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In multi-access edge computing (MEC) systems, there are multiple local cache servers caching contents to satisfy the users' requests, instead of letting the users download via the remote cloud server. In this paper, a multi -cell content scheduling problem (MCSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi -cell setting. Different from single -cell scenarios, a user may have multiple candidate local cache servers, and thus the caching decisions in all cells must be jointly optimized. We first prove that MCSP is NP -hard, then we formulate MCSP using integer linear programming, by which the optimal scheduling can be obtained for small-scale instances. For problem solving of large scenarios, via a mathematical reformulation, we derive a scalable optimization algorithm based on repeated column generation. Our performance evaluation shows the effectiveness of the proposed algorithm in comparison to an off -the -shelf commercial solver and a popularity -based caching.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones. IEEE

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks' traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading's category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Inom nätverkstekniken och datakommunikationen råder idag en tro på tekniken nätverkscache som kan spara data för att senare kunna hämta hem det snabbare. Tekniken har genom åren visat att den effektivt kan skicka den önskade data till sina klienter. Det finns flera cache-tjänster som använder tekniken för Windows-uppdateringar. Bland dessa finns Windows Server Update Services (WSUS) och LanCache. På uppdrag från företaget TNS Gaming AB jämförs dessa tjänster med varandra under examensarbetet. Nätverkscache är ett intressant forskningsområde för framtida kommunikationssystem och nätverk tack vare sina fördelar. Likaså är uppgiften om att jämföra cache-tjänsterna WSUS och LanCache intressant i och med det öppnar upp insikt om vilken tjänst är bättre för företaget eller andra intressenter. Både forskningsområdet och uppgiften är viktiga och intressanta när användare vill effektivisera användningen av internetanslutningen och bespara nätverksresurser. Därmed kan tekniken minska nedladdningstiden. Till det här arbetet besvaras frågor om vilken nätverksprestanda, resursanvändning och administrationstid respektive cache-tjänst har, och vilken cache-tjänst som lämpar sig bättre för företagets behov. I arbetet genomförs experiment, som omfattar tre huvudmättningar, och följs av en enfallstudie. Syftet med arbetet är att med hjälp av experimentets mätningar få en jämförelse mellan WSUS och LanCache. Resultatet av arbetet utgör sedan ett underlag för det framtida lösningsvalet. Resultaten består av två delar. Den första visar att båda cache-tjänsterna bidrar till kortare nedladdningstider. Den andra är att LanCache är bättre än WSUS när det gäller nätverksprestanda och resursanvändning, samt mindre administrationstid jämfört med WSUS. Givet resultat dras slutsatsen att LanCache är cache-tjänsten som är mest lämpad i det här fallet., In the field of network technology and data communication, there is a current belief in the technology of network caching, which can store data to later retrieve it more quickly. Over the years, this technology has proven its ability to efficiently deliver the desired data to its clients. There are several caching services that utilize this technology for Windows updates, among them are Windows Server Update Services (WSUS) and LanCache. On behalf of the company TNS Gaming AB, these services are compared to each other in this thesis. Network caching is an interesting area of research for future communication systems and networks due to its benefits. Likewise, the task of comparing the cache services WSUS and LanCache is interesting as it provides insights into which service is better suited for the company or other stakeholders. Both the research area and the task are important and intriguing when users seek to streamline the use of their internet connection and conserve network resources. Thus, the technology can reduce download times. For this work, questions about the network performance, resource usage, and administration time of each cache service are answered, as well as which cache service that is better suited to the company's needs. The work involves conducting experiments, including three main measurements, followed by a single case study. The purpose of the work is to compare WSUS and LanCache using the measurements from the experiment. The outcome of the work then forms a basis for future solution choice. The results consist of two parts. The first shows that both cache services contribute to shorter download times. The second is that LanCache outperforms WSUS in terms of network performance and resource usage, and also requires less administration time than WSUS. Given the results, the conclusion is drawn that LanCache is the most suitable caching service in this case.

Distributed systems offer a reliable and scalable solution for tackling massive and complex tasks that cannot be handled by a single computer. However, standard consensus protocols used in such systems often replicate data without considering the workload, leading to unnecessary retransmissions. This thesis proposes using machine learning (ML) to optimize consensus protocols and make them adaptable to recurring workloads. It introduces a cache that encodes frequently-transmitted data between nodes to reduce network traffic. To implement this, the thesis builds a caching layer at all nodes using the decided logs, which represent a consistent view of the application history. The cache can encode and decode incoming log entries to reduce the average message size and improve throughput under limited network bandwidth. The thesis selects an ML-based model that combines various caching policies and adapts to changing access patterns in the workload. Experimental results show that this approach can improve throughput up to 250%, assuming negligible preprocessing overhead., Distribuerade system erbjuder en pålitlig och skalbar lösning för att hantera massiva och komplexa uppgifter som inte kan hanteras av en enskild dator. Konventionella konsensusprotokoll som används i dessa system replikerar emellertid ofta data utan att ta hänsyn till arbetsbelastningen, vilket leder till överflödig dataöverföring. Denna avhandling föreslår att använda maskinin lärning (ML) för att optimera konsensusprotokoll och göra dem anpassade till återkommande mönster i arbetsbelastningen. Den introducerar en cache som kodar och komprimerar data som ofta överförs mellan noder för att minska nätverkstrafiken. För att implementera detta byggs ett cache baserat på den bestämda loggen på alla noder, som representerar en konsekvent syn på programhistoriken. Cachen kan koda inkommande data för att minska genomsnittlig meddelandestorlek och förbättra genomströmning under begränsad nätverksbandbredd. En ML-baserad modell som kombinerar olika cachningpolicyer och anpassar sig till ändrade åtkomstmönster i arbetsbelastningen används. Experimentella resultat visar att denna metod kan förbättra genomströmningen med 250%, under förutsättning att förbearbetningsöverhuvudet är försumbart.

Next-generation multi-spot beam satellite systems open a new way to design low earth orbit (LEO) satellite communication systems with full flexibility in managing bandwidth, transmit power, and spot beam coverage, enabling the adoption of spatial multiplexing techniques to meet the unprecedented demand for future mobile traffic. However, conventional spatial multiplexing techniques perform poorly in satellite systems due to high correlation between the satellite channels, resulting in inefficient mitigation of inter-user interference. In this paper, we exploit the flexibility of multi-spot beam LEO satellites and consider the geographic distribution of users to improve the performance of LEO satellite-assisted edge caching systems. Our goal is to jointly optimize the beam coverage, bandwidth and transmit power and minimize the cache delivery time. In particular, the spot beam coverage is optimized by using the K-means algorithm applied to the realistic user demands, followed by a proposed successive convex approximation (SCA)-based iterative algorithm for optimizing the radio resources. Simulations shows that our optimal approach outperforms the conventional precoding-based approach and also shows a significant improvement in the minimization of the maximum content delivery time.

Caching is a fundamental technique widely used in the field of computing to reduce network traffic, server load, and latency. Storing frequently accessed data in a high-speed cache layer can make future requests process faster by involving fewer system components when generating and serving the response. Kalix is a software product that demands a caching solution since it faces latency and is frequently processing partially repeated queries. However, a cache does not guarantee improved performance, which is why the main problems of caching are: determining what content to cache, when to insert or remove cache content, implementing the caching logic, and deciding where to store the cache efficiently. Therefore, this paper theoretically investigates where a cache solution should be implemented within the Kalix system architecture to decrease latency and server load, and evaluates the subsequent cache implementation experimentally. As a result, a client-side cache is implemented which decreases the latency of Kalix by up to 74%, while reducing the I/O load and memory utilization on the server by 98%. The reason for the decrease is that the cache in the client can directly serve the majority of the content, allowing the servers of Kalix to do substantially fewer computations. The evaluation acts as a recommendation for the company behindKalix, Polystar, as to if a cache is beneficial and where the cache can efficiently be deployed, and this paper gives valuable insights into the decision-making of cache placement. Concludingly, implementing the cache positively impacts the Kalix user experience.

We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle. The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. We provide a universal lower bound for prediction-Assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. In this pursuit, we design, to the best of our knowledge, the first optimistic Follow-The-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Networked Systems

The ability to communicate underwater is a lucrative capability for industries, academia and the military. In these sectors, underwater wireless communications (UWC) are used for a variety of applications, such as oil and gas exploration, the monitoring of undersea pipelines, oceanography, seismology, undersea surveillance, and the detection of mines. However, many of these undersea activities are deterred due to numerous challenges posed by the harsh underwater environment. Energy scarcity is one of the major challenges, as frequent recharging of underwater depleted nodes is not practical. Ensuring reliable and secure UWC is another major challenge since traditional techniques cannot be implemented due to energy shortages. This thesis addresses the challenge of energy-efficient underwater communications and studies its implications for reliable and secure UWC. The thesis introduces new techniques that minimise energy consumption, extend network lifetime, and offer secure communications in UWC networks. Firstly, this thesis presents a hybrid multi-modal optoacoustic UWC technique that not only takes into account the variability in weather conditions that adversely affect UWC, but also the temporal changes in underwater traffic to select the best endto- end communication strategy and hence minimise UWC energy consumption. Second, in this thesis, a caching strategy is introduced that jointly works with renewable energybased charging of underwater nodes, which can significantly extend the optoacoustic UWC network lifetime. Third, a residual energy maximisation technique is introduced that can simultaneously improve communication security in optoacoustic UWC networks without sacrificing its energy budget. Overall, this thesis contributes to this field of research by mainly investigating the energy aspect of UWC networks and by studying its effects on secure communications whilst introducing solutions to relevant research problems.

Software applications are built by teams of developers that constantly iterate over the codebase. Software projects rely on a build system, which handles the management of dependencies, compilation, testing, and deployment of the software. The execution of the tests during each build allow developers to validate that their changes do not introduce regressions. However, the execution of the test suite during each build can take a long time, potentially impacting the development process. To facilitate quicker feedback, build systems use incremental building in order to avoid the reprocessing of unmodified artifacts. This is achieved by maintaining a cache of source files, and only rebuilding artifacts that differ from their cached version. Yet, changing any part of a source file invalidates the cache, triggering the re-execution of unmodified tests. This focus over an entire file can be misleading to the build system, as it can not determine whether the actual function being tested has changed, thus invoking redundant re-testing. In this thesis, we propose a finer-grained approach to caching within build systems, by caching components within the Abstract Syntax Tree instead of entire source files. We compare their hashes on subsequent runs, in order to identify components that have changed. The potential advantage of this strategy is that re-running a specific test that has not been modified will leverage the use of caches even if the file that contains it has been modified. We implement our approach in a system called TRICORDER, and integrate it within a build system called WARP. TRICORDER works by analyzing RUST source code in order to identify the test cases that have not been changed, such as through the addition of comments, or modifications of unrelated functions. This can benefit developers by avoiding the re-execution of tests that are unmodified. We evaluate our approach against 4 notable, open-source RUST projects, targeting a set of 16 tests within them. We, Programvaruapplikationer byggs av utvecklingsteam som ständigt itererar över kodbasen. Programvaruprojekt förlitar sig på ett byggsystem som hanterar beroenden, kompilering, testning och implementering av programvaran. Utförande av testerna under varje byggprocess möjliggör för utvecklare att validera att deras ändringar inte introducerar regressionsfel. Dock kan utförningen av testsviten under varje byggprocess ta lång tid och potentiellt påverka utvecklingsprocessen. För att underlätta snabbare återkoppling använder byggsystemen inkrementell byggning för att undvika onödig återbearbetning av oförändrade artefakter. Detta uppnås genom att bibehålla en cache av källkodsfilerna och endast bygga om artefakter som skiljer sig från deras cachade version. Att ändra vilken del som helst av en källkodsfil invaliderar cachet och utlöser körningen av oförändrade tester. Fokuseringen på en hel fil kan vara vilseledande för byggsystemet, då det inte kan avgöra om den faktiska funktionen som testas har ändrats och därigenom påbörjar onödig omtestning. I detta projekt föreslår vi en mer detaljerad cache-strategi inom byggsystem, genom att cacha komponenter inom det abstrakta syntaxträdet istället för hela källkodsfiler. Vi jämför deras hash-värden vid senare körningar för att identifiera ändringar. Den potentiella fördelen med denna strategi är när man kör om ett specifikt test som inte har ändrats kan cachen användas även om filen som innehåller testet har modifierats. Vi implementerar vår metod i ett system som kallas TRICORDER och integrerar det i ett byggsystem som heter WARP. TRICORDER fungerar genom att analysera RUST-källkod för att identifiera testfall som inte har ändrats, till exempel genom tillägg av kommentarer eller ändringar av irrelevanta funktioner. Detta kan gynna utvecklare genom att undvika att köra om tester som inte har ändrats. Vi utvärderar vår metod mot 4 välkända öppen källkodsprojekt i RUST och riktar in oss på en uppsättning av 16 tester inom dem. Förs, Aplicațiile software sunt dezvoltate de programatori care iterează constant asupra codului. Proiectele de software se bazează pe un sistem de generare care gestionează dependențele, compilarea, testarea și lansarea software-ului. Execuția testelor permite dezvoltatorilor să valideze că modificările lor nu introduc regresii. Cu toate acestea, execuția testelor în cadrul fiecărei generări poate dura mult timp, având potențialul de a incetinii dezvoltarea. Pentru a facilita o reprocesare mai rapidă, sistemele de generare utilizează construirea incrementală pentru a evita reprelucrarea a artefactelor nemodificate. Acest lucru se realizează prin menținerea unei cache și reconstruirea doar a artefactelor care diferă de cele din cache. Cu toate acestea, orice modificare a unui fișier sursă invalidează cache-ul, declanșând reprocesarea. Focalizarea asupra unui fișier întreg poate induce în eroare sistemul de generare, deoarece nu poate determina dacă funcția testată a suferit modificări, declanșând astfel teste redundante. În această teză, propunem o abordare mai detaliată a cache-ului în cadrul sistemelor de generare, prin cacharea componentelor Arborelui Sintactic Abstract, în locul întregilor fișiere sursă. Comparăm hash-urile acestora în rulările ulterioare pentru a identifica componentele modificate. Avantajul potențial al acestei strategii constă în faptul că reexecutarea unui test care nu a nemodificat va utiliza cache-urile chiar dacă fișierul a fost modificat. Implementăm abordarea noastră într-un sistem numit TRICORDER și îl integrăm într-un sistem de construire numit WARP. TRICORDER funcționează prin analizarea codului sursă RUST pentru a identifica cazurile de testare care nu au fost modificate, cum ar fi prin adăugarea de comentarii sau modificări ale funcțiilor nerelevante. Acest lucru poate fi benefic pentru dezvoltatori, evitând reexecutarea testelor care nu au fost modificate. Evaluăm abordarea noastră în raport cu 4 proiecte notabile open-source în RUST, a

Online caching is among the most fundamental and well-studied problems in the area of online algorithms. Innovative algorithmic ideas and analysis - including potential functions and primal-dual techniques - give insight into this still-growing area. Here, we introduce a new analysis technique that first uses a potential function to upper bound the cost of an online algorithm and then pairs that with a new dual-fitting strategy to lower bound the cost of an offline optimal algorithm. We apply these techniques to the Caching with Reserves problem recently introduced by Ibrahimpur et al. [Ibrahimpur et al., 2022] and give an O(log k)-competitive fractional online algorithm via a marking strategy, where k denotes the size of the cache. We also design a new online rounding algorithm that runs in polynomial time to obtain an O(log k)-competitive randomized integral algorithm. Additionally, we provide a new, simple proof for randomized marking for the classical unweighted paging problem.

In recent years, ultra-dense relay network (UDRN) has become a hot research topic. Mobile edge computing (MEC) can improve the performance of intelligent interactive applications in terms of time delay and energy consumption. In traditional MEC, MEC server in the base station (BS) is responsible for all the computing in the network. However, the MEC deployed in the BS may lead to huge computation burden on the BS and heavy traffic load through the network. Deploying MEC server in the relays of the network, which helps computing and caching for the users, will lower the burden of the BS, reduce the demand for traffic transmission through the network and improve the quality of service for users. This paper studies the problem of task caching, offloading and stable matching (SM) in UDRNs with MEC deployed in the relays, which is formulated as a multi-variable integer programming problem. To enable task caching and offloading, we propose an improved branch-and-bound algorithm and a low-complexity suboptimal algorithm to obtain the optimal and suboptimal schemes, respectively. Then, according to the principle of minimizing individual costs, the mutual preference lists between the users and relays are established. Based on the mutual preference lists, we propose an improved SM algorithm to obtain the user-relay SM results. Simulation results show that the proposed algorithms can bring considerable performance gains compared with conventional algorithms.

This paper explores algorithms to optimize networked caching, where requests for files can be handled by a local cache instead of a remote server. Caches work collaboratively to prevent redundant caching, and each new batch of file requests is used to update the entire network. Data is cached in an integral manner, meaning that only discrete files or chunks can be stored, not fractions of them. Bipartite networks are studied, though the proposed model supports arbitrary network topologies. The approach is based upon an Online Mirror Descent (OMD) policy, which has been shown to have sublinear regret in single cache scenarios., CSE3000 Research Project, Computer Science and Engineering

In multi-access edge computing (MEC) systems, there are several local cache servers caching contents to satisfy the users' requests, instead of letting the users download via the remote cloud server. In this paper, a content scheduling problem (CSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate cache servers, and thus all cells and their caching decisions must be jointly taken. We first prove that CSP is NP-hard, then we formulate CSP using integer linear programming. For problem solving, via a mathematical reformulation, we derive a column generation algorithm embedded into a rounding scheme. Our performance evaluation demonstrates that the solutions obtained are within 0.8% from global optimality.

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 N 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., QC 20220314

Content recommendation can be tailored by not only personal interests, but also the incumbent content, namely the content that a user is currently viewing. Incumbent-aware recommendation adds a new dimension to optimizing content caching. We study this optimization problem subject to user satisfaction constraints. We prove the problem's NP-hardness, and present an integer linear programming formulation that enables global optimality for small-scale instances. On the algorithmic side, we first present a polynomial-time algorithm that delivers the global optimum of the recommendation sub-problem, by leveraging the problem's inherent graph structure. Next, we propose a fast, alternating algorithm for the overall problem. Numerical results using synthesized and real-world data show the close-to-optimal performance of the proposed algorithm.

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source's signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB's trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB's trajectory and the UB's trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. \bl{To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method.} Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes., QC 20220525

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment's bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client's next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%., QC 20230920

The hippocampal formation (HF) processes spatial memories for cache locations in food-hoarding birds. Hoarding is a seasonal behavior, and seasonal changes in the HF have been described in some studies, but not in others. One potential reason is that birds may have been sampled during the seasonal hoarding peak in some studies, but not in others. In this study, we investigate the seasonal changes in hoarding and HF in willow tits (Poecile montanus). We compare this to seasonal changes in HF in a closely related non-hoarding bird, the great tit (Parus major). Willow tits near Oulu, Finland, show a seasonal hoarding peak in September and both HF volume and neuron number show a similar peak. HF neuronal density also increases in September, but then remains the same throughout winter. Unexpectedly, the great tit HF also changes seasonally, although in a different pattern: the great tit telencephalon increases in volume from July to August and decreases again in November. Great tit HF volume follows suit, but with a delay. Great tit HF neuron number and density also increase from August to September and stay high throughout winter. We hypothesize that seasonal changes in hoarding birds’ HF are driven by food-hoarding experience (e.g., the formation of thousands of memories). The seasonal changes in great tit brains may also be due to experience-dependent plasticity, responding to changes in the social and spatial environment. Large-scale experience-dependent neural plasticity is therefore probably not an adaptation of food-hoarding birds, but a general property of the avian HF and telencephalon.

Over the years, the Internet of Things has grown significantly and integrated into many fields such as medicine, agriculture, smart homes, etc. This growth has resulted in a significant increase in the amount of data generated. IoT devices are constrained by various factors, including transient data properties, limited memory, energy consumption, and computation power. Edge caching has been used as a solution to alleviate the problem caused by the increase while also improving service quality. Because of IoT constraints, cutting-edge caching policies have proven inefficient due to the file’s limited lifetime. Several caching methods have been proposed over the years to address ephemeral IoT data because data freshness plays a significant role in caching policy for IoT. It is not only essential to develop innovative technologies and solutions; we must also consider the long-term impact on the environment. This paper proposes a collaborative edge caching method to optimise transmission latency, traffic cost, and carbon footprint, thereby improving sustainability issues. A deep reinforcement learning approach is used where each edge learns its best caching policy. It is compared with a state-of-the-art cache replacement policy LRU, a DRL model proposed in another paper, and a model that doesn’t utilise caching policies. The simulation result proves that our proposed DRL-based IoT data caching policy outperforms other baseline policies.

Bu projede hücresel kablosuz sistemlerde baz istasyonu aracılıgı olmadan haberlesmeye yarayan cihazdan cihaza haberlesme teknolojisi üzerinde çalısılmıstır. Öncelikle cihazdan cihaza haberlesmenin hücresel haberlesme üzerinde yarattıgı girisim de hesaba katılarak güç ve kanal tahsisi üzerinde çalısılmıstır. Sonrasında ise son dönemde popüler bir arastırma konusu olan önbellekleme, cihazdan cihaza haberlesmenin oldugu aglarda ele alınmıstır. Bu çalısmalarda optimizasyon tabanlı bir yaklasım izlenmistir. Proje bir arastırma projesi olarak 215E123 numarasıyla TÜBITAK ARDEB tarafından desteklenmistir.

Today, billions of smart devices are interconnected via wireless networks, leading to large volumes of video contents circulating through the bandwidth-limited backhaul. This causes network performance to deteriorate. As a mitigation mechanism, caching of highly popular contents to network edges is deployed. We propose a cooperative and demand-aware caching strategy, which is modelled using the Separable Assignment Problem, to maximize the cache hit ratio. This problem is solved with a recursive enumeration method, where dynamic programming is used to fill each edge. The extensive application-level evaluations show that the proposed strategy outperforms existing caching policies., The work of Luis Alonso was supported in part by the Spanish Government under Project PID2021-125799OA-I00., Peer Reviewed, Postprint (author's final draft)

Caching is among the most well-studied topics in algorithm design, in part because it is such a fundamental component of many computer systems. Much of traditional caching research studies cache management for a single-user or single-processor environment. In this paper, we propose two related generalizations of the classical caching problem that capture issues that arise in a multi-user or multi-processor environment. In the caching with reserves problem, a caching algorithm is required to maintain at least k_i pages belonging to user i in the cache at any time, for some given reserve capacities k_i. In the public-private caching problem, the cache of total size k is partitioned into subcaches, a private cache of size k_i for each user i and a shared public cache usable by any user. In both of these models, as in the classical caching framework, the objective of the algorithm is to dynamically maintain the cache so as to minimize the total number of cache misses. We show that caching with reserves and public-private caching models are equivalent up to constant factors, and thus focus on the former. Unlike classical caching, both of these models turn out to be NP-hard even in the offline setting, where the page sequence is known in advance. For the offline setting, we design a 2-approximation algorithm, whose analysis carefully keeps track of a potential function to bound the cost. In the online setting, we first design an O(ln k)-competitive fractional algorithm using the primal-dual framework, and then show how to convert it online to a randomized integral algorithm with the same guarantee.

Mobile networks deal with an increasing portion of the IP Traffic due to the significant growth in the number of mobile devices and the accompanied lifestyle. A large fraction of this IP traffic is spent on duplicate transfers for the same resources. Previous work has shown that a Content Delivery Network (CDN) based on edge-nodes can reduce redundant backhaul traffic by storing popular content closer to the user. It also shows that a reduced user population per node, as in edge-based caching systems, can have a significant negative impact on caching performance. This effect is attributed to a reduced view on global content popularity. In this work we first create and evaluate a simulator for resource requests that is used to evaluate different caching strategies in an edge network. Our simulation confirms the findings of previous work and inspire three different caching strategies: Cooperative-LRU, User Profiles, and Hybrid with Federated. These strategies include mobility information to help alleviate the reduced knowledge on content popularity and help nodes work together more efficiently. Our results show that we are successful in decreasing the impact of the reduced population using the Cooperative LRU strategy and Profiles strategy. We then improve upon that performance by using a Hybrid strategy of Federated nodes and one of the mobility strategies., https://github.com/nbelzer/msc-thesis This repository contains the related source code for the dataset retrieval, trace generators, proposed strategies, and experiments in the work., Computer Science | Software Technology

We study the writeback-aware caching problem, a variant of classic paging where paging requests that modify data and requests that leave data intact are treated differently. We give an O(log^2 k) competitive randomized algorithm, answering an open question of Beckmann et al. [8] and Even et a

The purpose of this study is to compare the two architectural techniques REST and GraphQL. This thesis will compare the two techniques and what defines them. A literature study and experimental study are carried out by the researchers. Four applications have been developed that include the ability to disable and to enable caching for both technologies to test the performance effect of caching. Earlier work has not covered the effects on caching related to these two frameworks. The literature study results point to that REST services are up to date and GraphQL is a technique with a shorter history, but that has declared growth in the industry and is a well suited choice for example when bandwidth matters in mobile phone applications. In the experimental study the tests showed slightly better results on average for REST API in terms of total response time (ms). Depending on the intended project there are many factors that need to be evaluated before making a decision on which framework to use.

Progressive Web Apps (PWA) is a concept of enhanced web apps which aim to erase the difference between web and native apps. The concept of PWA introduces several benefits, including simpler distribution and development, which makes web apps a viable option for many businesses. The installation process of a PWA is different from native apps and this study aims to understand whether or not it has an impact on the users will to install the PWA. PWA:s are installed directly from the browser, rather than an app marketplace. Said installation process is suspected to be unknown to many which might influence the impact. In this study, several papers on the topic are studied. A PWA is developed from scratch which is used as a platform for a user test, hosting a brief introduction of the topic as well as guiding participants through the installation process. In conjunction with the user test a survey is conducted to collect the impressions from their experience in the user test. The result of the survey shows indications that the suspected lack of knowledge and experience was correct, and that the installation process is not to complicated.Additionally, results show that many probably will not bother to install a PWA, even if it is available. Therefore, I argue that the installation process does have an impact in its current form. Alternatives to the installation process used in this study do exist and are discussed in this paper.

Distributed data stream processing is a popular research area and is one of the promising paradigms for faster and efficient data management. Application state is a first-class citizen in nearly every stream processing system. Nowadays, stream processing is, by definition, stateful. For a stream processing application, the state is backing operations such as aggregations, joins, and windows. Apache Flink is one of the most accepted and widely used stream processing systems in the industry. One of the main reasons engineers choose Apache Flink to write and deploy continuous applications is its unique combination of flexibility and scalability for stateful programmability, and the firm guarantee that the system ensures. Apache Flink’s guarantees always make its states correct and consistent even when nodes fail or when the number of tasks changes. Flink state can scale up to its compute node’s hard disk boundaries using embedded databases to store and retrieve data. Nevertheless, in all existing state backends officially supported by Flink, the state is always available locally to compute tasks. Even though this makes deployment more convenient, it creates other challenges such as non-trivial state reconfiguration and failure recovery. At the same time, compute, and state are bound to be tightly coupled. This strategy also leads to over-provisioning and is counterintuitive on state intensive only workloads or compute-intensive only workloads. This thesis investigates an alternative state backend architecture, FlinkNDB, which can tackle these challenges. FlinkNDB decouples state and computes by using a distributed database to store the state. The thesis covers the challenges of existing state backends and design choices and the new state backend implementation. We have evaluated the implementation of FlinkNDB against existing state backends offered by Apache Flink., Distribuerad dataströmsbehandling är ett populärt forskningsområde och är ett av de lovande paradigmen för snabbare och effektivare datahantering. Applicationstate är en förstklassig medborgare i nästan alla strömbehandlingssystem. Numera är strömbearbetning per definition statlig. För en strömbehandlingsapplikation backar staten operationer som aggregeringar, sammanfogningar och windows. Apache Flink är ett av de mest accepterade och mest använda strömbehandlingssystemen i branschen. En av de främsta anledningarna till att ingenjörer väljer ApacheFlink för att skriva och distribuera kontinuerliga applikationer är dess unika kombination av flexibilitet och skalbarhet för statlig programmerbarhet, och företaget garanterar att systemet säkerställer. Apache Flinks garantier gör alltid dess tillstånd korrekt och konsekvent även när noder misslyckas eller när antalet uppgifter ändras. Flink-tillstånd kan skala upp till dess beräkningsnods hårddiskgränser genom att använda inbäddade databaser för att lagra och hämta data. I allmänna tillståndsstöd som officiellt stöds av Flink är staten dock alltid tillgänglig lokalt för att beräkna uppgifter. Även om detta gör installationen bekvämare, skapar det andra utmaningar som icke-trivial tillståndskonfiguration och felåterställning. Samtidigt måste beräkning och tillstånd vara tätt kopplade. Den här strategin leder också till överanvändning och är kontraintuitiv för statligt intensiva endast arbetsbelastningar eller beräkningsintensiva endast arbetsbelastningar. Denna avhandling undersöker en alternativ statsbackendarkitektur, FlinkNDB, som kan hantera dessa utmaningar. FlinkNDB frikopplar tillstånd och beräknar med hjälp av en distribuerad databas för att lagra tillståndet. Avhandlingen täcker utmaningarna med befintliga statliga backends och designval och den nya implementeringen av statebackend. Vi har utvärderat genomförandet av FlinkNDBagainst befintliga statliga backends som erbjuds av Apache Flink.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system., Funding: Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research; Swedish Research Council (VR)Swedish Research Council; Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund in Information Technology (ELLIIT); Indo-Sweden Project, Department of Science and Technology, India

Conventionally, caching algorithms have been designed for the datapath — the levels of memory that must contain the data before it gets made available to the CPU. Attaching a fast device (such as an SSD) as a cache to a host that runs the application workload are recent developments. These host-side caches open up possibilities for what are referred to as non-datapath caches to exist. Non-Datapath caches are referred to as such because the caches do not exist on the traditional datapath, instead being optional memory locations for data. As these caches are optional, a new capability is available to caching algorithms that manage these caches: not caching at all and instead bypassing the cache entirely for an access. With this option, items may not get inserted into the cache, which is an outcome that is beneficial to the lifetime of cache devices that can only sustain a limited number of writes before they degrade and become unusable. We propose several non- datapath caching algorithms that are optimized to make these choices and achieve high hit-rates while reducing writes: mARC, FOMO, and ANX. We also propose a polynomial time optimal solution to non-datapath caches: mOPT. mOPT provides an optimal solution for non-datapath caches that can maximize hits while minimizing writes. This provides further insight into how appropriate non-datapath caches are for specific workloads, if at all.

Магістерська дисертація на тему “ Особливості оброблення сигналів з використанням ресурсів графічних адаптерів”. Обсяг пояснювальної записки 85 сторінки, вони міститять 5 розділів, 18 ілюстрацій, 26 таблиці, 23 джерела в переліку посилань та 1 додаток з лістингом програми. Метою даної роботи є створення графічної утиліти, яка спроможна реалізувати власні рішення в сфері графічної обробки сигналу. В роботі розглянуто різні види обробки та пост обробки сигналів, доступні графічні API та наявні бібліотеки. Обрано та протестовано новий вид анти-алиасингу та алгоритмів на відміну стандартним запропонованим програмним забезпеченням Nvidia, а також протестовано метод виконання графічним процесором функцій оброблення сигналів без використання ресурсів центрального процесора., Master's dissertation on "Features of signal processing using the resources of graphics adapters." The volume of the explanatory note is 85 pages, they contain 5 sections, 18 illustrations, 26 tables, 23 sources in the list of links and 1 appendix with the program listing. The purpose of this work is to create a graphical utility that is able to implement their own solutions in the field of graphical signal processing. The paper considers different types of signal processing and post-processing, available graphical APIs and available libraries. A new type of anti-aliasing and algorithms in contrast to the standard Nwidia software were selected and tested, as well as a method of performing signal processing by a GPU of CPU functions was tested.

In this paper, we study the problem of optimal scheduling of content placement along time in a base station with limited cache capacity, taking into account jointly the offloading effect and freshness of information. We model offloading based on popularity in terms of the number of requests and information freshness based on the notion of age of information (AoI). The objective is to reduce the load of backhaul links as well as the AoI of contents in the cache via a joint cost function. For the resulting optimization problem, we prove its hardness via a reduction from the Partition problem. Next, via a mathematical reformulation, we derive a solution approach based on column generation and a tailored rounding mechanism. Finally, we provide performance evaluation results showing that our algorithm provides near-optimal solutions.

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs., QC 20210113

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path-traced results, but with a greatly reduced computational complexity, allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering., Comp Graphics & Visualisation

Using transfer learning, convolutional neural networks for different purposes can have similar layers which can be reused by caching them, reducing their load time. Four ways of loading and executing these layers, bulk, linear, DeepEye and partial loading, were analysed under different memory constraints and different amounts of similar networks. When there is sufficient memory, caching will decrease the loading time and will always influence the single threaded bulk and linear mode. On the multithreaded approaches this only holds when the loading time is longer than the execution time. This depends largely on what network will be run. When memory constraints are applied caching can be a way to still run multiple networks without much increased cost. It can also be opted to use less memory on a device and use transfer learning with caching to still get the same results., Optimizing Multiple Deep Learning Models on Edge Devices, CSE3000 Research Project, Computer Science and Engineering

Decision trees are among the most popular classification models in ma- chine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms pose several disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning de- cision trees. In this paper, we introduce a new approach to learn decision trees using constraint programming. Compared to earlier approaches, we show that our approach obtains better performance, while still being sufficiently flexible to allow for the inclusion of constraints. Our approach builds on three key building blocks: (1) the use of AND/OR search, (2) the use of caching, (3) the use of the CoverSize global constraint proposed recently for the problem of itemset mining. This allows our constraint programming approach to deal in a much more efficient way with the decompositions in the learning problem.

Video streaming is used daily by people around the world, plays a big role in many Internet users daily lives, and are today responsible for the majority of the Internet traffic. As 360 video streaming services become increasingly popular and each such user session requires much higher bandwidths than traditional video streaming, optimized solutions for this type of video is becoming increasingly important. One method that has been proposed to reduce the bandwidth usage is the usage of proxy servers. In this thesis, we evaluate custom-adapted prefetching policies that tries to improve the users Quality of Experience (QoE). Defining a prefetching policy for something adaptive as 360 video brings challenges that need to be simulated before release in the real world. Without proper testing the prefetch policy can do more harm than good by flooding the network with unnecessary amount of transmissions. Prior research has shown that the QoE of HTTP-based adaptive streaming (HAS) clients can be improved with content-aware prefetching (e.g., Krishnamoothi et al. 2013). However, there have been limited prior work adapting and evaluating such policies in the context of 360 streaming. This thesis presents a simulation-based evaluation of proxy-assisted 360solutions that includes custom designed prefetching polices. The main contributions of the thesis are as follows:First, we implement four types of proxy-assisted prefetching policies and simulate these under two scenarios with different networks conditions. One scenario simulate a network environment with a bottleneck located between the client and proxy while the other scenario simulates a network environment with the bottleneck located between the proxy and server. The cooperation between the client and proxy is evaluated for each scenario and prefetching policy. Second, we evaluate the proxy-assisted prefetching policies in comparison with baselines and each other, in regards of their ability to improve the viewers QoE. Ou

The recent trends in the domain of wireless communications indicate severe upcoming challenges, both in terms of infrastructure as well as design of novel techniques. On the other hand, the world population keeps witnessing or hearing about new generations of mobile/wireless technologies within every half to one decade. It is certain the wireless communication systems have enabled the exchange of information without any physical cable(s), however, the dependence of the mobile devices on the power cables still persist. Each passing year unveils several critical challenges related to the increasing capacity and performance needs, power optimization at complex hardware circuitries, mobility of the users, and demand for even better energy efficiency algorithms at the wireless devices. Moreover, an additional issue is raised in the form of continuous battery drainage at these limited-power devices for sufficing their assertive demands. In this regard, optimal performance at any device is heavily constrained by either wired, or an inductive based wireless recharging of the equipment on a continuous basis. This process is very inconvenient and such a problem is foreseen to persist in future, irrespective of the wireless communication method used. Recently, a promising idea for simultaneous wireless radio-frequency (RF) transmission of information and energy came into spotlight during the last decade. This technique does not only guarantee a more flexible recharging alternative, but also ensures its co-existence with any of the existing (RF-based) or alternatively proposed methods of wireless communications, such as visible light communications (VLC) (e.g., Light Fidelity (Li-Fi)), optical communications (e.g., LASER-equipped communication systems), and far-envisioned quantum-based communication systems. In addition, this scheme is expected to cater to the needs of many current and future technologies like wearable devices, sensors used in hazardous areas, 5G and beyond, et

The fifth generation (5G) wireless networks have to deal with the high data rate and stringent latency requirements due to the massive invasion of connected devices and data-hungry applications. Edge caching is a promising technique to overcome these challenges by prefetching the content closer to the end users at the edge node’s local storage. In this paper, we analyze the performance of edge caching 5G networks with the aid of satellite communication systems. Firstly, we investigate the satellite-aided edge caching systems in two promising use cases: a) in dense urban areas, and b) in sparsely populated regions, e.g., rural areas. Secondly, we study the effectiveness of satellite systems via the proposed satellite-aided caching algorithm, which can be used in three configurations: i) mono-beam satellite, ii) multi-beam satellite, and iii) hybrid mode. Thirdly, the proposed caching algorithm is evaluated by using both empirical Zipf-distribution data and the more realistic Movielens dataset. Last but not least, the proposed caching scheme is implemented and tested by our developed demonstrators which allow real-time analysis of the cache hit ratio and cost analysis.

Recently, there has been substantial progress in the formal understanding of how caching resources should be allocated when multiple caches each deploy the common LRU policy. Nonetheless, the role played by caching policies beyond LRU in a networked setting where content may be replicated across multiple caches and where channels are unreliable is still poorly understood. In this paper, we investigate this issue by first analyzing the cache miss rate in a system with two caches of unit size each, for the LRU, and the LFU caching policies, and their combination. Our analytical results show that joint use of the two policies outperforms LRU, while LFU outperforms all these policies whenever resource pooling is not optimal. We provide empirical results with larger caches to show that simple alternative policies, such as LFU, provide superior performance compared to LRU even if the space allocation is not fine tuned. We envision that fine tuning the cache space used by such policies may lead to promising additional gains., QC 20210323

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end-user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing andML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks, Postprint (published version)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Content caching has been considered by both academia and industry as an efficient solution to tackle the problem of the back-haul becoming the bottleneck in the service of users in future heterogeneous cellular networks. Most of the related caching-oriented studies are based on the content popularity, overlooking the impact of content size on their analysis. In this context, this work studies content caching in an environment where cellular users are equipped with cache memories. In particular, we formulate the content caching as an optimization problem, where the objective is to minimize the average download latency of popular videos through self-caching and device-to-device (D2D) caching and, consequently, increase the network throughput. In addition, in order to solve this problem in real-time scenarios, we introduce a low-complexity utility-based algorithm, which accounts for parameters such as the size and the popularity of the requested contents, as well as the density of the end users. Finally, we provide extensive simulation results that validate our analysis and prove that our innovative scheme outperforms other existing solutions., Peer Reviewed, Postprint (author's final draft)

In this work, we consider a system where external requests arrive for status updates of a remote source, which is monitored by an energy harvesting (EH) sensor. The requests are placed in an aggregator that has direct communication with the sensor and is also equipped with storage space to cache a previously generated status update. A probabilistic model is considered to determine whether a new request will be served with a fresh update from the EH sensor or with a cached update from the aggregator. In the first case, the fresh update will replace the cached one in the aggregator. Assuming that the energy arrivals at the sensor can be modeled by a Bernoulli process, we characterize the average Age of Information (AoI) of the source seen at the aggregator as a function of the external request probability, the battery charging probability, and the probability that a fresh update will be generated by the EH sensor. Our numerical results reveal some insights about the role of caching in EH-based status updating systems.

In this thesis, it is aimed to calculate the time required for users to access their files by using their own cache and base stations. In the first part of the thesis, the previous studies are examined and the differences, similarities and shortcomings of the two level architecture are determined. It is determined that there is no delay analysis for the situation where users use both their own cache and the cache of the base station. In the continuation of the thesis, delay analysis is performed for the base stations in the spatial domain. Then, the scope of the system is expanded. A two-level architecture is created. Delay analysis is performed for the users where they use both their own cache and the base station caches. The use of base stations along with other users has increased the efficiency considerably.An intelligent caching technique is found to minimize latency. The optimization framework determines the most suitable cache placement according to the users' requests. As a result of the calculations, a lower limit was determined as the latency and a realistic latency is found by using the lower bound. The two layer architecture is superior compared to using only device caches or only base station caches., Bu tez çalışmasında, kullanıcıların kendi hafızalarını ve baz istasyonlarını kullanarak, istedikleri dosyalara ulaşabilmeleri için gereken süre hesaplanmak istenmiştir. Tezin ilk kısmında, daha önce yapılan çalışmalar incelenmiştir ve birbirleri arasındaki farkları, benzerlikleri ve iki seviyeli mimari için gecikme analizi yapılması için eklenmesi gereken eksikleri tespit edilmiştir. Kullanıcıların hem kendi hafızasını hem de baz istasyonunun hafızasını kullandığı durum için gecikme analizi yapılmadığı tespit edilmiş. Tezin devamında ise, uzamsal yaklaşım yöntemini kullanarak baz istasyonları için gecikme analizi yapılmıştır. Daha sonrasında ise sistemin kapsamı genişletilmiştir. İki seviyeli mimari oluşturulmuştur. Kullanıcıların hem kendi hafızasını hem de baz istasyonunun hafızasını kullandığı durum için gecikme analizi yapılmıştır. Baz istasyonlarının kullanıcı hafızaları ile birlikte kullanılması verimliliği oldukça arttırmıştır. Bu gecikme süresini en aza indirmek için akıllı önbellekleme tekniği bulunmuştur. Kullanıcıların isteklerine göre en uygun önbellekleme yöntemini optimizasyon ile tespit etmektedir. Hesaplamalar sonucunda gecikme süresi olarak bir alt sınır belirlenmiştir ve bu alt sınır değeri kullanılarak gerçekçi bir gecikme süresi bulunmuştur. Sadece kullanıcıların hafızasının ya da sadece baz istasyonlarının kullanıldığı durumlara göre iki seviyeli yapı oldukça verimli çalışmaktadır.

In this study we consider the problem of cache placement in a device-to-device (D2D) enabled cellular network with mobile users. D2D communication occurs between helper users and ordinary users, where helpers are able to cache contents and deliver these contents to ordinary users via D2D transmission upon request. The objective of the cache placement is maximizing the offloaded traffic and lighten the burden of the Base Station. We first formulate a single-shot cache placement problem as a Mixed Integer Linear Program (MILP) and obtain the optimal solution. We also propose three clustering algorithms and a cache placement algorithm. Then we tested the performance of these schemes under user and helper mobility. Cache placement is recomputed at a certain cache update periods, in order to take into account the changing topology. Simulation results show that the optimization framework designed for a single-shot scenario fails at challenging conditions, while the proposed algorithms is more robust to mobility. © 2019 IEEE.