Concept Of Cache Research Articles

PackCache: An Online Cost-Driven Data Caching Algorithm in the Cloud

In this paper, we study a data caching problem in the cloud environment, where multiple frequently co-utilised data items could be packed as a single item being transferred to serve a sequence of data requests dynamically with reduced cost. To this end, we propose an online algorithm with respect to a homogeneous cost model, called <i>PackCache</i> , that can leverage the FP-Tree technique to mine those frequently co-utilised data items for packing whereby the incoming requests could be cost-effectively served online by exploiting the concept of anticipatory caching. We show the algorithm is <inline-formula><tex-math notation="LaTeX">$2/\alpha$</tex-math></inline-formula> competitive, reaching the lower bound of the competitive ratio for any deterministic online algorithm on the studied caching problem, and also time and space efficient to serve the requests. Finally, we evaluate the performance of the algorithm via experimental studies to show its actual cost-effectiveness and scalability.

Topical Development Issues the State Automated System of the Russian Federation “Elections”

The article examined the role of the State Automated System of the Russian Federation «Elections» in ensuring the active citizen participation in the election process. We analyzed the prerequisites for the reengineering of the State Automated System of the Russian Federation «Elections». The research determined the main approaches to the construction of models of the information system that ensure the electoral process. The authors analyzed the problems that arise when using the centralized database model. The study demonstrated the importance of providing election commissions located in hard-to-reach settlements with reliable communication channels. Solutions are proposed to reduce the negative consequences of the transition to a centralized database model by using local data caching on the web client side. The mechanism of local caching using the Service Worker API is considered. Various scenarios for using the Service Worker in the context of the electoral process, taking into account the category and importance of the data, were studied and demonstrated. The study analyzed the software employed by election commissions — the Sputnik browser for the possibility of using the proposed concept of local caching in it.

Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.

Power allocation for video segment based caching strategy in F-RAN architecture

The concept of edge network caching has been proposed to alleviate the excessive pressure on the core networks. Furthermore, video segment caching technology, a method to cut the whole video into segments and cache them separately, has brought a novel idea to solve the caching problem in the smaller space for massive data. The adoption of segment caching in edge networks will divide the simple video transmission process into two coupling stages because of separate data caching, which leads to more complicated resource allocation. In this paper, this problem is discussed, and its mathematical model is established to minimize the energy consumption of video transmissions. By introducing an efficient prediction window of channel fading, an optimal dynamic scheduling algorithm based on Q-learning is proposed to minimize power consumption while ensuring smooth video streaming. The proposed Q-learning algorithm is simulated and the impacts of channel state, target video bit rate and large-scale channel parameter are evaluated. Simulation results show that the proposed method can effectively reduce the total power consumption while ensuring the smooth playback of video service, thanks to the fact that the proposed method is intelligent which can effectively utilize idle resources in favorable channel states.

Performance Analysis on Cache-Enabled FR2 IAB Networks

This paper brings the concept of wireless edge caching (WEC) to the realm of integrated access and backhaul (IAB) networks. Traditionally, both access and backhaul are allocated fixed spectrum. However, when WEC is applied to the IAB nodes to pre-fetch some popular files, the backhaul traffic can be drastically reduced. Accordingly, an analytical framework is developed for a cache-enabled IAB network for the wide-band frequency range 2 (FR2) (> 24.25 GHz) channel model. In particular, the optimal spectrum resource allocation policy between access and backhaul for IAB networks is investigated by utilizing WEC. For the considered WEC enabled IAB network, a mathematical framework is presented under the content delivery phase for the calculation of three key performance metrics, namely the average success probability (ASP), throughput, and latency of file delivery with respect to various network parameters such as different caching placement strategies, node density, cache size, antenna number, blockage density, and resource partitioning factor. Extensive numerical results are provided to both verify the theoretical derivations and to demonstrate the precedency of using WEC as a cost-effective measure for the improvement of the IAB network’s performance. In particular, under a normalized cache size of 0.6, compared to the baseline no caching scenario, the percentages of backhaul spectrum that can be saved are 48% (26%), 66% (48%), and 47% (27%) when caching the most popular files (caching uniformly) with respect to the ASP, latency, and throughput of file delivery, respectively. The saved backhaul spectrum can then be shifted to the access for its performance enhancements. Our findings also show that there exists an optimal spectrum partition for IAB with respect to different network parameter settings. Further, there exists a tradeoff in the selection of the optimal partitioning factor with respect to ASP/ throughput and latency of file delivery for varying IAB node densities.

Multi-Agent Deep Reinforcement Learning-Based Cooperative Edge Caching for Ultra-Dense Next-Generation Networks

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultra-dense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decision process (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches.

A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.

Improved Merkle Hash Tree-Based One-Time Signature Scheme for Capability-Enhanced Security Enforcing Architecture for Named Data Networking

The concept of network caching is determined to be the potential requirement of named data networks (NDN) for enhancing the capabilities of the traditional IP networking. It is responsible for location independent data accesses and optimal bandwidth utilization in multi-path data dissemination. However, the network caching process in NDN introduces security challenges such as content cache poisoning, malicious injection or flooding of the packets and violation in accessing content packets. In this paper, an Improved Merkle Hash Tree-based one-time signature scheme for capability-enhanced security enforcing architecture (IMHT-OTSS-CSEA) is proposed for provisioning data authenticity in a distributed manner for leveraging the capabilities to inform the access privileges of the packets during the process of data dissemination. It is proposed for permitting the routers to verify the forwarded packets’ authenticity in NDN. It is capable in handling the issues that emerge from unsolicited packets during a flooding-based denial of service attacks by supporting the indispensable verification process in routers that confirms the timeliness of packets. The simulation experiments conducted using the open source CCNs platform and Planetlab confirmed a significant mean reduction in delay of 14.61%, superior to the benchmarked schemes. It is identified to minimize the delay incurred in generating bit vectors by a average margin of 13.06%, excellent to the baseline approaches. It also confirmed a mean increase in the true positive rate of 5.42%, a mean increase in the precision rate of 6.04%, decrease in false positive rate of 6.82% and increase in F-measure of 5.62% compared to the baseline approaches in the context of detecting content cache pollution attack respectively.

Energy-aware caching and collaboration for green communication systems

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks.

A Novel Task Caching and Migration Strategy in Multi-Access Edge Computing Based on the Genetic Algorithm

Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment.

Algorithmic cache of sorted tables for feature selection

Feature selection is a mechanism used in Machine Learning to reduce the complexity and improve the speed of the learning process by using a subset of features from the data set. There are several measures which are used to assign a score to a subset of features and, therefore, are able to compare them and decide which one is the best. The bottle neck of consistence measures is having the information of the different examples available to check their class by groups. To handle it, this paper proposes the concept of an algorithmic cache, which stores sorted tables to speed up the access to example information. The work carries out an empirical study using 34 real-world data sets and four representative search strategies combined with different table caching strategies and three sorting methods. The experiments calculate four different consistency and one information measures, showing that the proposed sorted tables cache reduces computation time and it is competitive with hash table structures.

Advancing throughput of HEP analysis work-flows using caching concepts

High throughput and short turnaround cycles are core requirements for efficient processing of data-intense end-user analyses in High Energy Physics (HEP). Together with the tremendously increasing amount of data to be processed, this leads to enormous challenges for HEP storage systems, networks and the data distribution to computing resources for end-user analyses. Bringing data close to the computing resource is a very promising approach to solve throughput limitations and improve the overall performance. However, achieving data locality by placing multiple conventional caches inside a distributed computing infrastructure leads to redundant data placement and inefficient usage of the limited cache volume. The solution is a coordinated placement of critical data on computing resources, which enables matching each process of an analysis work-flow to its most suitable worker node in terms of data locality and, thus, reduces the overall processing time. This coordinated distributed caching concept was realized at KIT by developing the coordination service NaviX that connects an XRootD cache proxy infrastructure with an HTCondor batch system. We give an overview about the coordinated distributed caching concept and experiences collected on prototype system based on NaviX.

Synergy between Communication, Computing, and Caching for Smart Sensing in Internet of Things

Internet of Things (IoT) is composed of a large number of devices that are connected to the Internet and can exchange information regarding different applications including healthcare, transportation, and agriculture. In IoT, the information and communication technology refers to the involvement of computing to reduce the communication costs for information sharing along with managing the storage capabilities. It becomes more interesting and challenging by involving the concept of caching for optimization that can also be perfectly mapped to improve sensing capabilities. This paper presents the impact of Computing, Communication, and Caching (CCC) over the smart sensing capabilities to highlight the possible opportunities for this convergence in recent enabling technologies including 5G, cloud, fog and edge computing based networks. In this regard, different technologies have been identified that help solving bottlenecks during smart sensing operations by a large number of physical devices in IoT. We have investigated that how the convergence of CCC can improve the smart IoT sensing. A number of requirements for smart IoT based sensing are identified. Moreover, new challenges are identified that cause limited services for IoT applications due to sensing limitation of current and future convergence in IoT enabling technologies.

Device-to-Device (D2D) Communication as a Bootstrapping System in a Wireless Cellular Network

Device-to-Device (D2D) communication is considered to be a promising form of technology to distribute video contents to nearby users and devices at a high data rate and with few delays. In this paper, we propose utilizing the D2D services and cache of mobile devices as a bootstrapping system in a cellular network. The main objective of a bootstrapping-D2D (BD2D) system is to improve the quality of service in terms of achieving the minimum startup-time of popular video files in a cellular network. To achieve our objective, we propose a new caching concept for D2D communication in which each mobile device can cache and share the initial video portion or initial segments of the popular video files to other proximate users via a D2D communication link. We formulate an optimization problem that maximizes the cache-hit ratio of initial segments of the most popular video contents. The basic aim is to move the initial segments as close as possible to the wireless users. We also propose a network-assisted D2D architecture called mobile content delivery network-assisted-BD2D and algorithms that assist the BD2D system in the discovery of devices that have cached the initial segments of the requested video files, in cache placement decisions, and also in the registration of potential D2D devices at the core network. We evaluate the performance of the BD2D system and propose algorithms through extensive simulations under real wireless network conditions, such as interference, fading, and shadowing. Our concluding simulation results demonstrate that bootstrapping the initial segments using the short range BD2D system can significantly reduce the average startup-time of video files in comparison with bootstrapping the initial segments from the traditional cellular system.

Concept of Prefetching and Caching in Web Usage Mining

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Energy Efficient Task Caching and Offloading for Mobile Edge Computing

While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

Milking the Cache Cow With Fairness in Mind

Information-centric networking (ICN) is a popular research topic. At its heart is the concept of in-network caching. Various algorithms have been proposed for optimizing ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed altruistic nodes that will sacrifice their own performance for the global optimum. We argue that this assumption is insufficient and oversimplifies the reality. We address this problem by modeling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that consider both performance and fairness. We argue that only algorithms that are fair to all parties involved in caching will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

Cooperative Caching for Efficient Data Search in Mobile P2P Networks

In this paper, we propose a cooperative caching scheme for structured data via clusters based on peer connectivity in mobile P2P networks. In the proposed scheme, a cluster is organized for cache sharing among mobile peers with long-term connectivity, and metadata are disseminated to neighbor peers for efficient data search performance. It reduces data duplication and uses cache space efficiently through integrative cache management of peers inside the cluster. The proposed scheme reduces data replacement time in the event of changes in topology or cache data replacement using the concept of temp cache. It performs data recovery and cluster adjustment through cluster management in the event of an abrupt disconnection of a peer. In the scheme, metadata of popular data are disseminated to neighbor peers for efficient data searching. In a data search, queries are processed in the order of local cache, metadata, the cluster to which it belongs, and neighbor clusters, in accordance with cooperative caching strategy. Performance evaluation results show that the proposed scheme has a higher cache hit ratio, and lower cost for data replacement and query processing than existing schemes.

Cooperative caching for multimedia data in mobile P2P networks

In this paper, we propose a cooperative caching scheme for multimedia data via clusters based on peers connectivity in mobile P2P networks. In the proposed scheme, a cluster is organized for cache sharing among mobile peers with long-term connectivity, and metadata are disseminated to neighbor peers for efficient multimedia data search performance. It reduces data duplication and uses cache space efficiently through integrative cache management of peers inside the cluster. The proposed scheme reduces data replacement time in the event of changes in topology or cache data replacement using the concept of temporal cache. It performs data recovery and cluster adjustment through cluster management in the event of an abrupt disconnection of a peer. In this scheme, metadata of popular multimedia data are disseminated to neighbor peers for efficient data searching. In a data search, queries are processed in the order of local cache, metadata, the cluster to which it belongs, and neighbor clusters, in accordance with cooperative caching strategy. Performance evaluation results show that the proposed scheme has a higher cache hit ratio, and lower cost for data replacement and query processing than existing schemes.

Mobility-Aware Coded Probabilistic Caching Scheme for MEC-Enabled Small Cell Networks

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.