Eviction Policies Research Articles

LPCE-Based Replacement Scheme for Enhancing Caching Performance in Named Data Networking

The substantial surge in users has adversely impacted the performance of the present IP-based Internet. Named data networking (NDN) emerges as a future alternative, given its distributed content caching system, where data can be cached in multiple routers and retrieved from the closest one instead of the original producer, enhancing content availability, reducing latency, and minimizing data loss. This paper introduces the less popular content eviction (LPCE) policy, a novel cache replacement scheme designed to enhance the caching performance of the conventional LFU (least frequently used) policy in NDN routers, thereby improving overall network efficiency. The proposed method subsumes LFU and FIFO (first in first out) policies and employs an additional list controlled by the LRU (least recently used) policy. Utilizing the ccnSim simulator, we conduct a comparison of LPCE’s performance with that of the LFU technique and other competing caching techniques, considering variations in several simulation parameters. Experimental results reveal that the proposed LPCE algorithm excels over others across a majority of performance metrics, such as cache hit ratio, content delivery delay, upstream hop count, network traffic, and producers’ load. Besides, the findings indicate that LPCE outperforms LFU, with an increase in cache hit ratio ranging from 1.32% to 5.75%.

Moving From Crisis to Stability? The Success and Limits of an Eviction Prevention Program

Eviction prevention policies are often crafted to focus on either an upstream approach, prior to households interacting with eviction court, or a downstream approach, once the court process has begun and when households are in an acute housing crisis. This paper examines one such upstream approach, the Virginia Eviction Reduction Pilot (VERP) program, implemented in a first phase from 2021 to 2022 in the context of statewide Emergency Rental Assistance (ERA) and state and federal tenant protections. The paper uses a difference-in-differences approach to measure the effect of VERP on filings and judgments in areas served by the program and in-depth interviews with program staff to elucidate how VERP functioned as an upstream eviction prevention program during a volatile policy landscape during the COVID-19 pandemic. While we find no statistically significant effect of VERP on filings and judgments, we triangulate interview data to detail how VERP functioned in a rapidly changing policy environment to stabilize households by leveraging ERA funds. We find that the end of COVID eviction policies like ERA placed increasing pressure on VERP to fill a void it was not designed to address, and so effectively implementing upstream policies requires consistent and robust downstream assistance.

Collaborative Video Caching in the Edge Network using Deep Reinforcement Learning

With the enormous growth in mobile data traffic over the 5G environment, Adaptive BitRate (ABR) video streaming has become a challenging problem. Recent advances in Mobile Edge Computing (MEC) technology make it feasible to use Base Stations (BSs) intelligently by network caching, popularity-based video streaming, and more. Additional computing resources on the edge node offer an opportunity to reduce network traffic on the backhaul links during peak traffic hours. More recently, it has been found in the literature that collaborative caching strategies between neighbouring BSs (i.e., MEC servers) make it more efficient to reduce backhaul traffic and network congestion and thus improve the viewer experience substantially. In this work, we propose a Reinforcement Learning (RL)–based collaborative caching mechanism in which the edge servers cooperate to serve the requested content from the end-users. Specifically, this research aims to improve the overall cache hit rate at the MEC, where the edge servers are clustered based on their geographic locations. This task is modelled as a multi-objective optimization problem and solved using an RL framework. In addition, a novel cache admission and eviction policy is defined by calculating the priority score of video segments in the clustered MEC mesh network.

A survey of classification cache replacement techniques in the contentcentric networking domain

Content-Centric Networking (CCN) is an innovative approach that emphasizes content. A key strategy in CCN for spreading data across the network is in-network caching. Effective caching methods, including content placement and removal tactics, enhance the use of network resources. Cache replacement, also known as content eviction policies, is essential for maximizing CCN's efficiency. When cache storage is full, some content must be removed to make room for new items due to limited storage space. Recently, several advanced replacement strategies have been developed to determine the most suitable content for eviction. This study categorizes the latest cache replacement strategies into various groups such as static, space scarcity, content update, centralized, energy-efficient, weighted, adaptive, and based on dynamic popularity. These categories are based on the approaches suggested in previous research. Additionally, this paper provides a critical analysis of existing methods and suggests future research directions. To the best of our knowledge, this is the most up-to-date and comprehensive review available on this topic.

Green infrastructure inequalities in informal settlements

A growing number of cities are turning to green infrastructure as a way to manage climate change, nature recovery and unsustainable development. Although such an approach has been at the centre of thinking about development in many cities in the global North, there have been few interventions in cities of the global South. Only recently have scholars paid attention to how uneven land-use regulations in greening approaches toward informal settlements can exacerbate inequalities and result in green gentrification, residents' displacement and the relocation of informal communities. Eviction policies and the relocation of informal settlements have been justified by planning authorities under the banner of ecological improvement and climate adaptation. However, the eviction and replacement policies are often resisted by the residents of informal settlements. This paper builds upon and extends this nascent bank of knowledge by mobilising the conflicting rationalities framework to illustrate how green interventions conventionally are framed as a ‘win-win’ can exacerbate inequitable urban redevelopments, leading to exclusion and conflict. The paper qualitatively examines a case of greening plans in an informal settlement in Tehran metropolitan area, Iran. Thus, it also provides knowledge on a context that has received comparatively scant coverage in the academic literature on urban greening.

Enhanced dynamic fine‐grained popularity‐based caching algorithm for ICN‐based edge computing networks

AbstractIn this letter, we propose an Enhanced Dynamic Fine‐Grained Popularity‐based Caching (ED‐FGPC) algorithm to efficiently cache static contents in ICN‐based edge computing networks. The content popularity changes with time. Thus, the ED‐FGPC algorithm considers the number of incoming interests, the practical file size, and the available cache size to adjust the content popularity threshold. This allows dynamic adjustment of the content popularity threshold. Additionally, ED‐FGPC incorporates a single bit called cached bit (CB) in the header field of the content. This eliminates caching redundancy in the on‐path routers. The cache eviction policy jointly considers the content popularity and the average content access timestamp for content replacement. Based on the content access pattern, this allows finer adjustment of the content eviction policy. We use analytical and mathematical modeling to derive the effectiveness and efficiency of our contribution. Results present a better hit ratio due to dynamic adjustment of the content popularity threshold and the elimination of caching redundancy from the on‐path routers.

A novel eviction policy based on shortest remaining time for software defined networking flow tables

SummarySoftware defined networking is a modern paradigm that divides the control plane from the data plane for improved network manageability. A flow table in the data plane has limited and expensive memory called TCAM. The presence of unwanted flow rules would lead to flow bloat conditions and make the lookup operation inefficient. Eviction schemes based on LRU policy have been widely studied in the literature which preempts the life of the least recently used flow rule and reduces the occupancy of the flow table. LRU considers the past behavior for the eviction of a flow rule. This paper proposes a novel policy that preempts a flow rule by considering its future characteristic, the shortest remaining time (SRT). A rule with a higher probability of being used is avoided from eviction to mitigate the degradation of performance. The modeling of the SRT technique exhibits better utilization of a flow rule that has higher probability of being used. On the other hand, LRU does not guarantee that the evicted flow rule will not be used frequently in the future and it has been shown that an incorrectly evicted flow rule incurs controller delay. The experimental results show that for different traffic rates, SRT has reduced the delay by 15%, reinstallation count by 25%, and jitter by 40%. SRT has increased utilization by 22% compared to LRU.

Shelter From the Storm: State Eviction Moratoria, Implementation Context, and Eviction Filings During the First Two Years of the COVID-19 Pandemic

Forty-four state governments enacted eviction moratoria freezing or tempering the eviction process during the COVID-19 pandemic in an effort to forestall evictions. Combining data on state and federal eviction policies with data on eviction filings at the census tract level in 27 municipal areas from very late December 2019 through March 2022, we estimated correlated random effects Poisson models to examine effects of the moratoria. We found that state eviction moratoria were associated with a 32% lower rate of filings for a given tract, with moratoria targeting earlier stages of the eviction process having a particularly pronounced effect. We further found that state and federal moratoria were synergistic: eviction filings were lowest when both a strong state moratorium and a federal moratorium were in effect. Finally, state moratoria tempered the relationships between risk factors such as community poverty or racial and ethnic demographic composition and eviction filings. Results suggest that state eviction moratoria, particularly those targeting earlier stages of the eviction process, were successful in meeting their primary goal of decreasing eviction risks during the pandemic.

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach.

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

Taming data locality for task scheduling under memory constraint in runtime systems

A now-classical way of meeting the increasing demand for computing speed by HPC applications is the use of GPUs and/or other accelerators. Such accelerators have their own memory, which is usually quite limited, and are connected to the main memory through a bus with bounded bandwidth. Thus, particular care should be devoted to data locality in order to avoid unnecessary data movements. Task-based runtime schedulers have emerged as a convenient and efficient way to use such heterogeneous platforms. When processing an application, the scheduler has the knowledge of all tasks available for processing on a GPU, as well as their input data dependencies. Hence, it is possible to produce a tasks processing order aiming at reducing the total processing time through three objectives: minimizing data transfers, overlapping transfers and computation and optimizing the eviction of previously-loaded data. In this paper, we focus on how to schedule tasks that share some of their input data (but are otherwise independent) on a single GPU. We provide a formal model of the problem, exhibit an optimal eviction strategy, and show that ordering tasks to minimize data movement is NP-complete. We review and adapt existing ordering strategies to this problem, and propose a new one based on task aggregation. We prove that the underlying problem of this new strategy is NP-complete, and prove the reasonable complexity of our proposed heuristic. These strategies have been implemented in the StarPU runtime system. We present their performance on tasks from tiled 2D, 3D matrix products, Cholesky factorization, randomized task order, randomized data pairs from the 2D matrix product as well as a sparse matrix product. We introduce a visual way to understand these performance and lower bounds on the number of data loads for the 2D and 3D matrix products. Our experiments demonstrate that using our new strategy together with the optimal eviction policy reduces the amount of data movement as well as the total processing time.

HCFTL: A Locality-Aware Flash Translation Layer for Efficient Address Translation

In modern solid-state drives (SSDs), a flash translation layer (FTL) is the core engine performing the logical-to-physical address translations. However, the increasing capacity of SSDs requires a large DRAM space to hold the mapping table in a page-level FTL. Due to the limited size of built-in DRAM, existing FTL schemes selectively cache some active mapping entries in DRAM, while store the entire mapping table on flash. However, the low cache hit ratio introduced by the traditional mapping entry eviction policy degrades the access performance of SSDs. In this article, we propose a novel FTL, hot-clusterity FTL (HCFTL), to improve the hit ratio of cached mapping table (CMT) of SSDs with limited cache space. HCFTL clusters mapping entries, which are newly evicted from the cache, into dynamic translation pages (DTPs). Reading entries from DTPs will increase the CMT hit ratio due to spatial and temporal localities. Because the logical page numbers (LPNs) in a DTP are not consecutive, we introduce two different efficient index structures to speedup the lookup of mapping entries in DTPs. Furthermore, to make the DTP index highly accurate, we additionally allocate an auxiliary cache to buffer the mapping entries newly evicted from CMT, and produce dynamic pages with minimum difference between the maximal and the minimal LPNs of entries in those pages. Our experiments show that HCFTL can improve the CMT hit ratio by up to 41.1% and decrease the system response time by up to 33.3%, compared to baseline FTL schemes.

SLRL

HPC systems are composed of multiple tiers of storage, from the top high performance tier (high speed SSDs) to the bottom capacitive one (tapes). File placement in such architecture is managed through prefetchers (bottom-up) and eviction policies (top-down). Most state-of-the-art work focus on the former while using algorithm flavors of LRU, LFU and FIFO for the latter. LRU was for long considered the best choice. However, recent studies has shown that the simplicity of FIFO could make it more scalable than LRU because of metadata management, and thus more adequate in several cases. In this paper, we propose a new eviction policy based on predicted files lifetimes. It is comparable to FIFO in terms of metadata overhead and simplicity (thus scalability), while giving a hit ratio comparable to LRU (or even 10% better for some tested traces). We also propose a naive multi-tier heterogeneous storage simulator implementation to evaluate such policies.

Immuno-inspired management of halls of fame for embodied evolution

Evolutionary robotics employs unconventional techniques to continuously evolve controllers for robots, based on their fitness values. In most cases, a parent controller is subjected to mutation to evolve its offspring. If the offspring performs better than the parent, the former is made to replace the latter. This essentially results in a major loss in information learned by the parents over generations. One simple workaround to circumvent this problem is to maintain a Hall of Fame ( HoF ) comprising the best parent controllers for use in future generations. In embodied evolutionary robotic scenarios, caching a large number of controllers in an HoF would result in increased computational overheads while selecting the best out of them, resulting in a drastic reduction in performance. With no means to find an upper limit to the number of controllers that can populate an HoF a priori , devising a technique to dynamically regulate this population is imperative. In this work, a novel method to evict the non-performing controllers within an HoF based on the dynamics of the system, is proposed. We describe the evolution of such controllers using genetic operators that eventually form an Idiotypic Network. A constantly varying Resource , associated with each controller together with its concentration in the Idiotypic Network, helps decide its eviction from the HoF . Experiments performed using simulations and also a real robot indicate a marked improvement in the learning process due to the dynamic eviction policy.

MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation

The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tensor contractions. More specifically, it targets general GPU architectures (both NVIDIA and AMD) and optimizes many-body correlation’s memory management by exploiting a set of memory allocation and communication redundancy elimination opportunities: first, GPU memory allocation redundancy : the intermediate output frequently occurs as input in the subsequent calculations; second, CPU-GPU communication redundancy : although all tensors are allocated on both CPU and GPU, many of them are used (and reused) on the GPU side only, and thus, many CPU/GPU communications (like that in existing Unified Memory designs) are unnecessary; third, GPU oversubscription: limited GPU memory size causes oversubscription issues, and existing memory management usually results in near-reuse data eviction, thus incurring extra CPU/GPU memory communications. Targeting these memory optimization opportunities, this article proposes MemHC, an optimized systematic GPU memory management framework that aims to accelerate the calculation of many-body correlation functions utilizing a series of new memory reduction designs. These designs involve optimizations for GPU memory allocation, CPU/GPU memory movement, and GPU memory oversubscription, respectively. More specifically, first, MemHC employs duplication-aware management and lazy release of GPU memories to corresponding host managing for better data reusability. Second, it implements data reorganization and on-demand synchronization to eliminate redundant (or unnecessary) data transfer. Third, MemHC exploits an optimized Least Recently Used (LRU) eviction policy called Pre-Protected LRU to reduce evictions and leverage memory hits. Additionally, MemHC is portable for various platforms including NVIDIA GPUs and AMD GPUs. The evaluation demonstrates that MemHC outperforms unified memory management by \( 2.18\times \) to \( 10.73\times \) . The proposed Pre-Protected LRU policy outperforms the original LRU policy by up to \( 1.36\times \) improvement. 1

IMU: A Content Replacement Policy for CCN, Based on Immature Content Selection

In-network caching is the essential part of Content-Centric Networking (CCN). The main aim of a CCN caching module is data distribution within the network. Each CCN node can cache content according to its placement policy. Therefore, it is fully equipped to meet the requirements of future networks demands. The placement strategy decides to cache the content at the optimized location and minimize content redundancy within the network. When cache capacity is full, the content eviction policy decides which content should stay in the cache and which content should be evicted. Hence, network performance and cache hit ratio almost equally depend on the content placement and replacement policies. Content eviction policies have diverse requirements due to limited cache capacity, higher request rates, and the rapid change of cache states. Many replacement policies follow the concept of low or high popularity and data freshness for content eviction. However, when content loses its popularity after becoming very popular in a certain period, it remains in the cache space. Moreover, content is evicted from the cache space before it becomes popular. To handle the above-mentioned issue, we introduced the concept of maturity/immaturity of the content. The proposed policy, named Immature Used (IMU), finds the content maturity index by using the content arrival time and its frequency within a specific time frame. Also, it determines the maturity level through a maturity classifier. In the case of a full cache, the least immature content is evicted from the cache space. We performed extensive simulations in the simulator (Icarus) to evaluate the performance (cache hit ratio, path stretch, latency, and link load) of the proposed policy with different well-known cache replacement policies in CCN. The obtained results, with varying popularity and cache sizes, indicate that our proposed policy can achieve up to 14.31% more cache hits, 5.91% reduced latency, 3.82% improved path stretch, and 9.53% decreased link load, compared to the recently proposed technique. Moreover, the proposed policy performed significantly better compared to other baseline approaches.

Pandemic demolitions: The unrecognized Bedouin villages in southern Israel and the ongoing housing crisis

This Update reports on the continued eviction policy that the State of Israel has been leading towards the Bedouin of the Negev-Naqab, a situation existing since the establishment of the State in 1948 and deepened during the Covid-19 pandemic. The housing crisis for Bedouin indigenous citizens and communities has long been urgent and dire, as the State of Israel continues to deny the existence of thirty-five Bedouin villages that are unrecognized and thus lack basic infrastructure like electricity, sewage services, water connections and garbage disposal. With little access to health services, these communities continue to be transparent on the map and in national statistics. Members of the Negev Coexistence Forum for Civil Equality, an Arab-Jewish organization established in 1997 by Arab and Jewish residents of the Naqab to provide a platform for a joint fight for civil rights equality, detail these historic and ongoing housing injustices, supported by powerful photos from the exhibition Recognized: Life and Resilience captured by Bedouin women.

Exploring a Hybrid Voting-based Eviction Policy for Caches and Sparse Directories on Manycore Architectures

In manycore systems, eviction decisions related to caches and memory coherence greatly impact system performance, thereby emphasizing their importance. Extensive research has produced numerous standalone eviction policies such as LRU, LFU, FIFO, etc. all aiming to attain the Bélády optimum solution. Standalone eviction policies optimize for a single attribute (recency, frequency, etc.), limiting their impact on applications exhibiting non-uniform memory access patterns. The Hybrid Voting-based Eviction Policy (HyVE) extends multiple standalone eviction policies with a ranking system and evaluates them using concepts from the voting theory domain. The goal of HyVE is to make better replacement decisions by creating a consensus among its constituent eviction policies. With its inherent voting properties, HyVE takes different replacement decisions compared to its standalone counterparts, making it a unique and new eviction policy. We deploy and evaluate HyVE as part of two case-studies: last-level cache replacement decisions in a generic manycore environment, and sparse directory eviction decisions on a tile-based distributed shared memory (DSM) architecture. We explore different variants of HyVE, and evaluate them using workloads from the PARSEC and SPLASH-2 benchmark suites in a simulation environment. We also compare HyVE to state-of-the-art set-dueling and learning-based eviction policies. For last-level cache replacement decisions, HyVE reduces cache misses by 7.4% compared to the LRU policy, whereas DRRIP and Hawkeye reduce cache misses by 5.5% and 9.2% respectively compared to the LRU policy. Though Hawkeye exhibits better performance on average, HyVE offers a unique advantage for certain workloads by using a voting-based approach to solve the replacement problem. For sparse directory eviction decisions, results show that HyVE reduces coherence traffic and execution time by up to 11% compared to the LRU policy. We have synthesized HyVE on an FPGA prototype. Hardware analysis results show that HyVE’s constituent policies contribute the most to its overheads, while HyVE’s ranking and voting extensions do not add significant overheads. Timing analysis results show that HyVE’s logic delay is comparable to that of standalone eviction policies. Lastly, we evaluate HyVE on the FPGA prototype using characteristic micro-benchmarks that further emphasize HyVE’s ability to remain agnostic to varying data access patterns.

ML-CLOCK: Efficient Page Cache Algorithm Based on Perceptron-Based Neural Network

Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.

“There's no place like home”: Examining the associations between state eviction defense protections and indicators of biopsychosocial stress among survivors of intimate partner violence

Housing instability is prevalent among intimate partner violence (IPV) survivors and a source of biopsychosocial stress among this population. Eviction policies play an important role in determining housing instability of IPV survivors. However, few studies have investigated whether state-level policies that prevent evictions lessen vulnerability to biopsychosocial stress among IPV survivors. This study examined the relationship between state eviction defense policy and indicators of biopsychosocial stress among 6577 IPV survivors. State-level data on IPV-related housing policies were from a compendium on homelessness and violence. Individual-level data were collected from the National Intimate Partner and Sexual Violence Survey (NISVS), a nationally representative study of noninstitutionalized U.S. women and men from Wave 1 (2010). Multilevel regression models were conducted to investigate associations between the presence of an eviction defense policy and indicators of biopsychosocial stress (i.e., headaches, sleeping, safety concerns and PTSD symptoms). Stratified multilevel modeling was conducted to examine differences in the policy-stress associations across racial and ethnic groups and gender. Nearly 26% of states had an eviction defense policy for IPV survivors. Overall, residing in a state with an eviction defense policy (vs. none) was associated with no reports of frequent headaches (B [95% CI] = −0.21 [-0.41, −0.01], p < .05). For non-Hispanic Black survivors, residing in a state with an eviction defense policy (vs. none) was associated with reduced likelihood of reporting safety concerns (B [95% CI] = −1.36 [-2.16, −0.56], p < .001) and PTSD symptoms (B [SE] = −1.91 [-2.82, −1.01], p < .000). Among men survivors, residing in a state with an eviction defense policy was associated with reduced likelihood of reporting safety concerns (B [95% CI] = −0.63 [-1.26, −0.01], p < .05). State housing policies are important protective policies for IPV survivors. For IPV survivors, the eviction defense policy may interrupt the psychological sequeale of IPV and housing instability.

A New Upper Bound on Cache Hit Probability for Non-anticipative Caching Policies

Caching systems have long been crucial for improving the performance of a wide variety of network and web based online applications. In such systems, end-to-end application performance heavily depends on the fraction of objects transfered from the cache, also known as the cache hit probability. Many cache eviction policies have been proposed and implemented to improve the hit probability. In this work, we propose a new method to compute an upper bound on hit probability for all non-anticipative caching policies, i.e. for policies that have no knowledge of future requests. At each object request arrival, we use hazard rate (HR) function based ordering to classify the request as a hit or not. Under some statistical assumptions, we prove that our proposed HR based ordering model computes the maximum achievable hit probability and serves as an upper bound for all non-anticipative caching policies. We also provide simulation results to validate its correctness and to compare it to Belady's upper bound. We find it to almost always be tighter than Belady's bound.