Servers Research Articles

Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-Based Two-Timescale Approach

Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-Based Two-Timescale Approach

Stackelberg Game-Based Task Offloading for Joint Service Caching and Resource Allocation Optimization in UAV-Assisted VEC

Stackelberg Game-Based Task Offloading for Joint Service Caching and Resource Allocation Optimization in UAV-Assisted VEC

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

UAV-Enhanced Service Caching for IoT Systems in Extreme Environments

UAV-Enhanced Service Caching for IoT Systems in Extreme Environments

Digital twin intelligent transportation system (DT‐ITS)—A systematic review

AbstractDigital twin (DT) has attracted much attention from the transportation community over the past 6 years. Combining the DT with intelligent transportation system (ITS) forms a digital twin intelligent transportation system (DT‐ITS), which stands as one of the most effective solutions for addressing current complex traffic problems. Due to the rapid advancements in this field and a lack of recent literature reviews, this paper first reviews relevant literature on DT‐ITS architecture design, to comprehend its core structure, methods, potential services and stakeholders, and implementation challenges, and then discusses DT‐ITS core considerations, aiming to provide a general configuration model of DT‐ITS for future development. Second, this paper focuses on reviewing the existing progress of DT‐ITS services within the 32 categories of ITS services, adopting the service‐centred point of view, to explore the potential DT‐ITS services, proposed delivery methods, challenges, and opportunities for various stakeholders. Third, key enabling technologies supporting DT‐ITS are reviewed and discussed, such as data fusion, cooperative perception, multi‐access edge computing (MEC) (including computing offloading and service caching), federated learning, edge‐cloud collaboration, secure and efficient communication (including Blockchain [BC], 5G), virtual modelling, and eXtended reality (XR). Finally, the paper identifies development trends and provides recommendations for future advancements.

Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching.

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.

XRL-SHAP-Cache: an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

XRL-SHAP-Cache: an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

Cooperative Service Caching in Vehicular Edge Computing Networks Based on Transportation Correlation Analysis

Cooperative Service Caching in Vehicular Edge Computing Networks Based on Transportation Correlation Analysis

Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing

With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing-intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

Learning-driven service caching in MEC networks with bursty data traffic and uncertain delays

Mobile edge computing (MEC) provides extremely low-latency services for mobile users, by attaching computing resources to 5G base stations in an MEC network. Network service providers can cache their services from remote data centers to base stations to serve mobile users within their proximity, thereby reducing service latencies. However, mobile users of network services usually have bursty requests that require immediate processing in the MEC network. The data traffic of such requests is bursty, since mobile users have various hidden features including locations, group tags, and mobility patterns. Furthermore, such bursty data traffic causes uncertain congestion at base stations and thus leads to uncertain processing delays. Considering the limited resources of base stations, network services may not be able to be placed into base stations permanently to handle the bursty data traffic. As such, network services need to be dynamically cached in the MEC network to fully address the bursty data traffic and uncertain processing delays.In this paper, we investigate the problem of dynamic service caching and task offloading in an MEC network, by adopting the online learning technique to harness the challenges brought by bursty data traffic and uncertain processing delays. We first propose an online learning algorithm for the problem with uncertain processing delays, by utilizing the multi-armed bandits technique, and analyze the regret bound of the proposed algorithm. We then propose another online learning algorithm for the problem with bursty data traffic and uncertain processing delays, which adaptively learns the bursty data traffic of requests, based on small samples of mobile users’ hidden features. We also propose a novel architecture of Generative Adversarial Network (GAN) to accurately predict user demands using small samples of mobile users’ hidden features. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both bursty data traffic and uncertain delays. We finally evaluate the performance of the proposed algorithms by simulations, using a real data trace. Experimental results show that the performance of the proposed algorithms outperforms existing ones by up to 44% in terms of average delay.

A Cooperative Community-Based Framework for Service Caching and Task Offloading in Multi-Access Edge Computing

A Cooperative Community-Based Framework for Service Caching and Task Offloading in Multi-Access Edge Computing

Many-objective joint optimization for dependency-aware task offloading and service caching in mobile edge computing

Many-objective joint optimization for dependency-aware task offloading and service caching in mobile edge computing

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

Decoding Efficiency: A Technical Exploration of Apache, Nginx and Varnish Cache Server through Comprehensive Performance Metrics

Web servers play a crucial role in delivering web content efficiently to users. When a web server receives a request, it processes the request for the requested resources. One method to optimize this process is through the use of cache servers. Cache servers store frequently accessed data in memory, reducing the need to retrieve data from the original source each time a request is made. By leveraging cache servers, web servers can significantly enhance their performance by reducing response times. When a cache server successfully serves a request with a cache hit, it eliminates the need for the web server to process the request and retrieve the data, thereby speeding up the response time. This efficiency is crucial for improving user experience, as faster response times lead to quicker loading of web pages and reduced latency. Through this technical exploration of Apache, Nginx, and Varnish cache servers, we aim to analyze and compare their performance metrics to determine the most effective solution for reducing response times and optimizing web server efficiency. Understanding how cache servers impact performance in terms of cache hit ratio, cache miss ratio, client connections, CPU usage, memory usage, error rates, requests per second, and bandwidth will provide valuable insights into selecting the best cache server solution for improved web server performance. Keywords— Cache Server, Cache, Response time, Apache, Nginx, Varnish.

Optimal service caching, pricing and task partitioning in mobile edge computing federation

Mobile Edge Computing (MEC) federations aim to establish a joint edge service model between Edge Infrastructure Providers (EIPs) and clouds, facilitating the sharing and utilization of MEC services and resources. However, in such a hierarchical multi-EIP MEC federation environment, optimizing service caching, task partitioning, and pricing strategies to maximize the profits gained by EIPs and minimize the cost of mobile devices (MDs) is challenging. To address this challenge, This paper first formulates a two-stage multi-leader, multi-follower Stackelberg game between EIPs and MDs. Then, a new method combining a Stackelberg-based Multi-Agent Deep Deterministic Policy Gradient (STMADDPG) algorithm and a Transformer-based Popularity Prediction (TPP) model is devised to learn the optimal strategies. Extensive experiments with real-world datasets are conducted to demonstrate the excellent performance of the proposed STMADDPG-TPP method.

Low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model

To meet the low delay requirements for data content access in industrial IoT, efficient content caching strategies need to be designed in the Mobile Edge Computing (MEC). Most existing caching policies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal order of content popularity without fully considering the graph topology of MEC servers and ignore the spatial correlation of content popularity. This paper proposes a low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model (STCC). We integrate graph convolutional neural network and gated recurrent unit to construct spatio-temporal graph convolutional model, which mines the spatio-temporal correlation features of content popularity and make effective predictions on the MEC server graph topology constructed by proximity and semantic relations. Furthermore, we use hierarchical clustering to classify MEC servers into collaborative domains and design a low-complexity heuristic collaborative caching content placement algorithm to minimize the average access delay. Compared with the existing MEC server caching strategy, simulation experiments show that STCC has a better prediction effect of content popularity, and achieves better performance in the two indicators of the cache hit ratio and average access delay.

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

Multi-cell content caching: Optimization for cost and information freshness

In multi-access edge computing (MEC) systems, there are multiple local cache servers caching contents to satisfy the users’ requests, instead of letting the users download via the remote cloud server. In this paper, a multi-cell content scheduling problem (MCSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate local cache servers, and thus the caching decisions in all cells must be jointly optimized. We first prove that MCSP is NP-hard, then we formulate MCSP using integer linear programming, by which the optimal scheduling can be obtained for small-scale instances. For problem solving of large scenarios, via a mathematical reformulation, we derive a scalable optimization algorithm based on repeated column generation. Our performance evaluation shows the effectiveness of the proposed algorithm in comparison to an off-the-shelf commercial solver and a popularity-based caching.

Computation Offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.