Changes In Access Patterns Research Articles

Access for sale? Overlying rights, land transactions, and groundwater in California

Climate change intensifies longstanding tensions over groundwater sustainability and equity of access among users. Though private land ownership is a primary mechanism for accessing groundwater in many regions, few studies have systematically examined the extent to which farmland markets transform groundwater access patterns over time. This study begins to fill this gap by examining farmland transactions overlying groundwater from 2003–17 in California. We construct a novel dataset that downscales well construction behavior to the parcel level, and we use it to characterize changes in groundwater access patterns by buyer type on newly transacted parcels in the San Joaquin Valley groundwater basin during the 2011–17 drought. Our results demonstrate large-scale transitions in farmland ownership, with 21.1% of overlying agricultural acreage statewide sold at least once during the study period and with the highest rates of turnover occurring in critically overdrafted basins. By 2017, annual individual farmland acquisitions had halved, while acquisitions by limited liability companies increased to one-third of all overlying acres purchased. Together, these trends signal increasing corporate farmland acquisitions; new corporate farmland owners are associated with the construction, on comparable parcels, of agricultural wells 77–81 feet deeper than those drilled by new individual landowners. We discuss the implications of our findings for near-term governance of groundwater, and their relevance for understanding structural inequities in exposure to future groundwater level declines.

RHPM: Using Relative Hotness to Guide Page Migration for Hybrid Memory Systems

Modern computing systems and data-intensive applications are eager for larger and faster memory. Building hybrid memory systems with different memory technologies has become a dominant trend to satisfy these demands. For hybrid memory systems, page migration schemes that dynamically migrate frequently accessed hot pages into faster memory are crucial for improving performance. However, existing migration schemes are either too aggressive, resulting in unnecessary extra traffic, or too conservative to quickly adapt to changes in access patterns. Besides, the extra latency introduced by querying metadata is often ignored or handled in an unscalable manner. In this paper, we propose a Relative Hotness Page Migration (RHPM) strategy, which discovers hot pages in a set of pages by competing with each other rather than comparing with a threshold. The migration is performed only when a new page wins the competition. To overlap latency due to access metadata, RHPM fetches metadata and data in parallel. In addition, it enables a small metadata buffer to speed up metadata access. Compared to the state-of-the-art scheme, RHPM requires only 1/512 of the on-chip capacity, significantly reducing on-chip hardware overhead. Evaluation of RHPM with simulations of 25 workloads shows that RHPM outperforms the state-of-the-art scheme by an average of 13.34% in performance and saves 44.19% on energy, demonstrating better resilience to changes in access patterns.

Exploiting temporal changes in query submission behavior for improving the search engine result cache performance

A commonly used technique for improving query response time in retrieval systems is storing query-relevant information in a fast access memory storage such as a result cache. The effectiveness of the result cache heavily relies on giving correct admission and eviction decisions, which require careful analysis of query and stream characteristics. Due to anonymous and global user access patterns, search engines are often considered time-invariant architectures: query characteristics are frequently collected globally, and assumed to be unchanging for long periods of time. However, the highly distributed nature of the modern search engine framework consequently led to noticeable temporal changes in user access patterns through short periods of time for each of the data center within the distributed network.The work presented here attempts to evaluate temporal variations in query submissions and exploit them in order to improve the result caching performance. To this end, query logs are analyzed in order to verify the availability of such variations and a new caching framework that facilitate these changes is proposed. The proposed framework partitions the result cache into three segments: a static segment that stores the most frequent queries in an offline fashion, a newly introduced semi-static segment on top of the state-of-the-art Static–Dynamic Cache (SDC) that changes content during different periods of the day, and a dynamic segment that is maintained in an Least Recently Used (LRU) fashion. Conducted experiments demonstrate that the proposed caching framework improves the hit rate of a search engine result cache up to 3.31% and query response time up to 7.27% with respect to the state-of-the-art techniques.

Exploring Memory Access Patterns for Graph Processing Accelerators

Recent trends in business and technology (e.g., machine learning, social network analysis) benefit from storing and processing growing amounts of graph-structured data in databases and data science platforms. FPGAs as accelerators for graph processing with a customizable memory hierarchy promise solving performance problems caused by inherent irregular memory access patterns on traditional hardware (e.g., CPU). However, developing such hardware accelerators is yet time-consuming and difficult and benchmarking is non-standardized, hindering comprehension of the impact of memory access pattern changes and systematic engineering of graph processing accelerators. In this work, we propose a simulation environment for the analysis of graph processing accelerators based on simulating their memory access patterns. Further, we evaluate our approach on two state-of-the-art FPGA graph processing accelerators and show reproducibility, comparablity, as well as the shortened development process by an example. Not implementing the cycle-accurate internal data flow on accelerator hardware like FPGAs significantly reduces the implementation time, increases the benchmark parameter transparency, and allows comparison of graph processing approaches.

Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review.

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.

Hardware-Level Thread Migration to Reduce On-Chip Data Movement Via Reinforcement Learning

As the number of processing cores and associated threads in chip multiprocessors (CMPs) continues to scale out, on-chip memory access latency dominates application execution time due to increased data movement. Although tiled CMP architectures with distributed shared caches provide a scalable design, increased physical distance between requesting and responding cores has led to both increased on-chip memory access latency and excess energy consumption. Near data processing is a promising approach that can migrate threads closer to data, however prior hand-engineered rules for fine-grained hardware-level thread migration are either too slow to react to changes in data access patterns, or unable to exploit the large variety of data access patterns. In this article, we propose to use reinforcement learning (RL) to learn relatively complex data access patterns to improve on hardware-level thread migration techniques. By utilizing the recent history of memory access locations as input, each thread learns to recognize the relationship between prior access patterns and future memory access locations. This leads to the unique ability of the proposed technique to make fewer, more effective migrations to intermediate cores that minimize the distance to multiple distinct memory access locations. By allowing a low-overhead RL agent to learn a policy from real interaction with parallel programming benchmarks in a parallel simulator, we show that a migration policy which recognizes more complex data access patterns can be learned. The proposed approach reduces on-chip data movement and energy consumption by an average of 41%, while reducing execution time by 43% when compared to a simple baseline with no thread migration; furthermore, energy consumption and execution time are reduced by an additional 10% when compared to a hand-engineered fine-grained migration policy.

Towards cost-effective service migration in mobile edge: A Q-learning approach

Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration while minimizing its cost across the edge nodes, in this paper, we leverage reinforcement learning (RL) method to design a cost-effective framework, called Mig-RL, for the service migration with a reduction of total service costs as a goal in a mobile edge environment. The Mig-RL leverages the infrastructure of edge network and deploys a migration agent through Q-learning to learn the optimal policy with respect to the service migration status. We distinguish the Mig-RL from other existing works in several major aspects. First, we fully exploit the nature of this problem in a modest migration space, which allows us to constrain the number of service replicas whereby a defined state–action space could be effectively handled, as opposed to those methods that need to always approximate a huge state–action space for policy optimality. Second, we advocate a migration policy-base as a cache to save the learning process by retrieving the most effective policy whenever a similar migration pattern is encountered as time goes on. Finally, by exploiting the idea of software defined network, we also investigate the efficient implementation of Mig-RL in mobile edge network. Experimental results based on some real and synthesized access sequences show that Mig-RL, compared with the selected existing algorithms, can substantially minimize the service costs, and in the meantime, efficiently improve the QoS by adapting to the changes of mobile access patterns.

GCRFP - PAGE REPLACEMENT FOR SOLID STATE DRIVE USING GHOST-CACHE

State Drive (SSD) is an alternative to data storage that is popular today, widely used as a media cache to speed up data access to the hard disk (HDD). This paper proposes page replacement technique on SSD cache that used frequency and recency parameter, alternately. The algorithm is selected adaptively based on trace input. This method helps to overcome changes in access patterns while minimizing the number of write processes to SSD. The proposed algorithm can choose a replacement technique that suits the user access pattern so that it can bring a better hit rate. The proposed algorithm is also integrated with the ghost-cache mechanism so that the reduction in the number of writing processes to SSD is significant. The experiment runs using a real dataset, describing trace of data read, and data write taken from real usage. The trial shows that the proposed algorithm can give good results compared to other similar algorithms.

SAL-Hashing: A Self-Adaptive Linear Hashing Index for SSDs

Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing ( SAL-hashing ) to reduce small random-writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize the buckets of a linear hashing index into groups and sets to facilitate coarse-grained writes and adaptivity to access patterns. A group consisting of a fixed number of buckets is proposed to transform small random writes to buckets into coarse-grained writes and in turn improve write performance of the index. A set consists of a number of groups, and we propose to employ different split strategies for each set. With this mechanism, SAL-hashing is able to adapt to the changes of access patterns. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Fourth, we propose a new technique called virtual split to optimize the search performance of SAL-hashing . Finally, we propose a new scheme for the management of the log buffer. We conduct extensive experiments on real SSDs. The results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads.

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.

Efficient cache resource aggregation using adaptive multi-level exclusive caching policies

Multi-level buffer cache hierarchies are now commonly seen in most client/server cluster configurations, especially in today’s big data application deployment. However, multi-level caching policies deployed so far typically use independent cache replacement algorithms in each level, which has two major drawbacks: (1) File blocks may be redundantly cached on multiple levels, reducing the actual aggregate cache usable size; (2) Less accurate replacement decisions at lower level caches due to weakened locality. Inefficient cache resource usage may result in noticeable performance degradation for big data applications.To address these problems, we propose new adaptive multi-level exclusive caching policies that can dynamically adjust replacement and placement decisions in response to changing access patterns. (1) First, to capture locality information in multi-level cache hierarchies, we propose a Reuse Distance based Adaptive Replacement Caching (ReDARC) algorithm that adopts reuse distance as the means of locality measure and adaptively balances between the Small Reuse Distance (SRD) set and Large Reuse Distance (LRD) set. (2) Second, to achieve exclusive caching and make global caching decisions, we propose an Adaptive Level-Aware Caching Algorithm (ALACA) that works collaboratively with ReDARC. The ALACA algorithm uses an adaptive probabilistic PUSH technique that allows lower caches to push blocks to higher caches and appropriately decide blocks’ caching locations with the ReDARC algorithm. In this way, we achieve multi-level exclusive caching with significant cache performance improvement. Our trace-driven simulation experiments show that the policies we proposed achieve a reduction of the client average response time of 8 percent to 56 percent over other multi-level cache schemes.

On Service Migrations in the Cloud for Mobile Accesses

We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service and cost effectiveness. Moving the service closer to the client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, these benefits are not free. The migration comes at a cost of bulk-data transfer and service disruption, and hence, increasing the overall service costs. To gain the benefits of service migration while minimizing the caused monetary costs, we propose an efficient search-based algorithm Dmig to migrate a single server, and then extend it as a scalable algorithm, called mDmig , to the multi-server situation, a more general case in the cloud. Both algorithms are fully distributed, symmetric, and characterized by the effective use of historical access information to conduct virtual migration so that the limitations of local search in the cost reduction can be overcome. To evaluate the algorithms, we compared them with some existing algorithms and an off-line algorithm. Our simulation results showed that the proposed algorithms exhibit better performance in service migration by adapting to the changes of mobile access patterns in a cost-effective way.

Real-time data replication strategy for data grids

Current replication strategies update replicas by analyzing the data access pattern for a certain period of time. Therefore, the access latency temporarily increases due to delayed responses to changing data access pattern during the interval of updating replicas. Thus, we propose a real-time data replication strategy that can update a replica by reflecting changes in the data access patterns in real time to solve this problem. The proposed replication strategy introduces a priority value (PV) as a criterion to generate and delete replicas in real time. The PV is calculated based on the previous data access patterns and is updated in real time. We perform simulations to evaluate the performance of the proposed replication strategy by comparing it with the popular file replicate first algorithm (PFRF). The simulation results show that the average job execution time of the proposed strategy is shorter than that of the PFRF by 34.0---38.0%.

A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand

Understanding program behavior is at the foundation of computer architecture andprogram optimization. Programs pass through different behaviors where theirperformance characteristics and hardware resource requirements vary. Programphase detection and classification research aiming to understand the program timevaryingbehavior, can unlock a lot of phase-based optimizations which are speciallytailored to improve the performance of each individual program phase. In this paper,we introduce an efficient run-time phase detection and classification technique, basedon tracking changes in the L2 cache access pattern of different portions in theprogram execution. The proposed technique monitors a running program and keepstrack what phase the running program is currently executing, with no need torecompile the tracked program, and with execution time overhead of 4%, on average.Performance Monitoring Unit (PMU) is exploited to sample the memory addressescausing L1 data cache misses. This profiling data is used to construct the CacheAccess Signature Vectors (CASVs) that accurately reflect the L2 cache accesspatterns for each interval of execution. By comparing CASVs, the proposedtechnique classifies the program into a set of stable phases with high degree of intraphasehomogeneity. Our evaluation shows that phase changes detected by ourtechnique have strong correlation with the variation in Instruction Per Cycle (IPC).Furthermore, our technique can contribute in reducing L2 cache miss rates, andoptimizing L2 cache utilization, through its direct capability of estimating per-phaseL2 cache demand

Editorial

EditorialOn behalf of the editorial team, with immense pleasure, I present this issue of USHUS – Journal of Business Management (UJBM). This issue comprises of contemporary articles which are very pertinent in the changing business environment.Given the controversies that surfaced in the year 2015 on the Indian food industry on the whole, especially noodles, the article on "Are we nodding for Noodles? - An empirical evidence among the Bengaluru youth" addresses certain critical facts that can be useful to the manufacturers in mapping the buying behaviour of the consumers. The authors have explored the consumers' acceptance level of the Ready-to-cook and packaged food. Interestingly, the study finds that despite the allegations, popular brands such as Maggi, Yippee, etc. still perform well, and they do have the potential to capture larger market share.The article on "Marketing of Organic Food Products at Madurai City" investigates the increasing demand for organic food products. This article throws light on how the retailers can design their marketing strategies to attract people to buy organics food products and thereby increase the cultivation of the same. With increasing urbanisation and rise in health consciousness among the people, this article indicates that the demand for organic food products is only going to see an upswing.As the countries are preparing to embrace a paperless economy, the article titled "Futuristic perspective of online buyers for sustainability of E-Commerce" explores the ways and means of sustaining E-Commerce business. The study traces the growth of E-Commerce business in India. It primarily focuses on the different perspectives of the online buyers.Changes in lifestyle patterns and the ubiquitous presence of sophisticated technology has led to the emergence of a new breed of citizens who are extremely tech savvy. "Changing Access Patterns of Tech Savvy Citizens" sheds light on the different ways in which digital technology consumers use modern technology.

A New Approach to Re-Fragment in a Distributed Database Environment

The key idea of this paper is to present a novel method for Re-fragmentation in a Distributed Database System that ensures that the Database System is adaptive to the changes in access frequency patterns. Conventionally, fragmentation is done to reduce network transfer cost and communication costs. But with time, changing access patterns at the sites defies the very purpose of fragmentation. In this paper, we have presented an algorithm to maximize and maintain the efficiency of fragmentation in a Distributed Database System. This algorithm takes into account the change in the access patterns and queries; evaluates and quantifies them and performs re-fragmentation periodically according to the updated access frequency patterns. This helps in sustaining the efficiency of fragmentation irrespective of the changing access patterns. The technique used for studying the efficiency of the new algorithm was to employ the new algorithm on a sample Distributed Database system and compare the same with the existing conventional algorithm. The performance of a Distributed Database system is inversely proportional to its communication cost and execution time of queries1,2. On analysis of the presented algorithm, we see that the time complexity of this algorithm is linear. Also the total Network Transfer Cost is considerably lesser than the conventional fragmentation algorithm for Distributed Database. The algorithm presented in the paper is applicable to all Distributed Database systems, which have changing access frequency patterns

Changing Access Patterns of Tech Savvy Citizens

This paper attempts to reveal the impact of modern digital business models on the changing buyer behavior of the tech savvy consumers. An empirical research was undertaken with a sample size of 100 and the responses are analyzed to give an overview on the major factors that influence buyers to shift from traditional or conventional shopping. The study reveals that more and more people prefer online shopping over conventional shopping. Present investigation reveals that consumers have an overall positive image towards digitalization while making a purchase

Improving last level shared cache performance through mobile insertion policies (MIP)

We show the high variability in last-level cache access patterns.When applications interact, current dynamic policies have not enough flexibility.We present MIP, a replacement policy based on mobile insertion position.MIP rapidly adapts to sudden changes in access patterns during application runtime.Our solution provides 30% better hit-rate than LRU and 10% than DRRIP. For those cache hierarchy levels where program locality is not as evident as in L1, LRU replacement does not seem to be the optimal solution to determine which blocks will be requested soon. The literature is prolific on alternative reuse-distance estimations at last on-chip cache level, proving the difficulty of achieving an optimal hit rate. One of the key aspects for performance is knowing inter and intra application reuse-distance variability. Many solutions already do this, but most of them rely on a simple choice among a few alternative policies. The experiments performed to motivate the proposal confirm application variability, but also show that the behavior of applications is much more than bimodal. This means that there is a performance gap that current hybrid policies are not able to cover. In this paper we propose a mobile insertion position replacement policy (MIP), which combines well known LRU ordering and promotion policies with a completely adaptive insertion mechanism. The dynamic behavior of insertion is able to capture hit-rate variability in a more accurate way. Making use of set dueling and dynamic set sampling for prediction, our mechanism continuously estimates the insertion position that maximizes the cache hit rate. The hardware overhead compared to a LRU replacement algorithm is merely three 3-bit saturating counters per LLC bank. Our experiments show that for a wide range of applications, MIP is able to improve the hit rate of LRU by 30% on average. MIP outperforms current state-of-the-art replacement policies with a similar implementation cost by 10% on average and in single-thread or multi-thread workloads by 20%.

Editorial

On behalf of the editorial team, with immense pleasure, I present this issue of USHUS – Journal of Business Management (UJBM). This issue comprises of contemporary articles which are very pertinent in the changing business environment. Given the controversies that surfaced in the year 2015 on the Indian food industry on the whole, especially noodles, the article on "Are we nodding for Noodles? - An empirical evidence among the Bengaluru youth" addresses certain critical facts that can be useful to the manufacturers in mapping the buying behaviour of the consumers. The authors have explored the consumers' acceptance level of the Ready-to-cook and packaged food. Interestingly, the study finds that despite the allegations, popular brands such as Maggi, Yippee, etc. still perform well, and they do have the potential to capture larger market share. The article on "Marketing of Organic Food Products at Madurai City" investigates the increasing demand for organic food products. This article throws light on how the retailers can design their marketing strategies to attract people to buy organics food products and thereby increase the cultivation of the same. With increasing urbanisation and rise in health consciousness among the people, this article indicates that the demand for organic food products is only going to see an upswing. As the countries are preparing to embrace a paperless economy, the article titled "Futuristic perspective of online buyers for sustainability of E-Commerce" explores the ways and means of sustaining E-Commerce business. The study traces the growth of ECommerce business in India. It primarily focuses on the different perspectives of the online buyers. Changes in lifestyle patterns and the ubiquitous presence of sophisticated technology has led to the emergence of a new breed of citizens who are extremely tech savvy. "Changing Access Patterns of Tech Savvy Citizens" sheds light on the different ways in which digital technology consumers use modern technology.

Transaction Management Techniques and Practices in Current Cloud Computing Environments : A Survey

Distributed database management systems (DBMSs)have limited capacity to manage overhead of distributed transactions due to certain failures. These types of failures don't guarantee the enforcement of transactional properties and which reduces the scalability and availability of these systems. Research in this area proved that scaling out whiling providing consistency of transactions and guaranteeing availability is the presence of different failures in distributed computing environment is not feasible. As a result, the database community have used vertical scaling rather than horizontal scaling. Moreover changes in data access patterns resulting from new generation of web applications with high scalability and availability guarantees weaker consistency. In this paper we have analyses that how different systems such as ElasTras, Mstore, Sinfonia, ecStore and Gstore provide scalable transactional data access in cloud computing environment. We have also discussed transactions management (TM) in Gstore in detail and compared with other techniques and protocols used in current cloud based systems.