Hybrid Cache Research Articles

Exploration and optimization of novel replacement and prefetching strategies for inefficiencies of advanced MRAM-based hybrid cache systems

With the emergence of cutting-edge hardware systems such as cloud computing, edge computing, and on-chip neural network accelerators, how to design advanced memory strategies to substitute the traditional ones for maximizing the potential performance of non-volatile memory (NVM) under the existing hardware conditions, has become an urgent research issue for both academia and industrial communities. It is promising and innovative to improve computer systems in the layer of data exchanging with the emerging advanced semiconductor devices. In the paper, to address the inefficiencies of write-intensive, high power consumption, low hit rate and so on, which exist in hybrid magnetic random access memory cache systems, three novel cache replacement strategies and two cache prefetching strategies are put forward. The proposed triple novel replacement strategies, including historical frequency and time judgments, duplicate data-aware deletion, and dynamic relevance factors computing, can be utilized to compensate for the shortcomings of the traditional least recently used replacement strategy, respectively. In the two novel prefetching strategies, region distribution parameters and Listnet ranking network are imported into the caching process, respectively, to achieve optimized hitting performance. The simulation results demonstrate that the proposed replacement strategies can achieve up to 61.76%, 84.91%, 56.49%, and 53.21% optimization of write count, hit rate, dynamic power, and IPC compared to the conventional one. The proposed prefetching strategy can achieve up to 91.27%, 49.25% hit rate and IPC optimization. Meanwhile, the synthetic evaluation of the replacement and prefetching strategies are elaborated in the paper, including multi-core characteristics, information entropy, interplays and the performance constraints between replacement and prefetching mechanism, which would facilitate more credible ideas for future memory inefficiencies management and strategy design.

Cache-MCDM: A hybrid caching scheme in Mobile Named Data Networks based on multi-criteria decision making

Robust and reliable content retrieval in a Mobile Ad Hoc Network (MANET) is challenged by its intermittent connectivity and mobility characteristics. In-network caching and name-based content retrieval attributes of Named Data Networks (NDN) can counterattack the stated challenges of MANETs. The distance- or hop-based caching schemes, proposed mainly for infrastructure NDN, admit contents either close to the content source, client, or midway, which are trivial to realize since content mainly travels from the network core (content sources) to the edge (clients). In NDN-based MANETs, the mobility attribute causes source and consumer nodes to change their locations frequently. Therefore, the content can travel from the network core to the edge and vice-versa. Under such circumstances, the traditional hop-based schemes that cache at particular points, for instance, either near the source, client, or halfway, may vandalize the efficient content retrieval in MANETs. Thus, proposing a new strategy capable of caching at any of the stated caching points considering varying network contexts, for instance, location change of source and consumer, is inevitable. Such a strategy can scale well in terms of network resource usage and significantly improve PDR and content retrieval latency performances in NDN-based MANET. In this direction, we propose an AHP-based hybrid caching scheme, namely Cache-MCDM, that equips each router with a set of different hop-based caching options and employs AHP-TOPSIS method to pick the most suitable caching option. The proposed method models the suitable option selection as a Multi-Criteria Decision Making (MCDM) problem. Simulation reveals that Cache-MCDM is more effective and efficient than hop-based probabilistic caching (HPC), opportunistic caching (OppProb), probabilistic in-network caching (ProbCache), and SourceCacheProb (SCP), in terms of (i) PDR by approximately 3%, 15%, 10%, and 11%, respectively, (ii) latency by approximately 25%, 145%, 75%, and 77%, respectively, (iii) retransmission ratio by approximately 7%, 30%, 25%, and 25%, respectively, and (iv) cache replacement rate by approximately 8%, 100%, 80%, and 25%, respectively.

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency.

VMIFresh: Efficient and fresh caches for virtual machine introspection

Virtual machine introspection (VMI) is the process of extracting knowledge about the inner state of a virtual machine from the outside. Traditional passive introspection mechanisms have proved themselves ineffective in many application domains due to their low performance. As a remedy for this issue, caching at the level of the introspection application was introduced. However, this sacrificed the freshness of VMI and led to an inconsistent outside view.In this work, we propose a multi-purpose hybrid caching scheme with freshness and consistency guarantees that is interleaved with the guest's MMU. This scheme can easily be integrated into existing applications and frameworks such as libvmi and Volatility 3. We demonstrate its feasibility by developing a prototype for such applications. Furthermore, the experimental evaluation of our approach suggests that it even significantly exceeds the performance of previous inconsistent caches.

RISC-V-Based Evaluation and Strategy Exploration of MRAM Triple-Level Hybrid Cache Systems

Magnetic random access memory (MRAM) is considered one of the most promising memories among nonvolatile memories (NVMs), to replace static random access memory (SRAM) in the computing system to improve its cache performance. In this article, performance evaluation and strategy exploration of three-level hybrid cache systems are conducted under the framework of Reduced InstrucTIon Set Computer-Five (RISC-V) instruction set. First, system-level joint calls of Nvsim, Gem5, McPAT, and other cache emulation architectures are implemented based on the system-level simulator called MAGPIE tool. Based on the performance comparison of spin transfer torque (STT)-MRAM, spin-orbit torque (SOT)-MRAM, and SRAM with varied capacities, a variety of combined CPU three-level hybrid cache architectures are simulated. The performances of the proposed hybrid cache systems and the bus impact of interconnecting are evaluated, with better analysis of the cost of CPU system implementation and interactions among levels of cache. The power consumption of different processors with various instruction sets and cores is also evaluated. It is demonstrated that SOT-MRAM and STT-MRAM show great potential applications as L2 and L3 caches in the RISC-V system. RISC-V shows the potential benefits for future CPU cache. Meanwhile, an adaptive stride prefetching strategy is proposed to address the problem of delay hysteresis and high miss rate of STT-MRAM in the L3 cache. The applicability of this strategy to different storage technologies and the comparison with cutting-edge technologies are also illustrated. Simulation results show that the prefetching strategy can achieve at most 64.12% and 94.55% optimization effects on the miss latency and the miss rate of L3 cache, respectively.

A systematic survey on content caching in ICN and ICN-IoT: Challenges, approaches and strategies

Context:Data traffic increased in recent years due to the expansion of IoT (Internet of Things) applications, and most IoT applications follow a content-oriented paradigm. The host-to-host nature of the existing internet architecture makes it inefficient for handling content distribution and information sharing. ICN (Information Centric Network) is a content-centric network that offers guaranteed solutions for data transfer between consumers and producers. ICN caching enables the caching technique in available resources, which can enhance the overall network performance by enabling users to access data within the network. Objective:This study aims to review the literature on caching approaches and strategies in ICN-IoT to identify areas that need more attention in current and future research directions. Method:A systematic mapping study was conducted to identify the relevant literature, and 88 publications that are categorized as caching approaches and caching strategies were chosen as the most relevant studies. Result:Based on caching approaches and caching strategies, the articles are grouped. Edge caching (50%), in-network caching (25%), and hybrid caching (25%) represent the majority of the articles on caching approaches. Popularity-based caching (26%), cooperative caching (17%), collaborative caching (17%), machine learning-based caching (19%), and probabilistic caching (7%) represent the majority of the articles in caching strategies. This study further presents the simulators, performance metrics, parameters and performance evaluation of the existing literature. Conclusion:The results confirm that ICN is a futuristic internet architecture that assigns a content name to each content in the network and enables in-network caching; it is a superior technique for resolving IoT challenges and increases the overall network efficiency.

Cache-MAB: A reinforcement learning-based hybrid caching scheme in named data networks

The presence of content stores in routers enables in-network caching in Named Data Networks (NDN) to improve customer experience, especially when content distribution is considered. Typically, homogeneous caching is assumed in the network where each router operates its cache according to the installed policy. Numerous in-network caching policies that vary in admission logic and caching attributes have been urged. Hence, the policies vary in performance (efficiency) according to unbounded network factors such as caching capacity, users’ request pattern, content popularity distribution, and application type (amongst other contexts). A single scheme may not effectively realize all the network characteristics and vandalize the efficient use of precious cache spaces. Therefore, a new strategy capable of dynamically selecting the optimal caching policy under varying network contexts is inevitable. In this direction, we propose a reinforcement learning (RL)-based hybrid caching strategy, namely Cache-MAB, where the routers work in a distributed manner and learn to pick the most suitable policy for caching a content. The set of policies is formed by picking the policies that assume similar caching attributes for caching decisions. The proposed strategy decouples the caching policy selection from the admission logic used by the selected policy and models the suitable policy selection as a Multi-armed Bandit (MAB) problem. The hybrid strategy equips each router with a diverse set of caching policies and an RL agent that utilizes a reinforcement algorithm to solve the MAB problem and, hence, select the optimal policy in different cases. The optimal policy is the one that maximizes the local performance, for instance, the cache hit rate. Simulation results confirm that the hybrid strategy effectively achieves the optimal policy’s performance, having a performance gap of less than 1%, by adapting to different network scenarios.

Efficient placement and migration policies for an STT-RAM based hybrid L1 cache for intermittently powered systems

The number of battery-powered devices is rapidly increasing due to the widespread use of IoT-enabled nodes in various fields. Energy harvesters, which help to power embedded devices, are a feasible alternative to replacing battery-powered devices. In a capacitor, the energy harvester stores enough energy to power up the embedded device and compute the task. This type of computation is referred to as intermittent computing. Energy harvesters are unable to supply continuous power to embedded devices. All registers and cache in conventional processors are volatile. We require a Non-Volatile Memory (NVM)-based Non-Volatile Processor (NVP) that can store registers and cache contents during a power failure. NVM-based caches reduce system performance and consume more energy than SRAM-based caches. This paper proposes Efficient Placement and Migration policies for hybrid cache architecture that uses SRAM and STT-RAM at the first level cache. The proposed architecture includes cache block placement and migration policies to reduce the number of writes to STT-RAM. During a power failure, the backup strategy identifies and migrates the critical blocks from SRAM to STT-RAM. When compared to the baseline architecture, the proposed architecture reduces STT-RAM writes from 63.35% to 35.93%, resulting in a 32.85% performance gain and a 23.42% reduction in energy consumption. Our backup strategy reduces backup time by 34.46% when compared to the baseline.

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.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Hybrid Cache Management in IoT-Based Named Data Networking

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

Power-optimized Deployment of Key-value Stores Using Storage Class Memory

High-performance flash-based key-value stores in data-centers utilize large amounts of DRAM to cache hot data. However, motivated by the high cost and power consumption of DRAM, server designs with lower DRAM-per-compute ratio are becoming popular. These low-cost servers enable scale-out services by reducing server workload densities. This results in improvements to overall service reliability, leading to a decrease in the total cost of ownership (TCO) for scalable workloads. Nevertheless, for key-value stores with large memory footprints, these reduced DRAM servers degrade performance due to an increase in both IO utilization and data access latency. In this scenario, a standard practice to improve performance for sharded databases is to reduce the number of shards per machine, which degrades the TCO benefits of reduced DRAM low-cost servers. In this work, we explore a practical solution to improve performance and reduce the costs and power consumption of key-value stores running on DRAM-constrained servers by using Storage Class Memories (SCM). SCMs in a DIMM form factor, although slower than DRAM, are sufficiently faster than flash when serving as a large extension to DRAM. With new technologies like Compute Express Link, we can expand the memory capacity of servers with high bandwidth and low latency connectivity with SCM. In this article, we use Intel Optane PMem 100 Series SCMs (DCPMM) in AppDirect mode to extend the available memory of our existing single-socket platform deployment of RocksDB (one of the largest key-value stores at Meta). We first designed a hybrid cache in RocksDB to harness both DRAM and SCM hierarchically. We then characterized the performance of the hybrid cache for three of the largest RocksDB use cases at Meta (ChatApp, BLOB Metadata, and Hive Cache). Our results demonstrate that we can achieve up to 80% improvement in throughput and 20% improvement in P95 latency over the existing small DRAM single-socket platform, while maintaining a 43–48% cost improvement over our large DRAM dual-socket platform. To the best of our knowledge, this is the first study of the DCPMM platform in a commercial data center.

COCKTAIL: Multicore Co-Optimization Framework With Proactive Reliability Management

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power-hungry components of servers (e.g., cores and cache), hence avoiding server failure before its lifetime period. Traditional approaches focus on either using hybrid caches to reduce the leakage power of traditional static random-access memory (SRAM) cache, and thus increase the energy efficiency, or the trade-off between the lifetime and performance of multi-core processors. However, these approaches fall short in terms of flexibility and applicability for HPC tasks in terms of multi-parametric optimization including quality-of-service (QoS), lifetime reliability, and energy efficiency. As a result, in this paper we propose COCKTAIL, a holistic strategy framework to jointly optimize the energy efficiency of multi-core server processors and tasks performance in the HPC context, while guaranteeing the lifetime reliability. First, we analyze the best cache technology among traditional SRAM and resistive random access memory (RRAM), within the context of hybrid cache architectures, to improve the energy efficiency and manage cache endurance limits with respect to tasks requirements. Second, we introduce a novel efficient proactive queue optimization policy to reorder HPC tasks for execution considering their end time and possible reliability effects on the use of the hybrid caches. Third, we present a dynamic model predictive control (MPC)-based reliability management method to maximize task performance, by controlling the frequency, temperature, and target lifetime of the server processor. Our results demonstrate that, while consuming similar energy, COCKTAIL provides up to 60% QoS improvement when compared to latest state-of-the-art energy optimization and reliability management techniques in the HPC context. Moreover, our strategy guarantees a design lifetime longer than 5 years for the whole HPC system.

A Profit-Aware Coalition Game for Cooperative Content Caching at the Network Edge

The user demands to delay-sensitive applications have put forward new requirements for the mobile networks. Edge caching as a promising way is proposed to enhance the Quality of Service (QoS) for end users at the network edge. Given the widely distributed edge nodes, the content providers (CPs) usually prefer to integrate them with cooperative caching services, by forming a cache coalition. Although such a coalition could be beneficial as a whole, it neglects the profits of individual members, which is one major concern in forming the coalition itself. Besides, due to the poor scalability, the conventional cooperation scheme, which only considers fixed edge nodes, cannot adapt to the spatial and temporal imbalance of user requests. In this article, we tackle the problems of coalition establishment and profit allocation among the coalition members. Particularly, by adopting both fixed edge nodes and mobile vehicles as caching nodes, we propose a hybrid service provisioning framework and cooperative service caching and workload scheduling methods. To maximize the profits in managing the caching resources in the established coalition, we devise an optimization model with a mixed-integer programming (MIP), in which the QoS requirements of end users and caching capacities of each coalition member are also considered as constraints. In addition, based on the Hedonic game theory, we propose a dynamic coalition algorithm to guide each member to join or leave the coalition at each time slot out of its own profits. The experimental results demonstrate that compared to the cases only considering fixed caching nodes, our hybrid caching scheme can improve: 1) the overall profit of the coalition by 53% and 2) the average profit of individual participants by 42%, respectively.

Exploiting Data Compression for Adaptive Block Placement in Hybrid Caches

STT-RAM (Spin-Transfer Torque Random Access Memory) appears to be a viable alternative to SRAM-based on-chip caches. Due to its high density and low leakage power, STT-RAM can be used to build massive capacity last-level caches (LLC). Unfortunately, STT-RAM has a much longer write latency and a much greater write energy than SRAM. Researchers developed hybrid caches made up of SRAM and STT-RAM regions to cope with these challenges. In order to store as many write-intensive blocks in the SRAM region as possible in hybrid caches, an intelligent block placement policy is essential. This paper proposes an adaptive block placement framework for hybrid caches that incorporates metadata embedding (ADAM). When a cache block is evicted from the LLC, ADAM embeds metadata (i.e., write intensity) into the block. Metadata embedded in the cache block are then extracted and used to determine the block’s write intensity when it is fetched from main memory. Our research demonstrates that ADAM can enhance performance by 26% (on average) when compared to a baseline block placement scheme.

Cache-Mab: A Reinforcement Learning-Based Hybrid Caching Scheme in Named Data Networks Networks

Cache-Mab: A Reinforcement Learning-Based Hybrid Caching Scheme in Named Data Networks Networks

Cache-Mab: A Reinforcement Learning-Based Hybrid Caching Scheme in Named Data Networks

Cache-Mab: A Reinforcement Learning-Based Hybrid Caching Scheme in Named Data Networks

Exploiting bit-level write patterns to reduce energy consumption in hybrid cache architecture

Exploiting bit-level write patterns to reduce energy consumption in hybrid cache architecture

Enhancing Security in Key Management Using Hybrid Cache Supported One Way Hash Chain Technique

Background: Collusion resistance is preserving the value generated at individual nodes secret so that if any nodes share the information together or collude with each other, the secret values cannot be revealed.It is really a challenging task to securely resist the collusion of cloud server and users Collusion resistance also involves growing the number of people. Objective: Users can achieve an effective and economical approach for data sharing among group members in the cloud with the characters of low maintenance and little management cost. Method: Hybrid Cache supported One way hash chain Technique. Result: To test with the sparse caching HHCS and one-way hash chain model and the various sparse caching configurations are fully implemented by this work. Multiple situations and various scenarios are considered in the experiments and tests for the estimation of HHCS’ performance. The results which are measured in terms of storage units needed for the completion of one internet session and in terms of efficiency (which is the number of hash operations done in one session) are compared and then contrasted. Conclusion: Security improvement is proposed in this paper in group key management method, in order to solve the issue of collusion attack between whole members of the group for a secure group communication. It is shown that using lightweight is easy for the implementation of sparse caching method which can greatly improve the cryptographic one-way hash chain method’s performance which is used widely, for securing the transmission of data in cloud by using two levels of security scheme. A memory-times computation complexity metric has been introduced to help choose the best size of cache depending on storage requirements of the cloud application. For demonstrating various connection behaviors, various cache spacing methods have been evaluated. This paper presented and evaluated the potential of hybrid solution in which one-way hash chains which are divided are provided with caching capacities in order to store the values of session key and retrieve them when needed for authenticating a user session. Lower computational overhead is achieved by us as measured by the count of hash operations and computation time needed to give protection.

A Novel Hybrid Cache Coherence with Global Snooping for Many-core Architectures

Cache coherence ensures correctness of cached data in multi-core processors. Traditional implementations of existing protocols make them unscalable for many core architectures. While snoopy coherence requires unscalable ordered networks, directory coherence is weighed down by high area and energy overheads. In this work, we propose Wireless-enabled Share-aware Hybrid (WiSH) to provide scalable coherence in many core processors. WiSH implements a novel Snoopy over Directory protocol using on-chip wireless links and hierarchical, clustered Network-on-Chip to achieve low-overhead and highly efficient coherence. A local directory protocol maintains coherence within a cluster of cores, while coherence among such clusters is achieved through global snoopy protocol. The ordered network for global snooping is provided through low-latency and low-energy broadcast wireless links. The overheads are further reduced through share-aware cache segmentation to eliminate coherence for private blocks. Evaluations show that WiSH reduces traffic by and runtime by , while requiring smaller storage and lower energy as compared to existing hierarchical and hybrid coherence protocols. Owing to its modularity, WiSH provides highly efficient and scalable coherence for many core processors.