Caching Decisions Research Articles

Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing

Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs' offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs' desired program types or local execution delays. Despite the lack of WD's information and the coupling of all the WDs' offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server's computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs' offloading equilibrium, we jointly optimize the BS' pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity uniform pricing heuristics.

On-path caching based on content relevance in Information-Centric Networking

The Internet of things (IoT) is a new network concept. Based on the Internet, the idea of connecting everything is proposed, which is likely to become the development direction of computer and communication in the future. Meanwhile, as a disruptive new communication network model, Information-Centric Network (ICN) has become a hot spot in the field of future network architecture research in recent years. ICN is information-centric, and uses the name of the information to implement data identification, retrieval, and routing and forwarding. The information centric network uses the cache as a built-in structure, and nodes store all the data that flows by default, so that subsequent requests can be responded to as soon as possible. In the actual network, the spread and trend of an information constantly change with time, and the popularity of the information is different in different time periods. However, the existing ICN native caching mechanism ignores the correlation between contents, and the existing relevant research makes insufficient use of content relevance. In this paper may exist a certain correlation between nodes have access to the content of the facts, we design a path cache method based on the correlation content, by discovering target content and the correlation between nodes store content, at the same time considering the node position in the path, make caching decisions, make the cache memory more efficient. The feasibility and effectiveness of the proposed method are verified by simulation experiments under different parameters.

Tigris: A DSL and framework for monitoring software systems at runtime

The understanding of the behavioral aspects of a software system is an essential enabler for many software engineering activities, such as adaptation. This involves collecting runtime data from the system so that it is possible to analyze the collected data to guide actions upon the system. Consequently, software monitoring imposes practical challenges because it is often done by intercepting the system execution and recording gathered information. Such monitoring may degrade the performance and disrupt the system execution to unacceptable levels. In this paper, we introduce a two-phase monitoring approach to support the monitoring step in adaptive systems. The first phase collects lightweight coarse-grained information and identifies relevant parts of the software that should be monitored in detail based on a provided domain-specific language. This language is informed by a systematic literature review. The second phase collects relevant and fine-grained information needed for deciding whether and how to adapt the managed system. Our approach is implemented as a framework, called Tigris, that can be seamlessly integrated into existing software systems to support monitoring-based activities. To validate our proposal, we instantiated Tigris to support an application-level caching approach, which adapts caching decisions of a software system at runtime to improve its performance.

Value‐aware cache replacement in edge networks for Internet of Things

AbstractWith the development of Internet of Things (IoT), massive amounts of data will be brought. By offloading caching from the cloud to the edge, edge caching technology represents a promising solution in the era of IoT to meet the delay requirements of IoT applications. An efficient cache decision and replacement strategy on edge caching devices is a key factor in ensuring the cache hit ratio. The existing cache replacement policies do not comprehensively consider the characteristics of cache files and are likely to result in cache pollution problems. In order to cache data reasonably, to improve the cache hit ratio, and to reduce the user request delay, we propose a concept of file cache value and a file cache value‐aware cache replacement (FCVACR) algorithm of the edge cache system in this article. File cache value is associated with three aspects of cache file: the file size, the file popularity, and the time of requests. The proposed FCVACR algorithm adopts the file cache value method, improves the utilization of edge caching space, and reduces the content transmission delay. Experimental results show that the proposed FCVACR algorithm has a higher cache hit ratio and lower user request delay than the classical cache replacement algorithms.

CCA: Cost-Capacity-Aware Caching for In-Memory Data Analytics Frameworks.

To process data from IoTs and wearable devices, analysis tasks are often offloaded to the cloud. As the amount of sensing data ever increases, optimizing the data analytics frameworks is critical to the performance of processing sensed data. A key approach to speed up the performance of data analytics frameworks in the cloud is caching intermediate data, which is used repeatedly in iterative computations. Existing analytics engines implement caching with various approaches. Some use run-time mechanisms with dynamic profiling and others rely on programmers to decide data to cache. Even though caching discipline has been investigated long enough in computer system research, recent data analytics frameworks still leave a room to optimize. As sophisticated caching should consider complex execution contexts such as cache capacity, size of data to cache, victims to evict, etc., no general solution often exists for data analytics frameworks. In this paper, we propose an application-specific cost-capacity-aware caching scheme for in-memory data analytics frameworks. We use a cost model, built from multiple representative inputs, and an execution flow analysis, extracted from DAG schedule, to select primary candidates to cache among intermediate data. After the caching candidate is determined, the optimal caching is automatically selected during execution even if the programmers no longer manually determine the caching for the intermediate data. We implemented our scheme in Apache Spark and experimentally evaluated our scheme on HiBench benchmarks. Compared to the caching decisions in the original benchmarks, our scheme increases the performance by 27% on sufficient cache memory and by 11% on insufficient cache memory, respectively.

Joint Optimization of Cooperative Edge Caching and Radio Resource Allocation in 5G-Enabled Massive IoT Networks

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

Delay-Aware Caching in Internet-of-Vehicles Networks

With the emergence of a large number of computational resource-intensive applications and various content delivery services, there is an explosion of data growth in the Internet of Vehicles (IoV). To improve the transmission performance of the IoV, caching content on the edge of the network is considered as a potential solution to reduce the content transmission delay. In this article, we investigate the content caching decisions optimization method in the IoV to minimize the content fetching delay for vehicles, which is based on the vehicle-to-vehicle (V2V) collaboration. A delay-aware content caching (DCC) algorithm in the IoV is proposed, which consists of vehicle associations, content caching, and precaching decisions optimization. First, a delay-aware vehicle associations (DVAs) algorithm is proposed to optimize the vehicle associations. Consequently, based on the vehicle associations results, the content caching decisions are optimized in two network scenarios according to the existence of the handover vehicles. Finally, a practical scenario of Shanghai with time-varying traffic flow is used for simulations and the effectiveness of the proposed DCC algorithm is verified.

Dynamic popularity window and distance-based efficient caching for fast content delivery applications in CCN

Content-Centric Networking (CCN) is being established as an emerging Internet architecture that brings efficient data retrieval technology to access the contents with their names. In CCN, the in-network caching capability plays a critical role in the improvement of network performance by reducing the load of content server and increasing the content availability. Thus, the efficiency of the content caching strategy crucially affects the performance of the entire network. Towards this, a novel content caching scheme has been proposed in this article that efficiently utilizes the available caching capacity and improves Quality-of-Service (QoS) during content delivery. To perform optimized caching operations, the proposed scheme considers a dynamic threshold value and takes caching decisions based on two parameters, first is a novel dynamic size popularity window to determine content popularity, and secondly, the distance navigated by the Content message from the preceding on-path router that cached the content copy. For content caching decisions, the dynamic threshold value decreases with an increase in Content message distance from the preceding on-path router (that cached the content) and the content popularity. Using these heuristics, the scheme places the popular contents near the edges of the network. Through extensive simulations, the QoS delivered by the proposed scheme has been examined for different cache sizes on standard Abilene network topology. The results show that the proposed scheme outperforms the existing caching strategies for various performance parameters such as hit-ratio, average network delay, hop count, and average traffic generated in the network. This makes the proposed solution suitable for futuristic Internet architectures with 5G/6G and Industry 4.0 revolutions and their applications where faster content deliveries are required.

A Privacy-Preserving Caching Scheme for Device-to-Device Communications

With device-to-device (D2D) communication, user equipment can share data with each other without the involvement of network infrastructures. In order to maintain the Quality of Service (QoS) and Quality of Experience (QoE) for user applications in D2D communications, most existing schemes use proactive content caching that needs to predict content popularity before making caching decisions which may result in privacy leakage, since the information of users is collected to train a deep learning-based model to predict content popularity. Therefore, it is crucial to guarantee secure data collection in machine learning-based framework. In this paper, we propose a privacy-preserving D2D caching scheme with a passive content caching strategy based on node importance, which can deliver more efficient caching and prevent the potential leakage of user privacy. The scheme is based on softwaredefined networking (SDN), in which the controller is responsible for calculating node importance of devices according to the information of requests and encounters collected by SDN switches. Base station will decide which device can establish reliable and secure communication with content requester based on historical information. The simulation results show that the proposed strategy can outperform other D2D caching strategies in terms of cache hit rate and data rate.

Cooperative Caching Strategy With Content Request Prediction in Internet of Vehicles

In order to mitigate the impact of explosively increasing data traffic on content request services in the Internet of Vehicles (IoV), edge caching technology is implemented in IoV to accelerate the response process of content requests and release the backhaul burden of the base station. However, the content popularity obtained by the traditional content popularity method cannot capture the requests of vehicles accurately due to the time-varying characteristics of the content popularity, which results in a relatively low cache hit ratio. Thus, this article proposes a cooperative caching strategy with content request prediction (CCCRP) in IoV, which precaches the contents requested by vehicles with greater probability in other vehicles or the roadside unit (RSU) to reduce the content acquisition delay. Specifically, vehicles are first clustered using the K-means method to simplify the process of vehicle requesting and content transmission. Then, content requests from vehicles are predicted using the long short-term memory (LSTM) networks according to the historical content request information. Finally, reinforcement learning method is adopted to solve the objective function to obtain the optimal caching decision, which improves the Quality of Service (QoS) of vehicle requests. Simulation results demonstrate that CCCRP can improve the cache hit ratio and reduce content acquisition delay effectively. For example, the cache hit ratio of CCCRP can be increased by 5% and 7% compared to the traditional LFU and LRU caching strategies when the Zipf parameter equals 0.7, respectively.

ICN-Based Enhanced Cooperative Caching for Multimedia Streaming in Resource Constrained Vehicular Environment

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Modified reinforcement learning based- caching system for mobile edge computing

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

Named Data Networking (NDN) has been recognized as a lever to the Internet of Things (IoT). One of the most founding features of NDN is in-network caching to improve data availability and reduce retrieval delays and network load. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this article, we propose an efficient caching technique named PoolCache that augments the effective caching capacity of some defined conglomerates of nodes. This is accomplished by pooling the various caches and manage them in a way to insure zero content redundancy within any defined node conglomerate. The resulting high diversity of cached contents throughout the network tacitly amounts to much better overall performances. We conducted extensive simulations using the CCNsim simulator to evaluate the performance of PoolCache and compare it to that of some well known caching strategies. Simulations using a large Transit Stub topology show that PoolCache clearly outperforms the other caching strategies in terms of a much greater content diversity and consequently a limited number of content evictions, a much better cache hit ratio, and a much lower content retrieval delay. Simulations also showed that PoolCache benefits from any eventual content popularity.

Decentralized Multi-Agent Multi-Armed Bandit Learning With Calibration for Multi-Cell Caching

This paper investigates online decentralized cache strategy design in multi-cell networks without the knowledge of user preference. The goal is to minimize the cumulative transmission delay of multi-cell networks within a finite time interval. Each small base station (SBS) aims to decide on its own cache action autonomously based on its past observations and limited information transmitted from other SBSs without a central controller. To coordinate the cache actions of different SBSs in a decentralized manner, we first propose an ϵ-calibration learning algorithm for each SBS to predict the cache strategy of other SBSs in real-time, which can progressively improve the accuracy in cache action prediction. Then a decentralized multi-agent multi-armed bandit (MAMAB) algorithm is developed for each SBS to decide its own cache strategy based jointly on its past observations and estimated upcoming cache action of other SBSs. This decentralized MAMAB algorithm with ϵ-calibration enables multiple SBSs to converge to a reasonable joint cache action and realize a cooperative cache decision making in a decentralized manner with limited information exchange. Simulation results demonstrate that our proposed decentralized caching algorithm outperforms other decentralized caching algorithms and can rapidly approach towards the centralized caching solutions.

Cache allocation optimisation of user relationship group based on reinforcement learning

In order to alleviate network congestion and reduce request delay, device-to-device caching technology will be an important part of modern communication networks. People always browse the Internet for things they are interested in. However, different people have different points of interest. Therefore, how to choose a suitable cache node when people share content is a challenge. In this study, an optimal cache node selection algorithm based on virtual delay is proposed, where the multi-armed bandit model is used to obtain optimised cache decision based on the interest differences of users. Each candidate user may become a cache node, and the algorithm selects the user who minimises the overall delay as the node. When multiple candidate users exist, it is necessary to cooperate between multiple candidate users whose cache space is limited in order to maximise the cache hit rate. However, since each candidate user acts as a cache node has a defferent service efficiency for different requests from surrounding users, it is necessary to effectively distinguish cooperative cache users. This study proposes a master–slave node cooperative cache model based on the optimal cache node selection. The experimental results confirm that the proposed schemes achieve lower overall delay performance.

A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.

Energy Efficiency and Delay Optimization for Edge Caching Aided Video Streaming

In this paper, we design a computing, communication and caching scheme for edge caching-based video streaming in order to improve the network performance. Firstly, we optimize the system's energy efficiency and delay with the aid of network function virtualization. Then, a dynamic edge caching decision is developed, and based on Lyapunov optimization, an alternating resource optimization algorithm is proposed for allocating the optimal subcarrier and power resources, video caching and computing resources. Our numerical results show that the proposed scheme outperforms both the traditional caching scheme as well as the least frequently used (LFU)-40% regime, and strikes a compelling tradeoff between the energy efficiency and delay.

IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network

This work proposes an innovative infrastructure of secure scenario which operates in a wireless-mobile 6G network for managing big data (BD) on smart buildings (SBs). Count on the rapid growth of telecommunication field new challenges arise. Furthermore, a new type of wireless network infrastructure, the sixth generation (6G), provides all the benefits of its past versions and also improves some issues which its predecessors had. In addition, relative technologies to the telecommunications filed, such as Internet of Things, cloud computing (CC) and edge computing (EC), can operate through a 6G wireless network. Take into account all these, we propose a scenario that try to combine the functions of the Internet of Things with CC, EC and BD in order to achieve a Smart and Secure environment. The major purpose of this work is to create a novel and secure cache decision system (CDS) in a wireless network that operates over an SB, which will offer the users safer and efficient environment for browsing the Internet, sharing and managing large-scale data in the fog. This CDS consisted of two types of servers, one cloud server and one edge server. In order to come up with our proposal, we study related cache scenarios systems which are listed, presented, and compared in this work.

Diversity-improved caching of popular transient contents in Vehicular Named Data Networking

In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up content retrieval in vehicular environments, where a large variety of contents are typically transient, i.e., they expire after a certain amount of time, and may exhibit different popularity profiles. Lifetime and popularity play a crucial role in the content caching decision: intuitively, caching a popular content with long lifetime can be more useful than caching an unpopular one that is ready to expire and then to be dropped from the content store. At the same time, making nearby nodes caching different contents and, therefore, improving the caching diversity, can be crucial to get better delivery performance over the broadcast wireless medium. In this paper, we devise a novel distributed caching strategy where vehicles autonomously decide which content is to be locally cached according to the content residual lifetime, its popularity and the perceived availability of the same content in the neighborhood. The target is to cache with higher probability more popular contents with a longer lifetime, which are not already cached by a nearby node, thus improving the caching diversity in the neighborhood. As a result, vehicles can find the majority of distinct fresh and popular contents nearby, without flooding the network with content requests that have to reach the original source. Performance evaluation shows that the conceived solution outperforms representative benchmark schemes, by guaranteeing, among others, the shortest content retrieval time and the lowest network traffic load.

Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge computing (MEC) framework is proposed. For the purpose of efficiently allocating communication and computation resources to users' computation tasks requests, we propose a long-short-term memory (LSTM) network to predict the task popularity. Based on the predicted task popularity, a long-term reward maximization problem is formulated that involves a joint optimization of the task offloading decisions, computation resource allocation, and caching decisions. To tackle this challenging problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal action in every state. We prove that the BLA based action selection scheme is instantaneously self-correcting and the selected action is an optimal solution for each state. Extensive simulation results demonstrate that: 1) The prediction error of the proposed LSTMs based task popularity prediction decreases with increasing learning rate. 2) The proposed framework significantly outperforms the benchmarks like all local computing, all offloading computing and non-cache computing. 3) The proposed BLA based MAQ-learning achieves an improved performance compared to conventional MAQ-learning algorithm.