Cache Servers Research Articles

Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

Double Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs’ storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs’ caching utilities and MNO’s caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

An Improved Method to Deploy Cache Servers in Software Defined Network-based Information Centric Networking for Big Data

Big data involves a large amount of data generation, storage, transfer from one place to another, and analysis to extract meaningful information. Information centric networking (ICN) is an infrastructure that transfers big data from one node to another node, and provides in-network caches. For software defined network-based ICN approach, a recently proposed centralized cache server architecture deploys single cache server based on path-stretch value. Despite the advantages of centralized cache in ICN, single cache server for a large network has scalability issue. Moreover, it only considers the path-stretch ratio for cache server deployment. Consequently, the traffic can not be reduced optimally. To resolve such issues, we propose to deploy multiple cache servers based on joint optimization of multiple parameters, namely: (i) closeness centrality; (ii) betweenness centrality; (iii) path-stretch values; and (iv) load balancing in the network. Our proposed approach first computes the locations and the number of cache servers based on the network topology information in an offline manner and the cache servers are placed at their corresponding locations in the network. Next, the controller installs flow rules at the switches such that the switches can forward the request for content to one of its nearest cache server. Upon reaching a content request, if the content request matches with the contents stored at the cache server, the content is delivered to the requesting node; otherwise, the request is forwarded to the controller. In the next step, controller computes the path such that the content provider first sends the content to the cache server. Finally, a copy of the content is forwarded to the requesting node. Simulation results confirmed that the proposed approach performs better in terms of traffic overhead and average end-to-end delay as compared to an existing state-of-the-art approach.

Multi-Access Edge Computing Enhanced Video Streaming: Proof-of-Concept Implementation and Prediction/QoE Models

ETSI multi-access edge computing (MEC) provides an IT service environment and cloud-computing capabilities at the edge of the mobile network, enabling application and content providers to deploy new use cases, such as intelligent video acceleration, with low latency and high bandwidth. Specifically, ETSI MEC introduces an MEC server that implements the edge-cloud platform to host partial server-side service logics in the form of MEC applications (MEC Apps). In this paper, we aim to implement the first proof-of-concept (PoC) in the literature for the MEC-enhanced mobile video streaming service. Our PoC consists of Android User Apps, an MEC App, and the YouTube server. The MEC App implements two main functions: popular video caching and radio analytics/video quality adaptation. The User App provides general functions of a YouTube video streaming app and can access the videos from the cache server or the YouTube server under the MEC server's guidance. In addition to the PoC implementation, this paper further develops two machine learning models to be incorporated into the MEC App for popular video prediction and radio channel quality prediction, which allows to consider the effect of non-negligible round-trip times and adjust the video quality more accurately. The experimental results justify that our models, together with other advantages from MEC, can guarantee good performance for the mobile video streaming service. Finally, we model and investigate the effectiveness of the MEC architecture for improving the quality of experience of video-streaming users.

A Novel Fog Computing Based Architecture to Improve the Performance in Content Delivery Networks

Along with the continuing evolution of the Internet and its applications, Content Delivery Networks (CDNs) have become a hot topic with both opportunities and challenges. CDNs were mainly proposed to solve content availability and download time issues by delivering content through edge cache servers deployed around the world. In our previous work, we presented a novel CDN architecture based on a Fog computing environment as a promising solution for real-time applications. In such architecture, we proposed to use a name-based routing protocol following the Information Centric Networking (ICN) approach, with a popularity-based caching strategy to guarantee overall delivery performance. To validate our design principle, we have implemented the proposed Fog-based CDN architecture with its major protocol components and evaluated its performance, as shown through this article. On the one hand, we have extended the Optimized Link-State Routing (OLSR) protocol to be content aware (CA-OLSR), i.e., so that it uses content names as routing labels. Then, we have integrated CA-OLSR with the popularity-based caching strategy, which caches only the most popular content (MPC). On the other hand, we have considered two similar architectures for conducting performance comparative studies. The first is pure Fog-based CDN implemented by the original OLSR (IP-based routing) protocol along with the default caching strategy. The second is a classical cloud-based CDN implemented by the original OLSR. Through extensive simulation experiments, we have shown that our Fog-based CDN architecture outperforms the other compared architectures. CA-OLSR achieves the highest packet delivery ratio (PDR) and the lowest delay for all simulated numbers of connected users. Furthermore, the MPC caching strategy shows higher cache hit rates with fewer numbers of caching operations compared to the existing default caching strategy, which caches all the pass-by content.

Some complexity results for stateful network verification

In modern networks, forwarding of packets often depends on the history of previously transmitted traffic. Such networks contain stateful middleboxes, whose forwarding behaviour depends on a mutable internal state. Firewalls and load balancers are typical examples of stateful middleboxes. This work addresses the complexity of verifying safety properties, such as isolation, in networks with finite-state middleboxes. Unfortunately, we show that even in the absence of forwarding loops, reasoning about such networks is undecidable due to interactions between middleboxes connected by unbounded ordered channels. We therefore abstract away channel ordering. This abstraction is sound for safety, and makes the problem decidable. Specifically, safety checking becomes EXPSPACE-complete in the number of hosts and middleboxes in the network. To tackle the high complexity, we identify two useful subclasses of finite-state middleboxes which admit better complexities. The simplest class includes, e.g., firewalls and permits polynomial-time verification. The second class includes, e.g., cache servers and learning switches, and makes the safety problem coNP-complete. Finally, we implement a tool for verifying the correctness of stateful networks.

User-oriented cache deletion algorithm in a delayed update-tolerant web cache server for supporting a non-formal education

In developing countries, it is often difficult to use web content for e-learning because Internet accessing is unavailable or limited. To support education in such a situation, the authors previously proposed a system that uses a delayed update-tolerant web cache server and a wireless LAN. This system stores e-learning webpages requested by free school children in a cache memory, which is then carried to the free school so that the children can access the webpages in an off-line environment. The present paper proposes a cache deletion algorithm that reflects children’s requests for content retention in the cache server. It is called a user-oriented least recently used (LRU) algorithm. We have developed an experimental system that implemented this algorithm and carried out a learning experiment under conditions that simulated actual usage. We compared the proposed algorithm with existing cache deletion algorithms, including the least recently used (LRU), the least frequently used (LFU) and the least frequently recently used (LFRU) algorithms, in terms of the frequently accessed leaning cache retention rate.

Experience in using public Cloud for Belle II experiment within HNSciCloud Project

The current level of flexibility reached by Cloud serviceproviders enables Physicists to take advantage of extra resources to extend the distributed computing infrastructure supporting High Energy Physics Experiments. However, the discussion about the optimal use of such kind of resources is currently ongoing. Moreover, because each Cloud provider offers its interfaces, API sets, and different service levels, the integration in the computing model of an experiment requires a specific work for each provider. In this paper, we summarize the experience matured in the usage of Cloud res urces offered by different providers within the Europeanproject Helix Nebula Science Cloud for Belle II experiment. The goal of the user-case was to run Montecarlo Production jobs properly on Virtual Machines created dynamically. To do that, we defined the golden image, based on CENTOS; we then set up the basic environment, the internal and external network, and local DNS services. For each cloud, we implemented a cache server based on Squid, while the orchestrator has been entralized on a single server located in the INFN-Napoli infrastructure. Finally, we integrated the available resources in the Belle II production framework based on DIRAC, using VCYCLE as Virtual Machine Life Cycle Manager. After a tuning activity that required the optimization of severalaspects included Image distribution and Network configuration, we wer able to run production jobs over the different Cloud with success.

A data caching model for Tier 2 WLCG computing centres using XCache

As the WLCG continues to consolidate the data storage resources to reduce maintaining costs, more storage elements will be decommissioned or federated in the future. In both cases data streaming would become a common method for data accessing in many LHC Tier 2 and Tier 3 computing centres which makes it vital to optimise the network usage (i.e. latency reduction and bandwidth utilisation). Consequently, the data cache service sits closely to the computing resources becomes a desirable tool. In this paper we discuss how to setup a XCache server at an example WLCG Tier 2 site (Edinburgh) and use the ATLAS analysis queue as a testbed to study the data caching performance. The results could be used to guide the future development to make the caching server more effective.

Content Placement Based on Utility Function for Satellite Networks

The incorporation of the cognitive radio (CR) technology as a spectrum management tool in satellite communication has attracted considerable research attention because the CR allows the coexistence of the primary and secondary networks using the same resources. In this paper, satellites and their users are considered as a primary network, and the terrestrial base stations (BSs) and their users are considered as a secondary network. Besides, it is considered that terrestrial users cause interference to satellite users due to technical and environmental constraints. This interference reduces satellite users' satisfaction with downloads rate. In such a cognitive satellite network environment, content placement may also cause a decrease in user satisfaction. The successful download probability (SDP) is analyzed in this work based on different terrestrial user density. To utilize the satellite cache resources effectively, we propose two transmission strategies based on the terrestrial user density, namely, the multi-point cooperative transmission strategy (CT) and the parallel transmission strategy (PT). In addition, based on the cache service probability we determine the ratio of content distribution most popular content (MPC) and general popular content (GPC) and optimize the content placement process using the utility function in CT mode. Through computer simulation, we give numerical results of our method. The obtained numerical results show that the proposed strategies are effective in content placement.

Using remote cache service for bazel

Save time by sharing and reusing build and test output.

Analysis of the method of optimized data caching in the Content Delivery Network

Concept of Content Delivery Network (CDN) system and methods of caching data on edge servers were considered in this paper. The main task of the CDN network is providing the qualitative information delivery to the end user. Content Delivery Network is a geographically distributed network, that contain a number of content servers and routers. As a rule, it consists of a main node (Origin), and caching nodes (Edges) – points of presence, which can be located in various parts of the world. All content is stored and updated on the Origin server. The structural scheme of organizing the work of the CDN network, consisting of three complementary planes, was presented. Each plane performs special functions and has its own implementation features. Access Plane provides end-user access to content and the interaction of caching servers among themselves and on other planes. The load balancing plane is organized according to the principle of the DNS service and allows to redirect the user's requests to the nearest caching servers. This plane also inter-acts with the metric plane, which contains up-to-date information of the status of all servers involved in delivery and caching of data. As part of this paper, we proposed a method for optimized caching of data in the CDN network. The algorithm of this method is presented. Dependencies that make it possible to evaluate the work of this method in comparison with existing methods are given. The proposed method allows to significantly improve the Hit Ratio, and thus ensure efficient use of cached data and reduce the load on servers of origin.

A Novel Adaptive Caching Mechanism for Video on Demand System Over Wireless Mobile Network

Video on Demand system over the wireless mobile network is a system that provides video services to mobile clients.The main problem with these systems is the high service delay where the mobile clients have to wait to view their favorite movie.The importance of this paper is based on finding a solution on how to reduce the delay time in the VOD system.This paper introduces a novel caching mechanism named Proxy Server Cache mechanism to tackle the issue of service delay.This delay happens when the broadcasting phase that is related to the first segment is missed by a client from the current broadcasting channels. This delay happens when the broadcasting phase that is related to the first segment is missed by a client from the current broadcasting channels. The delayed clients will directly acquire the first segment from the proxy server instead of waiting for the following broadcasting channel pertaining to the first segment. The proposed scheme ensures obtaining the first segment from mobile clients when they arrive. Additionally, the performance of the proposed scheme is validated by applying the VOD system, which can involve the balancing mechanism to retain particular requests through to the local proxy server to provide a fair dissemination for these requests. The obtained result confirms that the proposed scheme reduces the time delay of the system in comparison with the best existing schemes. The results of the average time delay in the Proxy-Cache scheme is 179.2505 milliseconds when 10 clients arrive each minute (Client/minute), the average time delay is 140 milliseconds when the video lengths are 30, 60 and 90. Meanwhile, the failure probability for obtaining the first segment of the video remains zero when the number of arrived requests is set to 2, 4, 6, 8 and 10.

A Novel Adaptive Caching Mechanism for Video on Demand System Over Wireless Mobile Network

Video on Demand (VOD) system over the wireless mobile network is a system that provides video services to mobile clients. The main problem with these systems is the high service delay where the mobile clients have to wait to view their favorite movie. The importance of this paper is based on finding a solution on how to reduce the delay time in the VOD system. This paper introduces a novel caching mechanism named Proxy Server Cache mechanism to tackle the issue of service delay. This delay happens when the broadcasting phase that is related to the first segment is missed by a client from the current broadcasting channels. In this mechanism, the video’s first segment is stored on a server of a stationary proxy type. The delayed clients will directly acquire the first segment from the proxy server instead of waiting for the following broadcasting channel pertaining to the first segment. The proposed scheme ensuresobtaining the first segment from mobile clients when they arrive. Additionally, the performance of the proposed scheme is validated by applying the VOD system, which can involve the balancing mechanism to retain particular requests through to the local proxy server to provide a fair dissemination for these requests. The obtained result confirms that the proposed scheme reduces the time delay of the system in comparison with the best existing schemes. The results of the average time delay in the Proxy-Cache scheme is 179.2505 milliseconds when 10 clients arrive each minute (Client/minute), the average time delay is 140 milliseconds when the video lengths are 30, 60 and 90. Meanwhile, the failure probability for obtaining the first segment of the video remains zero when the number of arrived requests is set to2, 4, 6, 8 and 10.

A Q-learning-based network content caching method

Cloud computing provides users with a distributed computing environment offering on-demand services. As its technologies become gradually mature and its application becomes more universal, cloud computing greatly reduces users’ costs while increasing working efficiency of enterprises and individuals (Futur Gener Comput Syst 25:599–616, 2009). Software as a service (SaaS), as a kind of information servicing model based on cloud platforms, is rising with the developments of Internet technologies and the maturing of application software. The responsibility of a SaaS server is to timely and accurately satisfy users’ needs for information. An intelligent and efficient content caching solution or method plays a vital role in that. This paper proposes a reinforcement learning (RL)-based content caching method named time-based Q Cacher (TQC) which effectively solves the problem of low hit ratio of server caching and ultimately achieves an intelligent, flexible, and highly adaptable content caching model.

Maverick

This paper presents Maverick, a system for discovering exceptional facts about entities in knowledge graphs. Maverick is built upon a beam-search based algorithmic framework which we proposed in a research paper that is published in SIGMOD 2018. In this demonstration proposal, we showcase an end-to-end system that includes a user-facing portal and a cache server. In Maverick, an exceptional fact about an entity of interest is modeled as a context-subspace pair, in which the subspace is a set of attributes and the context is defined by a graph query pattern of which the entity is a match, together with other matching entities. The entity is exceptional among the entities in the context, with regard to the sub-space. The portal allows users to search entities in a knowledge graph and explores exceptional facts about the entities of interest. It presents exceptional facts to users in forms of natural language sentences and illustration charts, for better interpretability of the discovered exceptional facts. The cache server stores intermediate computation results, such as pattern evaluations, exceptionality calculations, and candidate patterns. It is built for sharing computation across entities, such that repetitive computation across entities can be avoided.

Red/LeD: An Asymptotically Optimal and Scalable Online Algorithm for Service Caching at the Edge

Edge servers, which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile Internet applications by moving cloud services to the edge of the network. Due to limited capacity of edge servers and dynamic request arrival, proper service caching at the edge is essential to guarantee good performance. This paper proposes a tractable online algorithm called retrospective download with least-requested deletion that caches services dynamically without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio of our policy, which quantifies the worst case performance in comparison to an optimal offline policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge server. We also show that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. Moreover, we prove that our policy is scalable, in the sense that it only needs doubled capacity to achieve a constant competitive ratio. The utility of our online policy is further evaluated on real-world traces. These trace-based simulations demonstrate that our policy has better, or similar, performance compared with many intelligent offline policies.

Using Remote Cache Service for Bazel

Remote cache service is a new development that significantly saves time in running builds and tests. It is particularly useful for a large code base and any size of development team. Bazel is an actively developed open-source build and test system that aims to increase productivity in software development. It has a growing number of optimizations to improve the performance of daily development tasks.

Collaborative Edge Caching through Service Function Chaining: Architecture and Challenges

Edge caching enables mobile users to fetch contents from cache servers deployed at base stations or even closer to users, with the aim of avoiding latency and improving the overall user experience while reducing backhaul/fronthaul bandwidth use. While sophisticated edge caching policies have been extensively studied in recent works, the practical implementation issues of edge caching are rarely touched. In this article, we first provide an overview of the edge caching policies proposed in the existing literature. Then we present the architecture for edge caching, in which the mobile edge applications can be conveniently provided by chaining the service functions with the aid of edge computing techniques and virtualized resources. Next, we discuss the challenges in implementing the existing caching policies based on the proposed framework. Finally, we build a testbed to evaluate our architecture in a proof of concept and test it with typical caching scenarios. Our experiments show that the proposed framework allows guaranteeing the requirements of the edge caching services.

Data Quality Guarantee for Credible Caching Device Selection in Mobile Crowdsensing Systems

Mobile crowdsensing systems (MCSs) present a flexible and economical alternative to traditional infrastructure-based large-scale sensing through the recruitment of personal mobile devices as data sources. As this becomes a popular sensing approach, it will impact the capacity of typical centralized cellular communication infrastructures widely adopted by MCS applications and any costs accrued. Following the trend toward edge processing, mobile edge caching offloads data and services from the system core to reduce service latency and bandwidth occupation. However, in the MCS case the edge devices are owned by the general public and are therefore more vulnerable to data or calculation manipulation by the user. We now better understand sensor data and user trustworthiness but have no way to determine which devices can also be trusted (i.e., act as a credible caching device). In this article, we treat the quality of sensing data reported by each user as an indication of their possibility of providing credible caching services. Specifically, we conduct a comprehensive study of the data quality problem with regard to cache-enabled MCSs, and develop an incentivization method to encourage users to actively provide high-quality data. That is, quality-aware behavior evaluation is core to the credible caching device selection process. Results of extensive simulations based on realworld data verify the effectiveness of our design. We also highlight several promising research directions that remain open for further elaboration.