arXiv subjects

Faheem Zafari

Publications and source records attributed to Faheem Zafari.

10 recordsLinked to original sources

arXiv2020

A TTL-based Approach for Content Placement in Edge Networks

Edge networks are promising to provide better services to users by provisioning computing and storage resources at the edge of networks. However, due to the uncertainty and diversity of user interests, content popularity, distributed network structure, cache sizes, it is challenging to decide where to place the content, and how long it should be cached. In this paper, we study the utility optimization of content placement at edge networks through timer-based (TTL) policies. We propose provably optimal distributed algorithms that operate at each network cache to maximize the overall network utility. Our TTL-based optimization model provides theoretical answers to how long each content must be cached, and where it should be placed in the edge network. Extensive evaluations show that our algorithm significantly outperforms path replication with conventional caching algorithms over some network topologies.

cs.NI↗

arXiv2020

Resource Sharing in the Edge: A Distributed Bargaining-Theoretic Approach

The growing demand for edge computing resources, particularly due to increasing popularity of Internet of Things (IoT), and distributed machine/deep learning applications poses a significant challenge. On the one hand, certain edge service providers (ESPs) may not have sufficient resources to satisfy their applications according to the associated service-level agreements. On the other hand, some ESPs may have additional unused resources. In this paper, we propose a resource-sharing framework that allows different ESPs to optimally utilize their resources and improve the satisfaction level of applications subject to constraints such as communication cost for sharing resources across ESPs. Our framework considers that different ESPs have their own objectives for utilizing their resources, thus resulting in a multi-objective optimization problem. We present an $N$-person \emph{Nash Bargaining Solution} (NBS) for resource allocation and sharing among ESPs with \emph{Pareto} optimality guarantee. Furthermore, we propose a \emph{distributed}, primal-dual algorithm to obtain the NBS by proving that the strong-duality property holds for the resultant resource sharing optimization problem. Using synthetic and real-world data traces, we show numerically that the proposed NBS based framework not only enhances the ability to satisfy applications' resource demands, but also improves utilities of different ESPs.

cs.GT↗

arXiv2020

Let's Share: A Game-Theoretic Framework for Resource Sharing in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitive applications. This is a challenging problem as an edge cloud-service provider may not have sufficient resources to satisfy all resource requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different edge cloud-service providers can address the aforementioned limitation as certain service providers may have resources available that can be ``rented'' by other service providers. However, edge cloud service providers can have different objectives or \emph{utilities}. Therefore, there is a need for an efficient and effective mechanism to share resources among service providers, while considering the different objectives of various providers. We model resource sharing as a multi-objective optimization problem and present a solution framework based on \emph{Cooperative Game Theory} (CGT). We consider the strategy where each service provider allocates resources to its native applications first and shares the remaining resources with applications from other service providers. We prove that for a monotonic, non-decreasing utility function, the game is canonical and convex. Hence, the \emph{core} is not empty and the grand coalition is stable. We propose two algorithms \emph{Game-theoretic Pareto optimal allocation} (GPOA) and \emph{Polyandrous-Polygamous Matching based Pareto Optimal Allocation} (PPMPOA) that provide allocations from the core. Hence the obtained allocations are \emph{Pareto} optimal and the grand coalition of all the service providers is stable. Experimental results confirm that our proposed resource sharing framework improves utilities of edge cloud-service providers and application request satisfaction.

cs.NI↗

arXiv2019

A Game-Theoretic Framework for Resource Sharing in Clouds

Providing resources to different users or applications is fundamental to cloud computing. This is a challenging problem as a cloud service provider may have insufficient resources to satisfy all user requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different cloud service providers can improve resource availability and resource utilization as certain cloud service providers may have free resources available that can be ``rented'' by other service providers. However, different cloud service providers can have different objectives or \emph{utilities}. Therefore, there is a need for a framework that can share and allocate resources in an efficient and effective way, while taking into account the objectives of various service providers that results in a \emph{multi-objective optimization} problem. In this paper, we present a \emph{Cooperative Game Theory} (CGT) based framework for resource sharing and allocation among different service providers with varying objectives that form a coalition. We show that the resource sharing problem can be modeled as an $N-$player \emph{canonical} cooperative game with \emph{non-transferable utility} (NTU) and prove that the game is convex for monotonic non-decreasing utilities. We propose an $\mathcal{O}({N})$ algorithm that provides an allocation from the \emph{core}, hence guaranteeing \emph{Pareto optimality}. We evaluate the performance of our proposed resource sharing framework in a number of simulation settings and show that our proposed framework improves user satisfaction and utility of service providers.

cs.MA↗

arXiv2019

Optimal Energy Consumption with Communication, Computation, Caching and QoI-Guarantee

Energy efficiency is a fundamental requirement of modern data communication systems, and its importance is reflected in much recent work on performance analysis of system energy consumption. However, most works have only focused on communication and computation costs, but do not account for caching costs. Given the increasing interest in cache networks, this is a serious deficiency. {In this paper, we consider the problem of energy consumption in data communication, compression and caching (C$3$) with a Quality of Information (QoI) guarantee in a communication network. {Our goal is to identify the optimal data compression rate and data placement over the network to minimize the overall energy consumption in the network.} he formulated problem is a \emph{Mixed Integer Non-Linear Programming} (MINLP) problem with non-convex functions, which is NP-hard in general. } {We} propose a variant of spatial branch and bound algorithm (V-SBB), that can {provide} the $ε$-global optimal solution to {our problem}. {We numerically show that our C3 optimization framework can improve the energy efficiency up to 88\% compared to any C2 optimization between communication and computation or caching. Furthermore, for our energy consumption problem, V-SBB {provides comparatively better solution than some other MINLP solvers.}}

cs.NI↗

arXiv2019

A Survey of Indoor Localization Systems and Technologies

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

cs.NI↗

arXiv2018

A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitive applications. However, allocating the limited edge resources to a number of applications is a challenging problem. To alleviate the resource scarcity problem, we propose sharing of resources among multiple edge computing service providers where each service provider has a particular utility to optimize. We model the resource allocation and sharing problem as a multi-objective optimization problem and present a \emph{Cooperative Game Theory} (CGT) based framework, where each edge service provider first satisfies its native applications and then shares its remaining resources (if available) with users of other providers. Furthermore, we propose an $\mathcal{O}(N)$ algorithm that provides allocation decisions from the \emph{core}, hence the obtained allocations are \emph{Pareto} optimal and the grand coalition of all the service providers is stable. Experimental results show that our proposed resource allocation and sharing framework improves the utility of all the service providers compared with the case where the service providers are working alone (no resource sharing). Our $\mathcal{O}(N)$ algorithm reduces the time complexity of obtaining a solution from the core by as much as 71.67\% when compared with the \emph{Shapley value}.

cs.GT↗

arXiv2018

Joint Data Compression and Caching: Approaching Optimality with Guarantees

We consider the problem of optimally compressing and caching data across a communication network. Given the data generated at edge nodes and a routing path, our goal is to determine the optimal data compression ratios and caching decisions across the network in order to minimize average latency, which can be shown to be equivalent to maximizing the compression and caching gain under an energy consumption constraint. We show that this problem is NP-hard in general and the hardness is caused by the caching decision subproblem, while the compression sub-problem is polynomial-time solvable. We then propose an approximation algorithm that achieves a $(1-1/e)$-approximation solution to the optimum in strongly polynomial time. We show that our proposed algorithm achieve the near-optimal performance in synthetic-based evaluations. In this paper, we consider a tree-structured network as an illustrative example, but our results easily extend to general network topology at the expense of more complicated notations.

cs.NI↗

arXiv2017

Micro-location for Internet of Things equipped Smart Buildings

Micro-location is the process of locating any entity with high accuracy (possibly in centimeters), while geofencing is the process of creating a virtual fence around a so-called Point of Interest (PoI). In this paper, we present an insight into various micro-location enabling technologies and services. We also discuss how these can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location based location-aware solutions can play a significant role in facilitating the tenants of an IoT equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using micro-location enabling technologies. We describe the challenges and propose some potential solutions such that micro-location enabling technologies and services are thoroughly integrated with IoT equipped smart building.

cs.OH↗

arXiv2017

An iBeacon based Proximity and Indoor Localization System

Indoor localization and Location Based Services (LBS) can greatly benefit from the widescale proliferation of communication devices. The basic requirements of a system that can provide the aforementioned services are energy efficiency, scalability, lower costs, wide reception range, high localization accuracy and availability. Different technologies such as WiFi, UWB, RFID have been leveraged to provide LBS and Proximity Based Services (PBS), however they do not meet the aforementioned requirements. Apple's Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide Proximity Based Services (PBS). However, it suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI) that is prone to multipath fading and drastic fluctuations in the indoor environment. Therefore, in this paper, we present our iBeacon based accurate proximity and indoor localization system. Our two algorithms Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity detection accuracy of iBeacons by 29% and 32% respectively, when compared with Apple's current moving average based approach. We also present our novel cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce the RSSI fluctuation and then inputs the filtered RSSI values into a Particle Filter (PF) to improve the accuracy of indoor localization. Our experimental results, obtained through experiments in a space replicating real-world scenario, show that our cascaded filter approach outperforms the use of only PF by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments respectively, and achieves a localization error as low as 0.70 meters in 2D environment and 0.947 meters in 3D environment.

cs.NI↗