Conventional Caching Schemes Research Articles

Cache Update and Delivery of Dynamic Contents: A Stochastic Game Approach

In this paper, we propose a cache update policy for wireless networks considering dynamic popularity for file requests. Our network scenario is a wireless network comprised of some cache-equipped access points (APs) which are deployed densely in an area and have connections with core network servers. We model the dynamics of file requests as Markov-modulated Poisson processes. Considering the congestion-dependent delay of APs' services and their overlapping coverage areas, we model the cache service for the cache-missed requests, as a stochastic game in which a Vickrey–Clarke–Groves (VCG) mechanism is exploited at each stage of the game as our proposed file routing policy, and the APs decide independently how to update their caches regarding the routed files. To this end, the APs' utilities are defined based on long-term average file delivery delay including the queueing delay in APs. By finding the Nash equilibrium of the game, we propose policies for both delivery and cache update of dynamic contents. Finally, by comparing numerical results of our proposed scheme with some conventional caching schemes, we show significant improvements in network performance in terms of file delivery delay in different conditions.

Optimal caching scheme in D2D networks with multiple robot helpers

Mobile robots are playing an important role in modern industries. The deployment of robots which act as mobile helpers in a wireless network is rarely considered in the existing studies of the device-to-device (D2D) caching schemes. In this paper, we investigate the optimal caching scheme for D2D networks with multiple robot helpers with large cache size. An improved caching scheme named robot helper aided caching (RHAC) scheme is proposed to optimize the system performance by moving the robot helpers to the optimal positions. The optimal locations of the robot helpers can be found based on partitioned adaptive particle swarm optimization (PAPSO) algorithm. And based on these two algorithms, we propose a mobility-aware optimization strategy for the robot helpers. The simulation results demonstrate that compared with other conventional caching schemes, the proposed RHAC scheme can bring significant performance improvements in terms of hitting probability, cost, delay and energy consumption. Furthermore, the location distribution and mobility of the robot helpers are studied, which provides a reference for introducing robot helpers into different scenarios such as smart factories.

Coded Caching for Time-Varying Files Popularities and Asynchronous Delivery

Data communication has seen exponential growth recently, and it currently dominates wireless communication. As a result, proactive caching was developed to minimize peak traffic rates by storing content, in advance, at different nodes in the network. We consider proactive caching for a broadcast wireless network with one central hub such as a satellite (ST) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> associated mobile units (MUs) such as mobile mini-ground stations or end users. The ST has a library of files, and the MUs demands are assumed to be limited to this library, while the popularity of the library files changes over time. We assume that the MUs demands arrive at different times, and hence, asynchronous file delivery is necessary. We propose a new scheme that minimizes the files delivery sum rate and show that we can use the file delivery messages to proactively and constantly update the MU finite caches. We show that this mechanism reduces the downloaded traffic of the network. The proposed scheme uses index coding to jointly encode the delivery of different demanded files with the cache updates to other MUs to follow the changes in the files popularities. An offline optimization of the delivery sum rate of the scheme is proposed, where it requires knowledge of the files popularities across the whole transmission period. In particular, the problem is formulated as a linear program and the optimal caching is obtained numerically. Moreover closed form solutions to two special cases are derived and a lowerbound to the achievable delivery sum rate is developed. Numerical results show the benefits of the proposed scheme over conventional caching schemes, in terms of reducing the delivery sum rate.

Optimized Caching and Spectrum Partitioning for D2D Enabled Cellular Systems With Clustered Devices

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

Energy-Efficient Mobility-Aware Caching Algorithms for Clustered Small Cells in Ultra-Dense Networks

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a 1/2k-approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes.

Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing.

The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination.

Geo-cascading and community-cascading in social networks: Comparative analysis and its implications to edge caching

The proliferation of social networking service (SNS) enables to share information through social links. Due to the characteristics of information and social properties, the diffused information appears various shapes. It may either be shaped as social communities or geographical regions. In order to investigate different information diffusion behaviors, we study over 54,000 pieces of diffused information in Twitter data set. We propose both macro and micro approaches to distinguish different information diffusion behaviors, either by geo-cascading or community-cascading. We then study the characteristics of information cascading behaviors, which come out with a more sophisticated diffusion network pattern in community-cascading diffusion networks. Furthermore, we conduct the experiments to show the impact of different kinds of information diffusion behaviors in the caching behaviors for mobile edge computing. It turns out that the caching scheme combining both social and geographical factors outperforms the conventional caching schemes.

Content Placement for Wireless Cooperative Caching Helpers: A Tradeoff Between Cooperative Gain and Content Diversity Gain

Depending on what and how caching helpers cache content in their finite storage, the caching helpers can offer either a content diversity gain by serving diverse content or a cooperative gain by jointly transmitting the same content. This paper identifies a tradeoff between the content diversity gain and the cooperative gain according to content placements and proposes a probabilistic content placement to optimally balance the tradeoff. Using stochastic geometry, we quantify this tradeoff by deriving the cache hit rate and the rate coverage probability. To efficiently control the tradeoff, we determine the near-optimal caching probabilities that maximize the average content delivery success probability with the cooperative caching helpers. Our analysis and numerical results reveal that our proposed content placement outperforms the conventional caching schemes, such as caching with uniform probabilities, caching the most popular contents, and caching the content maximizing the cache hit, in terms of the average content delivery success probability.

How Much Cache is Needed to Achieve Linear Capacity Scaling in Backhaul-Limited Dense Wireless Networks?

Dense wireless networks are a promising solution to meet the huge capacity demand in 5G wireless systems. However, there are two implementation issues, namely the interference and backhaul issues. To resolve these issues, we propose a novel network architecture called the backhaul-limited cached dense wireless network (C-DWN), where a physical layer (PHY) caching scheme is employed at the base stations (BSs) but only a fraction of the BSs have wired payload backhauls. The PHY caching can replace the role of wired backhauls to achieve both the cache-induced MIMO cooperation gain and cache-assisted Multihopping gain. Two fundamental questions are addressed. Can we exploit the PHY caching to achieve linear capacity scaling with limited payload backhauls? If so, how much cache is needed? We show that the capacity of the backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS cache size is larger than a threshold that depends on the content popularity. We also quantify the throughput gain due to cache-induced MIMO cooperation over conventional caching schemes (which exploit purely the cached-assisted multihopping). Interestingly, the minimum BS cache size needed to achieve a significant cache-induced MIMO cooperation gain is the same as that needed to achieve the linear capacity scaling.

PHY-caching in 5G wireless networks: design and analysis

In this article, we propose a PHY-caching scheme for 5G wireless networks to achieve spectral efficiency gain over conventional caching schemes, which do not induce physical layer cooperation. By properly caching some popular contents at the BSs, the proposed PHY-caching can opportunistically transform the topology of the RAN from an unfavorable topology (e.g., relay or interference topology) into a more favorable MIMO broadcast topology and enjoy spectral efficiency gain. Specifically, we first introduce a generic cache model and cache-assisted PHY transmission, and show that PHY-caching can significantly enhance the spectral efficiency of the wireless network by inducing dynamic side information at the BSs. Then we discuss the design challenges and solutions of PHY-caching. We introduce maximum-distance-separable-coded caching and online cache content placement design as a potential solution. As a case study, we analyze the performance trade-off between PHY-caching at the BS and CN-caching at the gateway under capacity-limited CN-fronthaul. We show that even though PHY-caching covers fewer users than CN-caching and results in a lower cache hit rate, it is still efficient to do PHY-caching at the BS.

Approximation Algorithms for Mobile Data Caching in Small Cell Networks

Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations (SBSs) has been proposed to decrease the costly transmissions from the macrocell base stations without requiring high capacity backhaul links for connecting the SBSs with the core network. However, the caching policy design is a challenging problem especially if one considers realistic parameters such as the bandwidth capacity constraints of the SBSs that can be reached in congested urban areas. We consider such a scenario and formulate the joint routing and caching problem aiming to maximize the fraction of content requests served locally by the deployed SBSs. This is an NP-hard problem and, hence, we cannot obtain an optimal solution. Thus, we present a novel reduction to a variant of the facility location problem, which allows us to exploit the rich literature of it, to establish algorithms with approximation guarantees for our problem. Although the reduction does not ensure tight enough bounds in general, extensive numerical results reveal a near-optimal performance that is even up to 38% better compared to conventional caching schemes using realistic system settings.

Fundamental Limits of Caching

Caching is a technique to reduce peak traffic rates by prefetching popular content into memories at the end users. Conventionally, these memories are used to deliver requested content in part from a locally cached copy rather than through the network. The gain offered by this approach, which we term local caching gain, depends on the local cache size (i.e., the memory available at each individual user). In this paper, we introduce and exploit a second, global, caching gain not utilized by conventional caching schemes. This gain depends on the aggregate global cache size (i.e., the cumulative memory available at all users), even though there is no cooperation among the users. To evaluate and isolate these two gains, we introduce an information-theoretic formulation of the caching problem focusing on its basic structure. For this setting, we propose a novel coded caching scheme that exploits both local and global caching gains, leading to a multiplicative improvement in the peak rate compared with previously known schemes. In particular, the improvement can be on the order of the number of users in the network. In addition, we argue that the performance of the proposed scheme is within a constant factor of the information-theoretic optimum for all values of the problem parameters.

Watching user generated videos with prefetching

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by performing a PlanetLab-based measurement demonstrating that video playback on YouTube is often unsatisfactory and introduce a series of prefetching schemes: (1) the conventional caching scheme, which caches all the videos that users have watched, (2) the search result-based prefetching scheme, which prefetches videos that are in the search results of users' search queries, and (3) the recommendation-aware prefetching scheme, which prefetches videos that are in the recommendation lists of the videos that users watch. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio of up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates strong potential for improving the playback quality at the client. In addition, we explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

GPU-accelerated 3D Bayesian image reconstruction from Compton scattered data

This paper describes the development of fast Bayesian reconstruction methods for Compton cameras using commodity graphics hardware. For fast iterative reconstruction, not only is it important to increase the convergence rate, but also it is equally important to accelerate the computation of time-consuming and repeated operations, such as projection and backprojection. Since the size of the system matrix for a typical Compton camera is intractably large, it is impractical to use a conventional caching scheme that stores the pre-calculated elements of a system matrix and uses them for the calculation of projection and backprojection. In this paper we propose GPU (graphics processing unit)-accelerated methods that can rapidly perform conical projection and backprojection on the fly. Since the conventional ray-based backprojection method is inefficient for parallel computing on GPUs, we develop voxel-based conical backprojection methods using two different approximation schemes. In the first scheme, we approximate the intersecting chord length of the ray passing through a voxel by the perpendicular distance from the center to the ray. In the second scheme, each voxel is regarded as a dimensionless point rather than a cube so that the backprojection can be performed without the need for calculating intersecting chord lengths or their approximations. Our simulation studies show that the GPU-based method dramatically improves the computational speed with only minor loss of accuracy in reconstruction. With the development of high-resolution detectors, the difference in the reconstruction accuracy between the GPU-based method and the CPU-based method will eventually be negligible.

Proxy Caching for Video-on-Demand Using Flexible Starting Point Selection

In this paper, we propose a novel proxy caching scheme for video-on-demand (VoD) services. Our approach is based on the observation that streaming video users searching for some specific content or scene pay most attention to the initial delay, while a small shift of the starting point is acceptable. We present results from subjective VoD tests that relate waiting time and starting point deviation to user satisfaction. Based on this relationship as well as the dynamically changing popularity of video segments, we propose an efficient segment-based caching algorithm, which maximizes the user satisfaction by trading off between the initial delay and the deviation of starting point. Our caching scheme supports interactive video cassette recorder (VCR) functionalities and enables cache replacement with a much finer granularity compared to previously proposed segment-based approaches. Our experimental results show a significantly improved user satisfaction for our scheme compared to conventional caching schemes.

GroCoca: group-based peer-to-peer cooperative caching in mobile environment

In a mobile cooperative caching environment, we observe the need for cooperating peers to cache useful data items together, so as to improve cache hit from peers. This could be achieved by capturing the data requirement of individual peers in conjunction with their mobility pattern, for which we realized via a GROup-based COoperative CAching scheme (GroCoca). In GroCoca, we define a tightly-coupled group (TCG) as a collection of peers that possess similar mobility pattern and display similar data affinity. A family of algorithms is proposed to discover and maintain all TCGs dynamically. Furthermore, two cooperative cache management protocols, namely, cooperative cache admission control and replacement, are designed to control data replicas and improve data accessibility in TCGs. A cache signature scheme is also adopted in GroCoca in order to provide information for the mobile clients to determine whether their TCG members are likely caching their desired data items and to perform cooperative cache replacement Experimental results show that GroCoca outperforms the conventional caching scheme and standard COoperative CAching scheme (COCA) in terms of access latency and global cache hit ratio. However, GroCoca generally incurs higher power consumption.

Aligned prefix caching based on singleton information

In recent years, the lookup speed of Content Address Memory (CAM), a hardware solution for IP caching, is greatly improved. CAM is a good candidate for lightweight routing-lookup modules on input line-cards in parallelism router architecture. In this paper, we propose aligned-ancestor poisoning (AAP) and aligned-prefix caching (APC) to enhance IP caching. In particular, AAP is a marking scheme for tree-based routing tables. Routing-lookup results thus carry additional singleton information, which indicates whether predefined aligned prefixes are cacheable or not. APC is a caching scheme to utilize such routing-lookup results. To properly configure AAP and APC, we further investigate the properties of referred singleton prefixes. Our trace-driven simulations show that the required cache size can be significantly reduced, compared with the conventional destination caching scheme.

Studies on striping and buffer caching issues for the software RAID file system

A software RAID file system is defined as a system that distributes data redundantly across an array of disks attached to each of the workstations connected on a high-speed network. This configuration provides higher throughput and availability compared to conventional file systems. In this paper, we consider two specific issues regarding the distribution of data among the cluster, namely, striping and buffer caching for such an environment. Through simulation studies we compare the performance of various striping methods and show that for effective striping in software RAID file systems, it must take advantage of its flexible nature. Further, for buffer caching, we show that conventional caching schemes developed for distributed systems are insufficient, and that the Exclusively Old Data and Parity scheme that is presented in this paper, overcomes the limitations of the previously proposed schemes.