To satisfy the huge storage capacity requirements of big data, the emerging high-density disks gradually adopt the Shingled Magnetic Recording (SMR) technique. However, the most serious challenge of SMR disks lies in ...
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ISBN:
(纸本)9781450371025
To satisfy the huge storage capacity requirements of big data, the emerging high-density disks gradually adopt the Shingled Magnetic Recording (SMR) technique. However, the most serious challenge of SMR disks lies in their weak fine-grained random write performance caused by the write amplification inner SMRs and its extremely unbalanced read and write latencies. Although fast storage devices like Flash-based SSDs can be used to boost SMR disks in SMR-based hybrid storage, the optimization targets of existing cache algorithms (e.g., higher popularity for LRU, lower SMR write amplification ratio for MOST) are NOT the crucial factor for the performance of the SMR-based hybrid storage. In this paper, we propose a new SMR-Aware Co-design cache algorithm called SAC to accelerate the SMR-based hybrid storage. SAC adopts a hardware/software co-design method to fit the characteristics of SMR disks and to optimize the crucial factor, i.e., RMW operations inner SMR disks, effectively. Furthermore, SAC also makes a good balance between some conflicting factors, e.g., the data popularity vs. the SMR write amplification and clean cache space vs. dirty cache space. In our evaluations under real-world traces, SAC achieves a 7.5. performance speedup compared with LRU in the write-only mode, and a 2.9. speedup in the read-write mixed mode.
Flash-based Solid State Drive (SSD) has limitations in terms of cost and lifetime. It is used as a second-level cache between main memory and traditional HDD-based storage widely. Adopting traditional cache algorithms...
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ISBN:
(纸本)9781467378918
Flash-based Solid State Drive (SSD) has limitations in terms of cost and lifetime. It is used as a second-level cache between main memory and traditional HDD-based storage widely. Adopting traditional cache algorithms, which are designed primarily depending on temporal locality and popular blocks, to SSD-based second-level disk cache can cause unnecessary cache replacements, which not only degrade the cache performance but also shorten the lifetime of SSD. To overcome this problem, this paper proposes a performance-effective Regional Popularity-Aware cache replacement algorithm (RPAC). Instead of a single block, the popularity of a region which is constituted by many adjacent disk blocks is recorded and used to determine replacing a block or not. In this way, the spatial locality of disk access is completely leveraged and sequential I/O blocks are gathered in SSD cache. Furthermore, it reduces the number of unnecessary cache replacement and erasure operation on SSD, prolonging its lifetime. We have implemented RPAC in real system and evaluated it by many workloads. Compared to traditional cache algorithms, it improve I/O throughput by up to 53% and reduce cache replacements of SSD up to 98.5%.
Caching plays a crucial role in many latency-sensitive systems, including content delivery networks, edge computing, and microprocessors. As the ratio between system throughput and transmission latency increases, dela...
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Caching plays a crucial role in many latency-sensitive systems, including content delivery networks, edge computing, and microprocessors. As the ratio between system throughput and transmission latency increases, delayed hits in cache problems become more prominent. In real-world scenarios, object access patterns often exhibit a non-stationary nature. In this paper, we investigate the latency optimization problem for caching with delayed hits in a non-stationary environment, where object sizes and fetching latencies are both non-uniform. We first find that given known future arrivals, evicting the object with the larger size, a higher aggregate delay due to miss and arriving the farthest in the future brings more gains in reducing latency. Following our findings, we design an online learning framework to make cache decisions more effectively. The first component of this framework utilizes historical data within the training window to estimate the object's non-stationary arrival process, modeled as a mixture of log-gaussian distributions. Subsequently, we predict future arrivals based on this estimated distribution. According to these predicted future arrivals, we can determine the priority of eviction candidates using our defined rank function. Experimental results on four real-world traces show that our algorithm consistently reduces latency by 2%- 10% on average compared to state-of-the-art algorithms.
Over the last few years, hybrid solid-state drives (SSDs) have been widely adopted due to their high performance and high capacity. Devices equipped with hybrid SSDs call be utilized to cache files from the network fo...
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ISBN:
(纸本)9781665432191
Over the last few years, hybrid solid-state drives (SSDs) have been widely adopted due to their high performance and high capacity. Devices equipped with hybrid SSDs call be utilized to cache files from the network for performance improvement. However, this paper finds an interesting observation, that is, the efficiency of hybrid SSDs is significantly degraded instead of improved when too much data is cached. This is because the internal mode switching between different types of Hash memory is affected by the device utilization. This paper proposes a dynamic file cache optimization scheme for hybrid SSDs, DFcache, which optimizes the device's efficiency and limits unreasonable space consumption. DFcache includes two key ideas, dynamic cache space management, and intelligent cache file sifting. DFcache is implemented in Linux kernel and tested under real hybrid SSDs. Experimental results show that the I/O performance outperforms the state-of the-art by up to 3.7x.
Solid State Drives (SSDs) are popularly used for caching in large scale cloud storage systems nowadays. Traditionally, most cache algorithms make replacement upon each miss when cache space is full. However, we observ...
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Solid State Drives (SSDs) are popularly used for caching in large scale cloud storage systems nowadays. Traditionally, most cache algorithms make replacement upon each miss when cache space is full. However, we observe that in a typical Cloud Block Storage (CBS) system, there is a great percentage of blocks with large reuse distances, which would result in large number of blocks being evicted out of the cache before they ever have a chance to be referenced while they are cached, significantly jeopardizing the cache efficiency. In this article, we propose LEA, Lazy Eviction cache algorithm, for cloud block storage to efficiently remedy the cache inefficiencies caused by cache blocks with large reuse distances. LEA mainly employs two lists, Lazy Eviction List (LEL) and Block Identity List (BIL), which keep track of two types of victim blocks respectively based on their cache duration when replacements occur, to improve cache efficiency. When a cache miss happens, if the victim block has not resided in cache for longer than its reuse distance, LEA inserts the missed block identity into BIL. Otherwise, it inserts the missed block entry into LEL. We have evaluated LEA by using IO traces collected from Tencent, one of the largest network service providers in the world, and several open source traces. Experimental results show that LEA not only outperforms most of the state-of-the-art cache algorithms in hit ratio, but also greatly reduces the number of SSD writes.
Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive ...
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Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive and latency-sensitive applications on the eve of the large-scale commercial operation of 5G. However, the slow-start phenomenon incurred by existing request history-based caching strategies limits the performance of wireless edge caching, especially in the dynamic scenario where both mobile devices and contents arrive and leave periodically. On the other hand, it is also a hard task for deep reinforcement learning-based methods to adapt to the dynamics of the environment. In this backdrop, a new caching algorithm, called Similarity-Aware Popularity-based Caching (SAPoC), is presented in this paper to promote the performance of wireless edge caching in dynamic scenarios through utilizing the similarity among contents. In SAPoC algorithm, a content?s popularity is determined by not only its requests history but also its similarity with existing popular ones to enable a quick-start of newly arrived contents. A series of simulation experiments are conducted to evaluate SAPoC algorithm?s performance. Results have shown that SAPoC outperforms several typical proposals in both cache hit ratio and energy consumption.
Mobile edge computing (MEC) can greatly reduce the latency experienced by mobile devices and their energy consumption through bringing data processing, computing, and caching services closer to the source of data gene...
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ISBN:
(纸本)9781450379564
Mobile edge computing (MEC) can greatly reduce the latency experienced by mobile devices and their energy consumption through bringing data processing, computing, and caching services closer to the source of data generation. However, existing edge caching mechanisms usually focus on predicting the popularity of contents or data chunks based on their request history. This will lead to a slow start problem for the newly arrived contents and fail to fulfill MEC's context-aware requirements. Moreover, the dynamic nature of contents as well as mobile devices has not been fully studied. Both of them hinder the further promotion and application of MEC caching. In this backdrop, this paper aims to tackle the caching problem in wireless edge caching scenarios, and a new dynamic caching architecture is proposed. The mobility of users and the dynamics nature of contents are considered comprehensively in our caching architecture rather than adopting a static assumption as that in many current efforts. Based on this framework, a Similarity-Aware Popularity-based Caching (SAPoC) algorithm is proposed which considers a content's freshness, short-term popularity, and the similarity between contents when making caching decisions. Extensive simulation experiments have been conducted to evaluate SAPoC's performance, and the results have shown that SAPoC outperforms several typical proposals in both cache hit ratio and energy consumption.
This paper proposes a reliability-oriented planning method to design reliable topologies for meshed high-voltage direct-current (HVdc) grids. Based on the proposed steady-state model for HVdc grids, a bi-level and mul...
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This paper proposes a reliability-oriented planning method to design reliable topologies for meshed high-voltage direct-current (HVdc) grids. Based on the proposed steady-state model for HVdc grids, a bi-level and multiobjective planning problem is formulated. The optimization model not only regards reliability as an independent objective, but also takes the power flow controllers (PFCs) into account. Compared with conventional methods, it overcomes the curse of dimensionality and solves the optimal allocation of PFCs. Then, the nondominated sorting genetic algorithm II is employed to solve the upper-level problem. For lower-level problems, an algorithm based on minimum spanning trees is proposed to optimally allocate PFCs, and an improved least frequently used cache algorithm and an optimum-test algorithm are developed to promote the computing efficiency of reliability evaluation. The European Supergrid and a Chinese ultra-HVdc system are adopted as test systems to validate the proposed method. Case studies prove that the proposed method provides an effective tool for the planning of HVdc grids. Also, results show that the cache technique and the optimum-test algorithm can reduce more than 70% of the total elapsed time.
This paper presents a new adaptive probabilistic cache algorithm (AProb) for modern caching networks. AProb is based on three main techniques: (1) dynamic probabilistic caching;(2) ghost list;and (3) adaptive probing ...
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This paper presents a new adaptive probabilistic cache algorithm (AProb) for modern caching networks. AProb is based on three main techniques: (1) dynamic probabilistic caching;(2) ghost list;and (3) adaptive probing and protection. It enables caching systems to quickly adjust their cached data to dynamic content popularity without intervention of network administrators and synchronization. The criteria of this adjustment are based on hit events occurring in AProb data structures. By using AProb, a caching system continuously adapts a caching probability and the ratio between probing and protection partitions of its cache. AProb has constant time complexity and its space overhead is minimal. Extensive computer simulations, which consider various network topologies and traffic traces, show that AProb offers improvement in terms of server-hit ratio, footprint distance, and caching time compared with those provided by several existing cache algorithms.
Solid State Drives (SSDs) are popularly used for caching in large scale cloud storage systems nowadays. Traditionally, most cache algorithms make replacement at each miss when cache space is full. However, we observe ...
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ISBN:
(纸本)9781538684771
Solid State Drives (SSDs) are popularly used for caching in large scale cloud storage systems nowadays. Traditionally, most cache algorithms make replacement at each miss when cache space is full. However, we observe that in a typical Cloud Block Storage (CBS), there is a great percentage of blocks with large reuse distances, which would result in large number of blocks being evicted out of the cache before they ever have a chance to be referenced while they are cached, significantly jeopardizing the cache efficiency. In this paper, we propose LEA, Lazy Eviction cache algorithm, for cloud block storage to efficiently remedy the cache inefficiencies caused by cache blocks with large reuse distances. Specifically, LEA uses two lists, Lazy Eviction List (LEL) and Block Identity List (BIL). When a cache miss happens, if the candidate evicted-block has not resided in cache for longer than its reuse distance, LEA inserts the missed block identity into BIL. Otherwise, it inserts the missed block entry into LEL. We have evaluated LEA by using IO traces collected from Tencent, one of the largest network service providers in the world, and several open source traces. Experimental results show that LEA not only outperforms most of the state-of-the-art cache algorithms in hit ratio, but also reduces the number of SSD writes greatly.
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