Independent Reference Model Research Articles

Performance Analysis of Heterogeneous Cellular Caching Networks With Overlapping Small Cells

Caching at network edges has attracted more and more research interests recently for the purpose of alleviating the network traffic pressure especially in backhaul links and improving user experience. We study Heterogeneous Cellular Caching Networks (HCCNs) consisting of macro cells in which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> small cell base stations (SBSs) equipped with cache memory operate in conjunction with the macro cell base station (MBS). We provide closed-form expressions of the MBS and SBSs utilization factors and average user-experienced-delay in HCCNs with overlapping coverage regions, considering general traffic models for the request arrivals based on the Independent Reference Model (IRM) and renewal traffic models. Moreover, we propose a novel caching scheme in HCCNs, namely Cooperative Most Popular Caching (CMPC), which outperforms the existing schemes in terms of delay. Subsequently, we present the bandwidth assignment problem aiming to minimize the average user-experienced-delay under stability and cache size constraints in HCCNs with overlapping coverage regions and stochastic request arrivals. Finally, the analytic results are validated through numerical results and real trace-driven experiments.

Caching Policies for Delay Minimization in Small Cell Networks With Coordinated Multi-Point Joint Transmissions

In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1/2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (non-stationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.

Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity

Caching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from caching. In this paper, we resort to a hybrid proactive and reactive policy to deal with the dynamics of popularity, in particular the hybrid of proactive probabilistic caching policy and least recently used policy, which are appropriate respectively for the contents with low and high dynamic popularity. We divide the contents requested in a region into two classes, where one can be modeled by independent reference model (IRM) and the other can be modeled by shot noise model (SNM). To maximize the total successful offloading ratio achieved by caching the two classes of contents, we optimize the hybrid caching policy including both the cache resource allocation to each class of contents and the probability of caching each IRM content. We find the optimal solution to the problem for general case, and provide a closed-form solution in a special case to gain insights. To provide a viable solution for practical use, we propose a heuristic method to obtain the optimal allocation fraction, and predict the popularity distribution and the allocation fraction using neural networks with historical data. We validate our analytical results by simulation results via synthetic datasets. We evaluate the performance of the proposed hybrid caching policy via synthetic data generated by SNM and two real datasets, and compare it with the proactive policy, the reactive policy and the existing hybrid proactive and reactive policy.

MRAS speed observer for sensorless adaptive intelligent backstepping controller of induction machines

In this paper, a sensorless adaptive neuro-fuzzy backstepping control scheme is developed for induction machines with unknown model, uncertain load-torque and nonlinear friction where the speed is obtained using the model reference adaptive system (MRAS). Neurofuzzy systems are used to online approximate the uncertain nonlinearities and an adaptive backstepping technique is employed to systematically construct the control law. The MRAS observer is based on two different models, one that is speed independent (reference model) and another that is speed dependent (adjustable model). The speed is estimated via the outputs of the two models. The proposed sensorless controller guarantees the tracking errors converge to a small neighbourhood of the origin and the boundedness of all closed-loop signals what demonstrate the effectiveness of the proposed approach.

MRAS speed observer for sensorless adaptive intelligent backstepping controller of induction machines

In this paper, a sensorless adaptive neuro-fuzzy backstepping control scheme is developed for induction machines with unknown model, uncertain load-torque and nonlinear friction where the speed is obtained using the model reference adaptive system (MRAS). Neurofuzzy systems are used to online approximate the uncertain nonlinearities and an adaptive backstepping technique is employed to systematically construct the control law. The MRAS observer is based on two different models, one that is speed independent (reference model) and another that is speed dependent (adjustable model). The speed is estimated via the outputs of the two models. The proposed sensorless controller guarantees the tracking errors converge to a small neighbourhood of the origin and the boundedness of all closed-loop signals what demonstrate the effectiveness of the proposed approach.

On the Convergence of the TTL Approximation for an LRU Cache under Independent Stationary Request Processes

The modeling and analysis of an LRU cache is extremely challenging as exact results for the main performance metrics (e.g., hit rate) are either lacking or cannot be used because of their high computational complexity for large caches. As a result, various approximations have been proposed. The state-of-the-art method is the so-called TTL approximation, first proposed and shown to be asymptotically exact for IRM requests by Fagin [13]. It has been applied to various other workload models and numerically demonstrated to be accurate but without theoretical justification. In this article, we provide theoretical justification for the approximation in the case where distinct contents are described by independent stationary and ergodic processes. We show that this approximation is exact as the cache size and the number of contents go to infinity. This extends earlier results for the independent reference model. Moreover, we establish results not only for the aggregate cache hit probability but also for every individual content. Last, we obtain bounds on the rate of convergence.

Access-Time-Aware Cache Algorithms

Most of the caching algorithms are oblivious to requests’ timescale, but caching systems are capacity constrained and, in practical cases, the hit rate may be limited by the cache’s impossibility to serve requests fast enough. In particular, the hard-disk access time can be the key factor capping cache performance. In this article, we present a new cache replacement policy that takes advantage of a hierarchical caching architecture, and in particular of access-time difference between memory and disk. Our policy is optimal when requests follow the independent reference model and significantly reduces the hard-disk load, as shown also by our realistic, trace-driven evaluation. Moreover, we show that our policy can be considered in a more general context, since it can be easily adapted to minimize any retrieval cost, as far as costs add over cache misses.

Spatial Multi-LRU Caching for Wireless Networks with Coverage Overlaps

This article introduces a novel family of decentralised caching policies for wireless networks, referred to as spatial multi-LRU. Based on these, cache inventories are updated in a way that provides content diversity to users that are covered by, and thus have access to, more than one station. Two variations are proposed, the multi-LRU-One and -All, which differ in the number of replicas inserted in the involved edge caches. Che-like approximations are proposed to accurately predict their hit probability under the Independent Reference Model (IRM). For IRM traffic multi-LRU-One outperforms multi-LRU-All, whereas when the traffic exhibits temporal locality the -All variation can perform better.

Temperature and food concentration have limited influence on the mixture toxicity of copper and Microcystis aeruginosa to Daphnia magna.

Standard ecotoxicity tests are conducted under constant and favorable experimental conditions. In natural communities, however, the toxicity of chemicals may be influenced by abiotic and biotic environmental factors. Firstly, the authors examined the influence of temperature and total food concentration on the nature of the combined effects of copper (Cu) and the cyanobacterium Microcystis aeruginosa to Daphnia magna (i.e., whether the combined effects deviated from noninteraction). Secondly, the authors investigated the relative influence of the percentage of M. aeruginosa in the diet, temperature, and total food concentration on chronic Cu toxicity to D. magna. The nature of the combined effects between Cu and M. aeruginosa (i.e., synergism according to the independent action reference model and noninteraction according to concentration addition reference model) was not affected by temperature and total food concentration. In line with other studies, the concentration addition reference model gave rise to more protective predictions of mixture toxicity than the independent action reference model, thus confirming the former model's suitability as a conservative scenario for evaluating mixture toxicity of Cu and M. aeruginosa under the temperature and food concentrations tested. Further, the 21-d median effective concentration for Cu based on reproduction varied between 20 μg/L and 100 μg/L, and the results indicate that the percentage of M. aeruginosa explained 76% of the variance in the Cu median effective concentration for reproduction, whereas the effects of temperature and total food were limited (together explaining 11% of the variance). The present study suggests that environmental risk assessment of Cu should consider specific situations where harmful M. aeruginosa blooms can co-occur with elevated Cu exposure.

Reuse Distance-Based Probabilistic Cache Replacement

This article proposes Probabilistic Replacement Policy (PRP), a novel replacement policy that evicts the line with minimum estimated hit probability under optimal replacement instead of the line with maximum expected reuse distance. The latter is optimal under the independent reference model of programs, which does not hold for last-level caches (LLC). PRP requires 7% and 2% metadata overheads in the cache and DRAM respectively. Using a sampling scheme makes DRAM overhead negligible, with minimal performance impact. Including detailed overhead modeling and equal cache areas, PRP outperforms SHiP, a state-of-the-art LLC replacement algorithm, by 4% for memory-intensive SPEC-CPU2006 benchmarks.

Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named shot noise model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard independent reference model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.

A General, Tractable and Accurate Model for a Cascade of LRU Caches

The recent evolution of the Internet towards "Information-centric" transfer modes has renewed the interest in characterizing multi-cache systems, in which requests not satisfied by a cache are forwarded to other caches. In this work, we characterize the traffic statistics of the output (miss) stream, via a simple but accurate approximate analysis for LRU caches feeded by general "renewal" traffic patterns. In turn, we exploit such output stream traffic pattern to analyze the performance of the subsequent cache stage, and so on. The computational efficiency of our model, joint with its ability to handle traffic patterns beyond the traditional independent reference model, permits simple and tractable assessment of cache hierarchies.

Check before storing

In some network and application scenarios, it is useful to cache content in network nodes on the fly, at line rate. Resilience of in-network caches can be improved by guaranteeing that all content therein stored is valid. Digital signatures could be indeed used to verify content integrity and provenance. However, their operation may be much slower than the line rate, thus limiting caching of cryptographically verified objects to a small subset of the forwarded ones. How this affects caching performance? To answer such a question, we devise a simple analytical approach which permits to assess performance of an LRU caching strategy storing a randomly sampled subset of requests. A key feature of our model is the ability to handle traffic beyond the traditional Independent Reference Model, thus permitting us to understand how performance vary in different temporal locality conditions. Results, also verified on real world traces, show that content integrity verification does not necessarily bring about a performance penalty; rather, in some specific (but practical) conditions, performance may even improve.

Fluid limit analysis of FIFO and RR caching for independent reference models

We study the fluid limit analysis of random replacement (RR) caching for the independent reference model. Applying the limit theorem for the mean field interaction model, we derive the fluid limit of fault probability in the transient state as well as in the steady state. Since the stationary fault probability for the RR cache is identical to that for the first-in first-out (FIFO) cache, our results on the stationary fault probability are available for the FIFO caching. We see that the fluid limit of stationary fault probability that we obtain is coincident with the existing heuristic approximation of stationary fault probability. That is, our fluid limit analysis gives a rigorous theoretical foundation to the heuristic approximation.

Error calibration of geopotential harmonics in recent and past gravitational fields

The geopotential coefficients of a number of recent and past models have been examined with the aim of calibrating their formal (or published) errors, principally by direct comparison with the same coefficients of a precise and wholly independent satellite-only reference field determined (in 2010) predominantly from over 7 years of μ/s low–low GRACE inter-satellite range rate observations (ITG-GRACE2010S). In all of these comparisons the reference field used, over specified spectral ranges, has much smaller reported errors than the ones to be calibrated. In particular we find that a recently published field (in 2010) using gravity gradient and position data from ESA’s GOCE satellite, GO_CONS_GCF_2_TIM_R1, has formal errors which are significantly optimistic for the lowest degrees (n) but with increasing realism where the gradiometer gains influence over the position information (15 < n < 120). Other GOCE models do not afford an unbiased error calibration. Validating error calibration with the independent reference model, of two past comprehensive fields (containing both satellite and surface data), confirms that one of them (JGM3, published in 1994, complete to n = 70) reported generally realistic formal errors while another (EGM96, published in 1998, complete to n = 360, tested only for n < 150) had significantly optimistic ones for most n < 100 but with better realism when affected only by the surface information.

The SCOR model for the alignment of business processes and information systems

The alignment of business processes and information systems is a critical factor for both business process management (BPM) and enterprise resource planning (ERP) system efficiency. Analysing existing approaches of alignment shows the need for an independent reference model to support the mapping between organisational and informational views. The SCOR model represents a strong management tool to evaluate, control, measure and improve existing supply chain process structures, based on a business process view of supply chain highlighting the functional requirements of best practices identified. We have critically analysed the contribution of the current SCOR model to the alignment of business processes and information systems. As the alignment is currently only based on the functional realisation of best practices, we show that this alignment risks neglecting important process dependencies. An extended reference model is then proposed, including the structuring of information exchanged between processes. That results in a more complete process map highlighting all physical and informational dependencies, in a multi-view of ‘business process mapping’ including the informational dimension, and thus a more precise alignment of ERP systems with processes.

Characterizing the miss sequence of the LRU cache

Renewed interest in caching systems stems from their wide-spread use for reducing the document download latency over the Internet. Since caches are usually organized in a hierarchical manner, it is important to study the performance properties of tandem caches. The first step in understanding this problem is to characterize the miss stream from one single cache since it represents the input to the next level cache. In this regard, we discover that the miss stream from one single cache is approximated well by the superposition of a number of asymptotically independent renewal processes. Interestingly, when this weakly correlated miss sequence is fed into another cache, this barely observable correlation can lead to measurably different caching performance when compared to the independent reference model. This result is likely to enable the development of a rigorous analysis of the tandem cache performance.

The persistent‐access‐caching algorithm

AbstractCaching is widely recognized as an effective mechanism for improving the performance of the World Wide Web. One of the key components in engineering the Web caching systems is designing document placement/replacement algorithms for updating the collection of cached documents. The main design objectives of such a policy are the high cache hit ratio, ease of implementation, low complexity and adaptability to the fluctuations in access patterns. These objectives are essentially satisfied by the widely used heuristic called the least‐recently‐used (LRU) cache replacement rule. However, in the context of the independent reference model, the LRU policy can significantly underperform the optimal least‐frequently‐used (LFU) algorithm that, on the other hand, has higher implementation complexity and lower adaptability to changes in access frequencies. To alleviate this problem, we introduce a new LRU‐based rule, termed the persistent‐access‐caching (PAC), which essentially preserves all of the desirable attributes of the LRU scheme. For this new heuristic, under the independent reference model and generalized Zipf's law request probabilities, we prove that, for large cache sizes, its performance is arbitrarily close to the optimal LFU algorithm. Furthermore, this near‐optimality of the PAC algorithm is achieved at the expense of a negligible additional complexity for large cache sizes when compared to the ordinary LRU policy, since the PAC algorithm makes the replacement decisions based on the references collected during the preceding interval of fixed length. © 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2008

Analysis of set-up time models: A metric perspective

We consider model based estimates for set-up time. The general setting we are interested in is the following: given a disk and a sequence of read/write requests to certain locations, we would like to know the total time of transitions (set-up time) when these requests are served in an orderly fashion. The problem becomes nontrivial when we have, as is typically the case, only the counts of requests to each location rather then the whole input, and we can only hope to estimate the required time. Models that estimate set-up time have been suggested and heavily used as far back as the sixties. However, not much theory exists to enable a qualitative understanding of such models. To this end we introduce several properties such as (i) super-additivity which means that the set-up time estimate decreases as the input data is refined (ii) monotonicity which means that more activity produces more set-up time, (iii) Dominance which means that one model always produces higher estimates than a second model and (iv) approximation guarantees for the estimate with respect to the worst possible time, by which we can study different models. We provide criteria for super-additivity and monotonicity to hold for popular models such as the Partial Markov model (PMM). The criteria show that the estimate produced by these models will be monotone for any reasonable system. We also show that the independent reference model (IRM) based estimate functions as a worst case estimate in the sense that the estimate is guaranteed to be at least half of the actual set-up time. We also show that it dominates the PMM based estimates. Using our criteria we prove that PMM based estimates are always super additive when applied to the special metrics that correspond to seek times of disk drives. To establish our theoretical results we use the theory of finite metric spaces, and en route show a result of independent interest in that theory, which is a strengthening of a theorem of J.B. Kelly [J.B. Kelly, Hypermetric spaces and metric transforms, in: O. Shisha (Ed.), Inequalities III, 1972, pp. 149–158] about the properties of metrics that are formed by concave functions on the line.

Near optimality of the discrete persistent access caching algorithm

Renewed interest in caching techniques stems from their application to improving the performance of the World Wide Web, where storing popular documents in proxy caches closer to end-users can significantly reduce the document download latency and overall network congestion. Rules used to update the collection of frequently accessed documents inside a cache are referred to as cache replacement algorithms. Due to many different factors that influence the Web performance, the most desirable attributes of a cache replacement scheme are low complexity and high adaptability to variability in Web access patterns. These properties are primarily the reason why most of the practical Web caching algorithms are based on the easily implemented Least-Recently-Used (LRU) cache replacement heuristic. In our recent paperJelenković and Radovanović (2004c), we introduce a new algorithm, termed Persistent Access Caching (PAC), that, in addition to desirable low complexity and adaptability, somewhat surprisingly achieves nearly optimal performance for the independent reference model and generalized Zipf's law request probabilities. Two drawbacks of the PAC algorithm are its dependence on the request arrival times and variable storage requirements. In this paper, we resolve these problems by introducing a discrete version of the PAC policy (DPAC) that, after a cache miss, places the requested document in the cache only if it is requested at least $k$ times among the last $m$, $m \geq k$, requests. However, from a mathematical perspective, due to the inherent coupling of the replacement decisions for different documents, the DPAC algorithm is considerably harder to analyze than the original PAC policy. In this regard, we develop a new analytical technique for estimating the performance of the DPAC rule. Using our analysis, we show that this algorithm is close to optimal even for small values of $k$ and $m$, and, therefore, adds negligible additional storage and processing complexity in comparison to the ordinary LRU policy.