Cached Copies Research Articles

Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network

The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of traffic and a significant amount of network latency. This brings great challenges to 5G-ICN mobile cache. It appears urgent to improve the cache placement strategy. This paper suggests a hybrid caching strategy called time segmentation-based hybrid caching (TSBC) strategy, based on the 5G-ICN bearer network infrastructure. A base station’s access frequency can change throughout the course of the day due to the “tidal phenomena” of mobile networks. To distinguish the access frequency, we split each day into periods of high and low liquidity. To maintain the diversity of cache copies during periods of high liquidity, we replace the path’s least-used cache copy. We determine the cache value of each node in the path and make caching decisions during periods of low liquidity to make sure users can access the content they are most interested in quickly. The simulation results demonstrate that the proposed strategy has a positive impact on both latency and the cache hit ratio.

Drone Helps Privacy: Sky Caching Assisted $k$-Anonymity in Spatial Querying

As wireless and mobile devices are prevalent and resource-rich, users can frequently query point-of-interests and enjoy diverse location-based services (LBSs) in infrastructure-based wireless networks. However, users often trade their location privacy with services by sharing their location information with an LBS server, without knowing that the server can fully be trusted. In this article, we propose a sky caching-aided spatial querying scheme with the support of a flying drone. The basic idea is that both the user and its drone collaboratively generate a set of dummy locations to hide the current location of the user from the server. We also propose cache admission control techniques to efficiently cache the queried results to minimize the number of duplicated cached copies. The location privacy is analyzed and measured in terms of the location obfuscation and the size of the convex hull area that consists of dummy locations resided within the cloaking areas of user and drone. We conduct extensive simulation experiments using the OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed approach can reduce the number of queries sent to the server, extend the cloaking area, and improve the location privacy of users significantly.

Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications

Fog-radio access networks (F-RANs) alleviate fronthaul delays for cellular networks as compared to their cloud counterparts. This allows them to be suitable solutions for networks that demand low propagation delays. Namely, they are suitable for millimeter wave (mmWave) operations that suffer from short propagation distances and possess a poor scattering environment (low channel ranks). The F-RAN here is comprised of fog nodes that are collocated with radio remote heads (RRHs) to provide local processing capabilities for mobile station (MS) terminals. These terminals demand various network functions (NFs) that correspond to different service requests. Now, provisioning these NFs on the fog nodes also yields service delays due to the requirement for service migration from the cloud, i.e., offloading to the fog nodes. One solution to reduce this service delay is to provide cached copies of popular NFs in advance. Hence, it is critical to study function popularity and allow for content caching at the F-RAN. This is further a necessity given the limited resources at the fog nodes, thus requiring efficient resource management to enhance network capacity at reduced power and cost penalty. This paper proposes novel solutions that allocate popular NFs on the fog nodes to accelerate services for the terminals, namely, the clustered and distributed caching methods. The two methods are analyzed and compared against the baseline uncached provisioning schemes in terms of service delay, energy consumption, and cost.

Time Dependent Popularity Caching Scheme for NDN Based MANETs

Named data networking (NDN) approach has natural benefits within Mobile Ad hoc Network (MANET) but presents different issues as well. Space for cache, energy, and mobility of devices in a MANET is limited; therefore, we need for an enhanced judgement concerning which data to be store and where to be cache. A Time dependent Popularity Caching Scheme (TDPC) has suggested which selects nodes for caching the content on the forwarding path of packet and chooses the contents which have cached constructed on their time dependent popularity. At this interval, the cache distribution of the content and the storage capability of the devices are also measured. Results of the suggested TDPC approach are evaluated by using the simulator ndnSIM which is beached on Network Simulator 3 (NS-3). Simulation outcomes show that TDPC has good performance in expression of cache hit ratio, content retrieval interval, total cache copies and compared to the Dynamic Caching Strategy for CCN-based MANETs (CSCM). The goal of TDPC is to reduce cache redundancy, retrieval time of content and total number of cache copies.

Cooperative Caching in Vehicular Content Centric Network Based on Social Attributes and Mobility

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. To increase the network performance, CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher cache hit ratio and lower content access delay compared to other state-of-the-art schemes.

Speeding Up VM Startup by Cooperative VM Image Caching

Virtual machine (VM) management is at the core of virtualized cloud data centers. Among others, how to reduce the startup delay of VMs is a key issue for improving user experience and resource utility. In this paper, we study this issue by jointly considering VM placement and VM image caching. We formulate the joint placement problem and design several joint algorithms, including both online and offline algorithms, to speed up VM startup. In our design, we adopt the cooperative caching approach, where image cache copies are shared among physical machines (PMs) so as to reduce image retrieval time. The key point of our algorithms lies in how to appropriately place VM image cache among PMs so as to speed up VM startup as much as possible. The proposed algorithms are evaluated by extensive simulations via SimGrid. The results show that our algorithms can achieve shorter startup delay in most cases, compared with existing ones.

Profitable Cooperative Region for Distributed Online Edge Caching

Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither a-priori knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.

Online Caching and Cooperative Forwarding in Information Centric Networking

Information centric networking is one of the most promising future Internet architectures to tackle the increasing network traffic by enabling in-network caching to cache popular contents. Although in-network caching reduces the network traffic by providing requested content locally to the users, several challenging issues are still unsolved. For example, identical contents are replicated in all routers along each request’s forwarding path, which incurs faster cache replacement and degrades cache utilization, and temporally cached content’s locations are not easy to track or search in the network. Besides, it is tough to correctly predict future popularity of contents and decide which contents to store. Hence, in this paper, an online caching and cooperative forwarding scheme is proposed to enhance cache utilization and to reduce network delay, as well as reduce the workload on each router. The caching problem is formulated as a Ski-Rental problem, which is a classical method for online decision making, in combination with consistent-hashing to obtain an online coordinated caching solution. The proposed request forwarding scheme is based on consistent-hashing, where every router knows the potential location of the cached copy of the requested content and thereby avoiding the unnecessary forwarding. Finally, the proposed request forwarding and caching schemes were validated by a chunk-level simulator. The simulation results show that the proposed scheme outperforms the existing algorithms in terms of content hit rate, server load, the processing load on routers, and access delay.

Reducing Write Amplification of Flash Storage through Cooperative Data Management with NVM

Write amplification is a critical factor that limits the stable performance of flash-based storage systems. To reduce write amplification, this article presents a new technique that cooperatively manages data in flash storage and nonvolatile memory (NVM). Our scheme basically considers NVM as the cache of flash storage, but allows the original data in flash storage to be invalidated if there is a cached copy in NVM, which can temporarily serve as the original data. This scheme eliminates the copy-out operation for a substantial number of cached data, thereby enhancing garbage collection efficiency. Simulated results show that the proposed scheme reduces the copy-out overhead of garbage collection by 51.4% and decreases the standard deviation of response time by 35.4% on average. Measurement results obtained by implementing the proposed scheme in BlueDBM, 1 an open-source flash development platform developed by MIT, show that the proposed scheme reduces the execution time and increases IOPS by 2--21% and 3--18%, respectively, for the workloads that we considered. This article is an extended version of Lee et al. [2016], which was presented at the 32nd International Conference on Massive Data Storage Systems and Technology in 2016.

Content Look-Aside Buffer for Redundancy-Free Virtual Disk I/O and Caching

Storage consolidation in a virtualized environment introduces numerous duplications in virtual disks and imposes considerable pressure on disk I/O and caching. In this paper, we present a content look-aside buffer (CLB) approach for simultaneously providing redundancy-free virtual disk I/O and caching. CLB attaches persistent fingerprints to virtual disk blocks, which enables detection of I/O redundancy before disk access. At run time, CLB exploits content pages already present in the guest disk caches to service the redundant reads through page sharing, thus eliminating both redundant I/O requests and redundant disk cache copies. For write requests, CLB uses a group invalidating writeback protocol for updating fingerprints to support crash consistency while minimizing disk write overhead. By implementing and evaluating a CLB prototype on KVM hypervisor, we demonstrate that CLB delivers considerably improved I/O performance with realistic workloads. Our CLB prototype improves the throughput of sequential and random read on duplicate data by 4.1x and 26.2x, respectively. For typical read-intensive workloads, such as booting VM and launching application, CLB's I/O deduplication and cache deduplication eliminates 94.9%--98.5% of read requests and saves 50%--100% cache memory in each VM, respectively. Compared with the QEMU's raw virtual disk format, CLB improves the per-disk VM density by 8x--16x. For mixed read-write workloads, the cost of on-line fingerprint updating offsets the read benefit; nevertheless, CLB substantially improves overall performance.

A cellular automata based self-correcting protocol processor for scalable CMPs

This work reports an effective design of protocol processor (PP), the key component of coherence controller, in a Chip Multiprocessors (CMPs) cache system. It caters to the need for determining the state of a data block in processors' private caches with high accuracy. An insignificant defect in the PP can introduce major inconsistencies in computing the states of cached copies of a data block. A realizable PP, therefore, is a necessity for accurate computation of cache line states. The proposed design is the outcome of radical change in the design approach of PP. The modeling tool of cellular automata (CA) is considered for the design. The special class of cellular automata with single length cycle attractors (SLCA) is tuned to replicate the PP for computing cache line states in CMPs. Theory has been developed to empower the SLCA based PP to sense its malfunctioning and that leads to the design of a self-correcting PP.

Theoretical analysis on caching effects in urban vehicular ad hoc networks

AbstractMost applications in urban vehicular ad hoc networks (VANETs) rely on information sharing, such as real‐time traffic information queries, and advertisements. However, existing data dissemination techniques cannot guarantee satisfactory performance when amounts of information requests come from all around the network. Because these pieces of information are useful for multiple users located in various positions, it is beneficial to spread the cached copies around. Existing work proposed caching mechanisms and conducted simulations for validation, but there is a lack of theoretical analysis on the explicit caching effects. Because of the complex urban environment and high mobility of vehicles, quantifying the caching effects on the VANET performance is quite challenging. We present the cache coverage ratio as the metric to measure the caching effects, and theoretical analysis is given based on reasonable assumptions for urban VANETs, through which we find the affecting factors include vehicle density, transmission range, and ratio of caching vehicles. We deduce the quantitative relationship among them, which have similar forms as the cumulative density function of an exponential distribution. We also consider the impact of vehicle mobility to predict the future cache effect on surrounding roads of the caching area. We conduct intensive simulations, which verify that the theoretical analysis results match quite well with the simulated reality under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Towards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage

Load balance just reasonably allocate request from users to each edge cloud storage servers. It can avoid over- loading or congestion to ensure data transmission efficiency. Referring to idea of ant colony algorithm, the paper brought out Content Delivery Load Balance Algorithm Based on Probability Matching (CDLBA-PM) algorithm. The method cal- culated access probability of each edge cloud storage peer and selected edge cloud storage peer with probability matching algorithm comprehensively considering about history access costs and real-time access cost of edge cloud storage peers. It fully takes into account current network bandwidth, edge cloud storage peer performance as well as history access cost. Random probability matching was also introduced for cache copy selection in the algorithm. Simulation experiment re- sults with CloudSim show that CDLBA-PM can not only reduce response time of user request, but also effectively bal- ance load of edge cloud storage peer.

Boosting named data networking for data dissemination in urban VANET scenarios

Named Data Networking (NDN) is a data-centric networking architecture designed for the future Internet. Instead of establishing host-based end-to-end communications, NDN accomplishes the content retrieval and distribution through the delivery of named data. In this paper, we boost the NDN architecture to support efficient content delivery in urban Vehicular Ad hoc Networks (VANETs). First we propose a new geo-based NDN forwarding strategy to achieve efficient and reliable packet delivery in urban VANET scenarios. Then we investigate the caching redundancy problem with the default full-path caching scheme in VANETs, and present some heuristic strategies to reduce the unnecessary cached copies. Simulation results show that our forwarding strategy can greatly enhance NDN for data dissemination in urban VANETs, which achieves 27%∼75% higher request success ratio, and 40%∼80% lower delay compared with the original NDN strategy in scenarios with different vehicle densities. Besides, the caching strategies can reduce the cached copies by about 50% in dense scenarios with nearly no drops in the success ratio and negligible increase in delay.

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.

NIQuery: Neuroimaging Informatics Query Framework for Data Sharing, Discovery, and Analysis

Event Abstract Back to Event NIQuery: Neuroimaging Informatics Query Framework for Data Sharing, Discovery, and Analysis B. Nolan Nichols1*, Robert F. Dougherty2, Landon T. Detwiler1, Gunnar Schaefer2, Randall J. Frank1, James F. Brinkley1, Brian A. Wandell2 and Thomas J. Grabowski1 1 Integrated Brain Imaging Center, University of Washington, Seattle, WA, United States 2 Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States Scientific discovery about the human brain will be accelerated by neuroinformatics services on the programmable web. Just as bioinformatics databases provide services for molecular data, a scalable service-oriented framework is needed to take advantage of the large number of human neuroimaging data sets now available online. We have developed a specification, NIQuery, for remote access to observation-level data in distributed and heterogeneous image-specialized databases. NIQuery integrates emerging open source standards for metadata description, access & query, and provides investigators with computational access to voxel-level data. The protocol supporting this functionality consists of: 1) a persistent Session object that wraps databases (e.g. XNAT[1], NIMS[2], Allen Institute[3]), exposes the NIQuery application programming interface, and serves objects and requests; 2) a Query object that provides a mechanism to interrogate databases with user defined and/or predefined web-accessible queries; 3) a Data object conforming to a supported 'image' data model (e.g., DICOM, NIfTI, etc.) that provides a mechanism to return pixel data to an application; and 4) a Workflow object through which a server provides a computational service on a Data object. A registry service (www.niquery.org) provides an index of available NIQuery servers, as well as the query, data, and workflow objects available on each server. NIQuery enables client applications to discover shared neuroimaging data using metadata-level distributed queries and then execute image processing workflows on discovered data at their source, on a cached copy in the cloud, or locally. A sample implementation of this framework involves exporting a snapshot of XNAT and NIMS databases into XCEDE XML files, indexing the snapshots with the NIQuery registry service, and remotely calculating quality control metrics on resting-state fMRI data. These informatics tools will support agile exploration and reuse of open access neuroimaging data.

QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks

In this paper, we investigate the problem of optimal content cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks. Specifically, in the media cloud, each content is transcoded into a set of media files with diverse playback rates, and appropriate files will be dynamically chosen in response to channel conditions and screen forms. Our design objective is to maximize the quality of experience (QoE) of an individual content for the end users, under a limited storage budget. Deriving a logarithmic QoE model from our experimental results, we formulate the individual content cache management for HTTP ABR streaming over wireless network as a constrained convex optimization problem. We adopt a two-step process to solve the snapshot problem. First, using the Lagrange multiplier method, we obtain the numerical solution of the set of playback rates for a fixed number of cache copies and characterize the optimal solution analytically. Our investigation reveals a fundamental phase change in the optimal solution as the number of cached files increases. Second, we develop three alternative search algorithms to find the optimal number of cached files, and compare their scalability under average and worst complexity metrics. Our numerical results suggest that, under optimal cache schemes, the maximum QoE measurement, i.e., mean-opinion-score (MOS), is a concave function of the allowable storage size. Our cache management can provide high expected QoE with low complexity, shedding light on the design of HTTP ABR streaming services over wireless networks.

Improving Efficiency of Apriori Algorithm using Cache Database

One of the most popular data mining approach to find frequent itemset in a given transactional dataset is Association rule mining. The important task of Association rule mining is to mine association rules using minimum support value which is specified by the user or can be generated by system itself. In order to calculate minimum support value, every time the complete database has to be scanned for each item in the transaction. This decreases the time complexity of the algorithm. Here we proposed a new algorithm which scan the database once and create a cache database for each transaction using hash map. This cache copy is then used to search for frequent item sets. Due to which the overhead of scaning complete database for each item is reduced, and efficiency is increased. Key word: Apriori, cache database, hash map, scanning time, time complexity.

A COOPERATIVE MODEL FOR PREFERENCE-BASED INFORMATION SHARING IN NARROW BANDWIDTH NETWORKS

Users of wide area network applications are usually concerned about both response time and content validity. The common solution of client-side caching that reuses cached content based on arbitrary time-to-live may not be applicable in narrow bandwidth environment, where heavy load is imposed on sparse transmission abilities. In such cases, some users may wait for a long time for fresh content extracted from the origin server although they would settle for obsolescent content, while other users may receive the cached copy which is considered valid, although they would be ready to wait longer for fresher content. In this work, a new model for caching is introduced, where clients introduce preferences regarding their expectations for the time they are willing to wait, and the level of obsolescence they are willing to tolerate. The cache manager considers user preferences, and is capable of balancing the relative importance of each dimension. A cost model is used to determine which of the following three alternatives is most promising: delivery of a local cached copy, delivery of a copy from a cooperating cache, or delivery of a fresh copy from the origin server. The proposed model is proven to be useful by experiments that used both synthetic data and real Web traces simulation. The experiments reveal that using the proposed model, it becomes possible to meet client needs with reduced latency. We also show the benefit of cache cooperation in increasing hit ratios and reducing latency. A prototype of the proposed model was built and deployed on real-world environment demonstrating how users can set preferences towards Web pages, and how cache managers are affected.

Cluster Based Data Consistency for Cooperative Caching over Partitionable Mobile Adhoc Network

Problem statement: Data availability and consistency are foremost issues in Mobile Ad hoc Networks due to the absence of permanent infrastructure. Cooperative caching addresses the data availability issue through coordinating the mobile nodes and sharing the cache copies among them. In the meantime, the mobile node must ensure the staleness of cache copies. The consistency maintenance resolves the staleness of the data among the source and caching node. Due to addition of more mobile nodes the network size is increased and it leads to increase the caching nodes. The mobility and disconnections causes additional overhead, latency and reduces the data delivery success ratio while updating the cache copy from the source. Approach: This study proposed Adaptive Push and Pull Algorithm for Clusters (APPC) and Cluster Based Data Consistency (CBDC) approach to address the consistency requirements and maintenance in mobile ad hoc network. Results: The CBDC satisfy the consistency requirements in partitioned clusters. The source node transmits the updated data through the cluster heads to the caching nodes. The APPC ensures the validity of cache copy by threshold Time-to-Live (TTL). Thus it provides efficient valid data accessibility in mobile ad hoc network. Conclusion: The simulation results shown that this proposed approach increases packet success ratio and reduces the delay and overhead when compared with the existing approaches Flexible Cache Consistency (FCPP) maintenance and Cluster Based Cooperative Caching Technique (CBCCT) for increasing number of nodes and speed respectively.