Cache Size Research Articles

Content-Aware Cooperative Transmission in HetNets With Consideration of Base Station Height

With macrocell base stations (MBSs) providing basic coverage for mobile users, the multiple tiers of cache-enabled small-cell base stations (SBSs) can opportunistically form user-centric clusters to enhance network capacity through traffic offloading and cooperative transmission. In this paper, we investigate cooperative transmission in cache-enabled heterogeneous networks considering the impact of base station (BS) heights. Specifically, the user-centric cooperative SBS clusters are formed based on the information of the cached contents, the transmission distance, and the cell load at SBSs. The users failed to be offloaded to the cooperative SBS clusters are served by the nearest MBSs. By incorporating the COST 231 Hata model for the line-of-sight and nonline-of-sight channels, the explicit expressions for the average spectral efficiency (SE) are obtained with statistical characterizations for the cell load distribution, as well as the aggregated information and interference signal strength. The analytical results indicate that with the COST 231 Hata model and the cooperative SBS cluster, the average SE decreases with the increase of BS height. Moreover, different tradeoffs exist with the varying cache size, SBS density, and cooperative distance threshold, which results in a bell-shaped SE with respect to (w.r.t) the SBS density and the cooperative distance threshold. In addition, with an appropriate cooperative distance threshold, the average SE exhibits a bell-shaped relationship w.r.t the cache size. Extensive simulations are conducted to validate the analytical results and demonstrate the impact of the network parameters.

RT-CaCC: A Reliable Transport With Cache-Aware Congestion Control Protocol in Wireless Sensor Networks

Reliability of data transport in wireless sensor networks is critical in the presence of high packet loss level either due to link contentions or buffer congestions. Mechanisms such as intermediate caching and congestion control can enhance the reliability performance of transport protocols in the event of packet loss. However, these two mechanisms are designed independently for most cache-based transport protocols. In this paper, we developed a new reliable transport protocol with a cache-aware congestion control mechanism called RT-CaCC. RT-CaCC utilizes cache management policies such as cache insertion, cache elimination, and cache size allocation to mitigate packet losses in the network while maximizing cache utilization and bandwidth allocation. The protocol was evaluated in network topologies with different packet loss scenarios. Results showed that RT-CaCC obtained an average of 15%–38% improvement gain compared with baseline protocols. In addition, RT-CaCC consistently outperformed other cache-based transport protocols like distributed TCP caching and end-to-end reliable and congestion aware transport layer protocol in terms of cache utilization, end-to-end delay, and fairness metrics. Finally, the comparison between simulation and analytical results showed that there is an almost perfect match with the difference between curve points never exceeding 14% of the analytical cost.

On Base Station Coordination in Cache- and Energy Harvesting-Enabled HetNets: A Stochastic Geometry Study

In this paper, we study the performance of base station (BS) coordination in heterogeneous networks (HetNets) with cache-enabled and renewable energy-powered small cell BSs (SBSs). Macrocell base stations (MBSs) provide basic coverage, while the SBSs, powered by harvested energy, conduct content-aware coordinated transmission to provide high data rate and further improve the network coverage. Specifically, a joint transmission strategy is performed based on the knowledge of the energy states and the cached contents of SBSs, along with the awareness of the availability of channel resources and the average received signal strength (RSS) of the corresponding link. Stochastic geometry is applied to characterize the statistics of the cell load at MBSs and SBSs, as well as the aggregated information and interference signal strength. Then, the average user capacity for the joint transmission is obtained. Additionally, the coverage probability is derived with gamma approximation for the aggregated information and interference signal strength. Analytical results reveal that the average user capacity and coverage probability can be maximized with optimal cache size, energy harvesting rate and cooperative RSS threshold. Finally, extensive numerical and simulation results are provided.

Performance Analysis of Cache-Enabled Millimeter Wave Small Cell Networks

Millimeter wave (mmWave) small-cell networks can provide high regional throughput, but the backhaul requirement has become a performance bottleneck. This paper proposes a hybrid system that combines traditional backhaul-connected small base stations (SBSs) and cache-enabled SBSs to achieve the maximum area spectral efficiency (ASE) while saving backhaul consumption in mmWave small cell networks. We derive and compare the ASE results for both the traditional and hybrid networks, and also show that the optimal content placement to maximize ASE is to cache the most popular contents. Numerical results demonstrate the performance improvement of deploying cache-enabled SBSs. Furthermore, given a total caching capacity, it is revealed that there is a tradeoff between the cache-enabled SBSs density and individual cache size to maximize the ASE.

MPart: miss-ratio curve guided partitioning in key-value stores

Web applications employ key-value stores to cache the data that is most commonly accessed. The cache improves an web application's performance by serving its requests from memory, avoiding fetching them from the backend database. Since the memory space is limited, maximizing the memory utilization is a key to delivering the best performance possible. This has lead to the use of multi-tenant systems, allowing applications to share cache space. In addition, application data access patterns change over time, so the system should be adaptive in its memory allocation. In this work, we address both multi-tenancy (where a single cache is used for multiple applications) and dynamic workloads (changing access patterns) using a model that relates the cache size to the application miss ratio, known as a miss ratio curve. Intuitively, the larger the cache, the less likely the system will need to fetch the data from the database. Our efficient, online construction of the miss ratio curve allows us to determine a near optimal memory allocation given the available system memory, while adapting to changing data access patterns. We show that our model outperforms an existing state-of-the-art sharing model, Memshare, in terms of overall cache hit ratio and does so at a lower time cost. We show that for a typical system, overall hit ratio is consistently 1 percentage point greater and 99.9th percentile latency is reduced by as much as 2.9% under standard web application workloads containing millions of requests.

On Resource Pooling and Separation for LRU Caching

Caching systems using the Least Recently Used (LRU) principle have now become ubiquitous. A fundamental question for these systems is whether the cache space should be pooled together or divided to serve multiple flows of data item requests in order to minimize the miss probabilities. In this paper, we show that there is no straight yes or no answer to this question, and depends on complex combinations of critical factors, including, e.g., request rates, overlapped data items across different request flows, data item popularities and their sizes. To this end, we characterize the performance of multiple flows of data item requests under resource pooling and separation when the cache size is large. Analytically we show that it is asymptotically optimal to jointly serve multiple flows if their data item sizes and popularity distributions are similar, and their arrival rates do not differ significantly; the self-organizing property of LRU caching automatically optimizes the resource allocation among them asymptotically. Otherwise, separating these flows could be better, e.g., when data sizes vary significantly. We also quantify critical points beyond which resource pooling is better than separation for each of the flows when the overlapped data items exceed certain levels. These results provide new insights on the performance of caching systems.

ECI-Cache

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance. In this paper, we propose a new high-Endurance and Cost-efficient I/O caching (ECI-Cache) scheme for virtualized platforms in large-scale data centers, which improves both performance-per-cost and endurance of the SSD cache. ECI-Cache dynamically assigns 1) an efficient cache size for each VM, to maximize the overall performance of the running VMs and 2) an effective write policy for each VM, to enhance the endurance and performance-per-cost of the storage subsystem.

Adaptive TTL-Based Caching for Content Delivery

Content delivery networks (CDNs) cache and serve a majority of the user-requested content on the Internet. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for non-stationary production CDN traffic. We propose two TTL-based caching algorithms that provide provable performance guarantees for request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using stochastic approximation. Given a feasible target hit rate, we show that d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We evaluate both d-TTL and f-TTL using an extensive trace containing more than 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out non-stationary content.

Adding Transmitters Dramatically Boosts Coded-Caching Gains for Finite File Sizes

In the context of coded caching in the $K$ -user broadcast channel, our work reveals the surprising fact that having multiple ( $L$ ) transmitting antennas, dramatically ameliorates the long-standing subpacketization bottleneck of coded caching by reducing the required subpacketization to approximately its $L$ th root, thus boosting the actual DoF by a multiplicative factor of up to $L$ . In asymptotic terms, this reveals that as long as $L$ scales with the theoretical caching gain, then the full cumulative (multiplexing + full caching) gains are achieved with constant subpacketization. This is the first time, in any known setting, that unbounded caching gains appear under finite file-size constraints. The achieved caching gains here are up to $L$ times higher than any caching gains previously experienced in any single- or multi-antenna fully connected setting, thus offering a multiplicative mitigation to a subpacketization problem that was previously known to hard-bound caching gains to small constants. The proposed scheme manages for the first time to virtually decompose the fully connected cache-aided channel into $L$ parallel channels. The scheme is practical; it works for all the values of $K$ and $L$ and all cache sizes, and its gains show in practice: e.g., for $K = 100$ , when $L = 1$ the theoretical caching gain of $G = 10$ , under the original coded caching algorithm, would have needed subpacketization $S_{1} = \binom {K}{G} = \binom {100}{10} > 10^{13}$ , while if extra transmitting antennas were added, the subpacketization was previously known to match or exceed $S_{1}$ . Now for $L = 5$ , our scheme offers the theoretical (unconstrained) cumulative DoF $d_{L} = L + G = 5 + 10 = 15$ , with subpacketization $S_{L} = \binom {K/L}{G/L} = \binom {100/5}{10/5} = 190$ . The work extends to the multi-server and cache-aided IC settings, while the scheme’s performance, given subpacketization $S_{L} = \binom {K/L}{G/L}$ , is within a factor of 2 from the optimal linear sum-DoF.

Cache-Aware Source Coding

In this letter, we show that Huffman’s source coding method is not optimal for cache-aided networks. To that end, we propose an optimal algorithm for the cache-aided source coding problem. We define cache-aided entropy , which represents a lower bound on the average number of transmitted bits for cached-aided networks. A sub-optimal low-complexity cache-aided coding algorithm is presented. In addition, we propose a novel polynomial-time algorithm that obtains the global-optimal source code for wide range of cache sizes. Simulation results show a reduction in the average number of transmitted bits by more than 50% over Huffman’s method at moderate cache sizes.

Coded Caching and Content Delivery With Heterogeneous Distortion Requirements

Cache-aided coded content delivery is studied for devices with diverse quality-of-service requirements, specified by a different average distortion target. The network consists of a server holding a database of independent contents, and users equipped with local caches of different capacities. User caches are filled by the server during a low traffic period without the knowledge of particular user demands. As opposed to the current literature, which assumes that the users request files in their entirety, it is assumed that the users in the system have distinct distortion requirements; and therefore, each user requests a single file from the database to be served at a different distortion level. Our goal in this paper is to characterize the minimum delivery rate the server needs to transmit over an error-free shared link to satisfy all possible demand combinations at the requested distortion levels, considering both centralized and decentralized cache placement. For centralized cache placement, the optimal delivery rate is characterized for the two-file two-user scenario for any pair of target distortion requirements, when the underlying source distribution is successively refinable. For the two-user scenario with more than two successively refinable files, the optimal scheme is characterized when the cache capacities of the users are the same and the number of files is a multiple of 3. For the general source distribution, not necessarily successively refinable, and with arbitrary number of users and files, a layered caching and delivery scheme is proposed, assuming that scalable source coding is employed at the server. This allows dividing the problem into two subproblems: the lossless caching of each layer with heterogeneous cache sizes, and cache allocation among layers. A delivery rate minimization problem is formulated and solved numerically for each layer; while two different schemes are proposed to allocate user caches among layers, namely, proportional cache allocation and ordered cache allocation . A decentralized lossy coded caching scheme is also proposed, and its delivery rate performance is studied. Simulation results validate the effectiveness of the proposed schemes in both settings.

Energy Minimization for Cache-Assisted Content Delivery Networks With Wireless Backhaul

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic time by bringing data closer to end users. In this letter, we investigate energy-efficiency performance of cache-assisted content delivery networks with wireless backhaul by taking into account cache capability when designing the signal transmission. We consider multi-layer caching and the performance in cases when both base station and users are capable of storing content data in their local cache. Specifically, we analyze energy consumption in both backhaul and access links under two uncoded and coded caching strategies. Then two optimization problems are formulated to minimize total energy cost for the two caching strategies while satisfying some given quality of service constraint. We demonstrate via numerical results that the uncoded caching achieves higher energy efficiency than the coded caching in the small user cache size regime.

Fog-based caching in software-defined information-centric networks

In this paper, we propose a cache replacement approach for Fog applications in Software Defined Networks (SDNs). Our approach depends on three functional factors in SDNs. These three factors are: age of data based on periodic request, popularity of on-demand requests, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These factors are considered together to assign a value to the cached data in a software-defined network in order to retain the most valuable information in the cache for longer time. The higher the value, the longer the duration for which the data will be retained in the cache. This replacement strategy provides significant availability for the most valuable and difficult to sense data in the SDNs. Extensive simulations are performed to compare our approach against other dominant cache replacement policies under varying circumstances such as data popularity, cache size, network load, and connectivity degree.

Combining Software Cache Partitioning and Loop Tiling for Effective Shared Cache Management

One of the biggest challenges in multicore platforms is shared cache management, especially for data-dominant applications. Two commonly used approaches for increasing shared cache utilization are cache partitioning and loop tiling. However, state-of-the-art compilers lack efficient cache partitioning and loop tiling methods for two reasons. First, cache partitioning and loop tiling are strongly coupled together, and thus addressing them separately is simply not effective. Second, cache partitioning and loop tiling must be tailored to the target shared cache architecture details and the memory characteristics of the corunning workloads. To the best of our knowledge, this is the first time that a methodology provides (1) a theoretical foundation in the above-mentioned cache management mechanisms and (2) a unified framework to orchestrate these two mechanisms in tandem (not separately). Our approach manages to lower the number of main memory accesses by an order of magnitude keeping at the same time the number of arithmetic/addressing instructions to a minimal level. We motivate this work by showcasing that cache partitioning, loop tiling, data array layouts, shared cache architecture details (i.e., cache size and associativity), and the memory reuse patterns of the executing tasks must be addressed together as one problem, when a (near)-optimal solution is requested. To this end, we present a search space exploration analysis where our proposal is able to offer a vast deduction in the required search space.

Resource allocation for cache-enabled cloud-based small cell networks

To address the serious challenge of satisfying explosively increasing multimedia content requests from a massive number of users in mobile networks, deploying content caching in base stations to offload network traffic while satisfying content requests locally has been regarded as an effective approach to enhance the network performance. Moreover, content delivery via wireless transmissions in a cache-enabled mobile network needs to be optimized taking the proactive caching policy into consideration. Accordingly, in this paper, we investigate and propose an efficient resource allocation framework for cache-enabled cloud-based small cell networks (C-SCNs) to achieve the benefits of content caching by considering two phases, i.e., content placement and content delivery. In particular, for the content placement phase, we propose a low-complexity distributed popularity-based framework for allocating cache sizes of SBSs to popular contents, in order to offload network traffic and satisfy content requests locally. For the content delivery phase, we propose a low-complexity joint user association and subcarrier-power allocation scheme for min-rate guaranteed content delivery over orthogonal frequency division multiple access (OFDMA) based downlink transmissions. Trace-based simulations and numerical results demonstrate the effectiveness of the proposed schemes in the cache-enabled C-SCNs.

An efficient implementation of semi-numerical computation of the Hartree-Fock exchange on the Intel Phi processor

Unique technical challenges and their solutions for implementing semi-numerical Hartree-Fock exchange on the Phil Processor are discussed, especially concerning the single- instruction-multiple-data type of processing and small cache size. Benchmark calculations on a series of buckyball molecules with various Gaussian basis sets on a Phi processor and a six-core CPU show that the Phi processor provides as much as 12 times of speedup with large basis sets compared with the conventional four-center electron repulsion integration approach performed on the CPU. The accuracy of the semi-numerical scheme is also evaluated and found to be comparable to that of the resolution-of-identity approach.

Fast Content Delivery via Distributed Caching and Small Cell Cooperation

The demand for higher and higher wireless data rates is driven by the popularity of mobile video content delivery through wireless devices such as tablets and smartphones. To achieve unprecedented mobile content delivery speeds while reducing backhaul cost and delay, in this paper we propose a new system architecture that combines two recent ideas, distributed caching of content in small cells (FemtoCaching), and, cooperative transmissions from nearby base stations (Coordinated Multi-Point). A key characteristic of the proposed architecture is the interdependence between the caching strategy and the physical layer coordination. Specifically, the caching strategy may cache different content in nearby base stations (BSs) to maximize the cache hit ratio, or cache the same content in multiple nearby BSs such that the corresponding BSs can transmit concurrently, e.g., to multiple users using zero-forcing beamforming, and achieve multiplexing gains. Such interdependency allows a joint cross-layer optimization. Given the popularity distribution of the content, the available cache size, and the network topology, we devise near-optimal strategies of caching such that the system throughput is maximized or the system delay is minimized. Under realistic scenarios and assumptions, our analytical and simulation results show that our system yields significantly faster content delivery, which can be one order of magnitude faster than that of legacy systems.

AC-DSE: Approximate Computing for the Design Space Exploration of Reconfigurable MPSoCs

This paper concerns the design space exploration (DSE) of Reconfigurable Multi- Processor System-on- Chip (MPSoC) architectures. Reconfiguration allows users to allocate optimum system resources for a specific application in such a way to improve the energy and throughput balance. To achieve the best balance between power consumption and throughput performance for a particular application domain, typical design space parameters for a multi-processor architecture comprise the cache size, the number of processor cores and the operating frequency. The exploration of the design space has always been an offline technique, consuming a large amount of time. Hence, the exploration has been unsuitable for reconfigurable architectures, which require an early runtime decision. This paper presents Approximate Computing DSE (AC-DSE), an online technique for the DSE of MPSoCs by means of approximate computing. In AC-DSE, design space solutions are first obtained from a set of optimization algorithms, which in turn are used to train a neural network (NN). From then on, the NN can be used to rapidly return its own solutions in the form of design space parameters for a desired energy and throughput performance, without any further training.

AMR Parsing With Cache Transition Systems

In this paper, we present a transition system that generalizes transition-based dependency parsing techniques to generateAMR graphs rather than tree structures. In addition to a buffer and a stack, we use a fixed-size cache, and allow the system to build arcs to any vertices present in the cache at the same time. The size of the cache provides a parameter that can trade off between the complexity of the graphs that can be built and the ease of predicting actions during parsing. Our results show that a cache transition system can cover almost all AMR graphs with a small cache size, and our end-to-end system achieves competitive results in comparison with other transition-based approaches for AMR parsing.

ECI-Cache

In recent years, high interest in using Virtual Machines (VMs) in data centers and Cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to provide a high performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualization platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance. In this paper, we present a new high-Endurance and Cost-efficient I/O Caching (ECI-Cache) scheme for virtualized platforms, which can significantly improve both the performance-per-cost and endurance of storage subsystems as opposed to previously proposed I/O caching schemes. Unlike traditional I/O caching schemes which allocate cache size only based on reuse distance of accesses, we propose a new metric, Useful Reuse Distance (URD), which considers the request type in reuse distance calculation, resulting in improved performance-per-cost and endurance for the SSD cache. Via online characterization of workloads and using URD, ECI-Cache partitions the SSD cache across VMs and is able to dynamically adjust the cache size and write policy for each VM. To evaluate the proposed scheme, we have implemented ECI-Cache in an open source hypervisor, QEMU (version 2.8.0), on a server running the CentOS 7 operating system (kernel version 3.10.0-327). Experimental results show that our proposed scheme improves the performance, performance-per-cost, and endurance of the SSD cache by 17%, 30% and 65%, respectively, compared to the state-of-the-art dynamic cache partitioning scheme.