Partial Caching Research Articles

Content caching based on mobility prediction and joint user Prefetch in Mobile edge networks

With the development of 5G mobile networks, people’s demand for network response speed and services has increased to meet the needs of a large amount of data traffic, reduce the backhaul load caused by frequently requesting the same data (or content). The file is pre-stored in the base station by the edge device, and the user can directly obtain the requested data in the local cache without remotely. However, changes in popularity are difficult to capture, and data is updated more frequently through the backhaul. In order to reduce the number of backhauls and provide caching services for users with specific needs, we can provide proactive caching with users without affecting user activity. We propose a content caching strategy based on mobility prediction and joint user prefetching (MPJUP). The policy predicts the prefetching device data by predicting the user’s movement position with respect to time by the mobility of the user and then splits the partial cache space for prefetching data based on the user experience gain. Besides, we propose to reduce the backhaul load by reducing the number of content backhauls by cooperating prefetch data between the user and the edge cache device. Experimental analysis shows that our method further reduces the average delay and backhaul load, and the prefetch method is also suitable for more extensive networks.

A Partial Page Cache Strategy for NVRAM-Based Storage Devices

Nonvolatile random access memory (NVRAM) is becoming a popular alternative as the memory and storage medium in battery-powered embedded systems because of its fast read/write performance, byte-addressability, and nonvolatility. A well-known example is phase-change memory (PCM) that has much longer life expectancy and faster access performance than NAND flash. When NVRAM is considered as both main memory and storage in battery-powered embedded systems, existing page cache mechanisms have too many unnecessary data movements between main memory and storage. To tackle this issue, we propose the concept of “union page cache,” to jointly manage data of the page cache in both main memory and storage. To realize this concept, we design a partial page cache strategy that considers both main memory and storage as its management space. This strategy can eliminate unnecessary data movements between main memory and storage without sacrificing the data integrity of file systems. A series of experiments was conducted on an embedded platform. The results show that the proposed strategy can improve the file accessing performance up to 85.62% when PCM used as a case study.

Generalization of LRU Cache Replacement Policy with Applications to Video Streaming

Caching plays a crucial role in networking systems to reduce the load on the network and is commonly employed by content delivery networks (CDNs) to improve performance. One of the commonly used mechanisms, Least Recently Used (LRU), works well for identical file sizes. However, for asymmetric file sizes, the performance deteriorates. This article proposes an adaptation to the LRU strategy, called gLRU, where the file is sub-divided into equal-sized chunks. In this strategy, a chunk of the newly requested file is added in the cache, and a chunk of the least-recently-used file is removed from the cache. Even though approximate analysis for the hit rate has been studied for LRU, the analysis does not extend to gLRU, since the metric of interest is no longer the hit rate as the cache has partial files. This article provides a novel approximation analysis for this policy where the cache may have partial file contents. The approximation approach is validated by simulations. Further, gLRU outperforms the LRU strategy for a Zipf file popularity distribution and censored Pareto file size distribution for the file download times. Video streaming applications can further use the partial cache contents to help the stall duration significantly, and the numerical results indicate significant improvements (32%) in stall duration using the gLRU strategy as compared to the LRU strategy. Furthermore, the gLRU replacement policy compares favorably to two other cache replacement policies when simulated on MSR Cambridge Traces obtained from the SNIA IOTTA repository.

Synergy: A Hypervisor Managed Holistic Caching System

Efficient system-wide memory management is an important challenge for over-commitment based hosting in virtualized systems. Due to the limitation of memory domains considered for sharing, current deduplication solutions simply cannot achieve system-wide deduplication. Popular memory management techniques like sharing and ballooning enable important memory usage optimizations individually. However, they do not complement each other and, in fact, may degrade individual benefits when combined. We propose $\mathsf{Synergy}$Synergy, a hypervisor managed caching system to improve memory efficiency in over-commitment scenarios. $\mathsf{Synergy}$Synergy builds on an exclusive caching framework to achieve, for the first time, system-wide memory deduplication. $\mathsf{Synergy}$Synergy also enables the co-existence of the mutually agnostic ballooning and sharing techniques within hypervisor managed systems. Finally, $\mathsf{Synergy}$Synergy implements a novel file-level eviction policy that prevents hypervisor caching benefits from being squandered away due to partial cache hits. $\mathsf{Synergy}$Synergy's cache is flexible with configuration knobs for cache sizing and data storage options, and a utility-based cache partitioning scheme. Our evaluation shows that $\mathsf{Synergy}$Synergy consistently uses 10 to 75 percent lesser memory by exploiting system-wide deduplication as compared to inclusive caching techniques and achieves application speedup of 2x to 23x. We also demonstrate the capabilities of $\mathsf{Synergy}$Synergy to increase VM packing density and support for dynamic reconfiguration of cache partitioning policies.

Cloud-Edge Coordinated Processing: Low-Latency Multicasting Transmission

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

Deep learning-based edge caching for multi-cluster heterogeneous networks

In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability.

Access Types Effect on Internet Video Services and Its Implications on CDN Caching

Video providers heavily rely on geographically distributed content distribution networks (CDNs) to place video content as close to users as possible, with an aim of improving video quality and avoiding single point of failure at the server side. The effectiveness of CDNs is mostly dependent on the content consumption patterns. Currently, video providers are offering access to content from different platforms (e.g., mobile devices and PC clients), which might result in distinct video content consumption patterns and finally affect the efficiency of CDN caching. Nevertheless, the access type effect on Internet videos is not well understood. In this paper, using a data set consisting of 26 million video requests of a large-scale commercial video-on-demand system, we study the effect of three main access types, i.e., proprietary software on PC clients, Web browser, and mobile apps. Several observations suggest that access types should be considered carefully in CDN design. In particular, the user engagement, user interests in content, and video popularity dynamics patterns, three important factors for video caching, vary remarkably in the three access types. Leveraging off our findings, we propose an access type-aware CDN caching system that associates a cache for each access type and also several optimizations, including partial caching of videos based on chunk-level caching, cross-platform read-only cache access, and prefiltering of the least popular videos. Trace-driven simulations demonstrate that the access type-aware CDN caching achieves high cache hit rate and, more importantly, greatly reduces the disk load that is measured by the number of cache replacement operations.

Сравнение областей истинности запросов к реляционной базе данных

We propose new methods and algorithms of analytical truth space comparison for Relational Database queries. Such a comparison aims to define the possibility of partial or full cache usage. The cache is stored on user’s computer and Database server is supposed to be remote. In case user query’s result is contained in cache we can simply take the data from there avoiding any server requests. The suggested method may also be used for defining data missing in cache and performing query to only receive that data. Analytical computations are also used to achieve this and it differs our approach from existing ones. Query comparison algorithms are based on the Predicate Logic theory. Formulas are presented by logical constraints in SQL and predicates correspond to SQL operations.

A Power Saving Set Associative Cache Model

Background/Objectives: Placing conflict cache lines in alternate set is proposed for set associative caches in literature. The conflicting lines are placed in one alternate set for each set. Methods/Statistical Analysis: This paper proposes architecture to place conflicting lines in set with maximum vacant ways in set associative caches. The model introduces one register per cache way with number of bits equal to number of cache sets. A sequential circuit using the register enables the cache ways of any set. The unoccupied ways are disabled. The proposed model is simulated with SPEC2K benchmarks. The power consumption is calculated using Quartus 2 tool and verilog code. Findings: An average improvement in power consumption of 68% for benchmarks with partial filled cache ways is observed with average memory access degradation of about 4.86%. A performance degradation in average memory access time of about 34% compared with direct mapped cache system and 3% compared with random placement algorithm is observed for the proposed system. The proposed model is for power consumption as is validated from the findings. The proposed model can be adapted for set associative caches to improve power consumption. Applications/Improvements: A model with optimal operational cache sets with methods to decrease power dissipation of sequential circuits, low power techniques for digital circuits can be used to improve the power consumption

Optimal chunking and partial caching in information-centric networks

Caching is widely used to reduce network traffic and improve user experience. Traditionally caches store complete objects, but video files and the recent emergence of information-centric networking have highlighted a need for understanding how partial caching could be beneficial. In partial caching, objects are divided into chunks which are cached either independently or by exploiting common properties of chunks of the same file. In this paper, we identify why partial caching is beneficial, and propose a way to quantify the benefit. We develop an optimal n-Chunking algorithm with complexity O(ns2) for an s-byte file, and compare it with ∊-optimal homogeneous chunking, where ∊ is bounded by O(n-2). Our analytical results and comparison lead to the surprising conclusion that neither sophisticated partial caching algorithm nor high complexity optimal chunking are needed in information-centric networks. Instead, simple utility-based in-network caching algorithm and low complexity homogeneous chunking are sufficient to achieve the most benefits of partial caching.

Combining Instruction Prefetching with Partial Cache Locking to Improve WCET in Real-Time Systems

Caches play an important role in embedded systems to bridge the performance gap between fast processor and slow memory. And prefetching mechanisms are proposed to further improve the cache performance. While in real-time systems, the application of caches complicates the Worst-Case Execution Time (WCET) analysis due to its unpredictable behavior. Modern embedded processors often equip locking mechanism to improve timing predictability of the instruction cache. However, locking the whole cache may degrade the cache performance and increase the WCET of the real-time application. In this paper, we proposed an instruction-prefetching combined partial cache locking mechanism, which combines an instruction prefetching mechanism (termed as BBIP) with partial cache locking to improve the WCET estimates of real-time applications. BBIP is an instruction prefetching mechanism we have already proposed to improve the worst-case cache performance and in turn the worst-case execution time. The estimations on typical real-time applications show that the partial cache locking mechanism shows remarkable WCET improvement over static analysis and full cache locking.

A Predictive Model for Frequently Viewed Tiles in a Web Map

Distributing pre-generated image tiles from a server-side cache is a fast and popular way of serving maps on the Web. However, these caches can grow unmanageably large for maps covering many scales over a broad extent. This research presents a model for determining high-priority geographic areas for tile caching. The model considers variables found in previous research to be of interest to Web map users, such as populated places, major roads, coastlines, and tourist attractions. The proportion of area, disk space, and time saved when creating a partial cache (consisting only of high-interest areas) and a cache of the entire map extent are compared to establish that marked savings are possible. Several applications of the model beyond tile caching are briefly discussed.

Traffic Modeling and Proportional Partial Caching for Peer-to-Peer Systems

Peer-to-peer (P2P) file sharing systems generate a major portion of the Internet traffic, and this portion is expected to increase in the future. We explore the potential of deploying proxy caches in different autonomous systems (ASes) with the goal of reducing the cost incurred by Internet service providers and alleviating the load on the Internet backbone. We conduct an eight-month measurement study to analyze the P2P traffic characteristics that are relevant to caching, such as object popularity, popularity dynamics, and object size. Our study shows that the popularity of P2P objects can be modeled by a Mandelbrot-Zipf distribution, and that several workloads exist in P2P traffic. Guided by our findings, we develop a novel caching algorithm for P2P traffic that is based on object segmentation, and proportional partial admission and eviction of objects. Our trace-based simulations show that with a relatively small cache size, a byte hit rate of up to 35% can be achieved by our algorithm, which is close to the byte hit rate achieved by an off-line optimal algorithm with complete knowledge of future requests. Our results also show that our algorithm achieves a byte hit rate that is at least 40% more, and at most triple, the byte hit rate of the common Web caching algorithms. Furthermore, our algorithm is robust in face of aborted downloads, which is a common case in P2P systems.

A P2P streaming service architecture with distributed caching

Multimedia streaming served through peer-to-peer (P2P) networks is booming nowadays. However, the end-to-end streaming quality is generally unstable due to the variability of the state of serve-peers. On the other hand, proxy caching is a bandwidth-efficient scheme for streaming over the Internet, whereas it is a substantially expensive method needing dedicated powerful proxy servers. In this paper, we present a P2P cooperative streaming architecture combined with the advantages of both P2P networks and multimedia proxy caching techniques to improve the streaming quality of participating clients. In this frame-work, a client will simultaneously retrieve contents from the server and other peers that have viewed and cached the same title before. In the meantime, the client will also selectively cache the aggregated video content so as to serve still future clients. The associate protocol to facilitate the multi-path streaming and a distributed utility-based partial caching scheme are detailedly discussed. We demonstrate the effectiveness of this proposed architecture through extensive simulation experiments on large, Internet-like topologies.

Prototype Implementation of a Proxy Caching System for Streaming Media Objects

Existing techniques for caching Web objects are not appropriate for the multimedia streaming service. In this paper, the authors focus on the proxy caching problem specifically for multimedia streaming objects. A prototype design and implementation of a proxy caching system – HUSTProxy is proposed. The main contribution of HUSTProxy is its ability of partial video caching, sending rate control and providing a more rational prefix caching size determination. These techniques are implemented in our design and are discussed in detail in this paper.

Study of the Impact of Replacement Granularity and Associated Strategies on Video Caching

Partial caching of large media objects such as video files has been proposed recently as the caching of entire objects can easily exhaust the storage resources of a proxy server. In this paper the idea of segmenting video files into chunks and applying replacement decisions at the chunk level rather than on entire videos is examined. It is shown that a higher byte hit ratio (BHR) can be achieved by appropriately adjusting the replacement granularity. The price paid for the improved BHR performance is that the replacement algorithm takes a longer time to converge to the steady state BHR. For the segmentation of video into chunks two methods are presented. The Fixed Chunk Size segmentation scheme that is rather simple and reveals the basic trade-off between byte hit ratio (BHR) and responsiveness to changes of popularity; the Variable Chunk Size segmentation scheme that uses the request frequencies to dynamically adjust the size of the chunk and is shown to be capable of combining a small response time with high BHR. Moreover, a variation of the fixed chunk size segmentation scheme is presented, which is shown to improve its performance by switching between different chunk sizes. Video segmentation is also considered as a mechanism to provide for caching differentiation based on access costs. By employing access cost dependent chunk sizes an overall access cost reduction is demonstrated.

Content distribution for seamless transmission

We introduce a new paradigm in information transmission, the concept of SEAMLESS TRANSMISSION, whereby any client in a network requesting a file starts receiving it immediately, and experiences no delays throughout the remainder of the downloading time. This notion is based on the partial caching concept [2] which was introduced to overcome some of the disadvantages of traditional cache replacement algorithms such as LRU and LRU-threshold [1]. The main idea of partial caching is to store an initial part of the file in the cache and to obtain the rest of the file from the origin server. To achieve the maximal retrieval performance of seamless transmission, clients must be prepared to re-sequence segments of the files received out of order. With this caveat, seamless transmission can be viewed as a way to implement strict quality of service (QoS) guarantees to all clients of a network. This paper gives a provably correct technique for achieving seamlessness for a given file located at the root node in a tree structured network.

Network-aware partial caching for Internet streaming media

The delivery of multimedia over the Internet is affected by adverse network conditions such as high packet loss rate and long delay. This paper aims at mitigating such effects by leveraging client-side caching proxies. We present a novel cache architecture and associated cache management algorithms that turn edge caches into accelerators of streaming media delivery. This architecture allows partial caching of media objects and joint delivery from caches and origin servers. Most importantly, the caching algorithms are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit rate requirements, and the available bandwidth between clients and servers. Using Internet bandwidth models derived from proxy cache logs and measured over real Internet paths, we have conducted extensive simulations to evaluate the performance of various cache management algorithms. Our experiments demonstrate that network-aware caching algorithms can significantly reduce startup delay and improve stream quality. Our experiments also show that partial caching is particularly effective when bandwidth variability is not very high.