7,225 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
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QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING
A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs.
The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs.
In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications
Routing and caching on DHTS
L'obiettivo della tesi e' quello di analizzare i principali meccanismi di caching e routing implementati oggigiorno nelle DHT piu' utilizzate.
In particolare, la nostra analisi mostra come tali meccanismi siano sostanzialmente inefficaci nel garantire un adeguato load balancing tra i peers; le principali cause di questo fenomeno sono individuate nella struttura, eccessivamente rigida, adottata dalle DHT e nella mancanza di correlazione tra meccanismi di routing e di caching.
Viene quindi proposto un diverso overlay, organizzato in base a una struttura ipercubica, che permetta di adottare un algoritmo di routing piu' flessibile e di sviluppare due meccanismi di caching e routing strettamente interconnessi.
In particolare, l'overlay ottenuto riesce a garantire che ogni nodo subisca un carico al piu' costante, con una taglia di cache costante e una complessita' di routing polilogaritmica nel caso peggior
A high bit rate flexible MAC protocol for monitoring applications using 60ghz radio technology
In recent years there has been a growing trend in optical wireless convergence.
One particular aspect of this is 60 GHz radio-over-fiber technology. It is intended for use
in wireless personal area networks. However, we think that the same technology could be
used for monitoring applications in the indoor environment. It could be used to detect
emergency situations or to detect intruders. We shall examine reasons why this choice
might be a suitable one. We shall then propose a MAC layer protocol to accomplish this
task. Since in case of emergency we might require to obtain data from only one node for
an extended duration, flexibility in implementation is required. We shall develop an
adaptive MAC protocol where this would be possible. We accomplish this by including
two protocol modes called the Icarus mode, which is to be used in case of an emergency
and the Resync mode which is used when normality is restored. A significant problem at
high frequencies is that the beam becomes increasingly narrow and behaves more in a ray
like condition. This implies that particularly in an indoor environment it is possible that
the beam may be accidentally blocked. In this case the node must be able shift the beam
in order to enable communication. We demonstrate three such strategies and offer a
comparative analysis.M.S.Committee Chair: Chang, Gee-Kung; Committee Member: Barry , John; Committee Member: Gaylord, To
Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement
With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues.
First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems
Enabling Distributed Applications Optimization in Cloud Environment
The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud.
In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications
Efficient resource management for virtual desktop cloud computing
In virtual desktop cloud computing, user applications are executed in virtual desktops on remote servers. This offers great advantages in terms of usability and resource utilization; however, handling a large amount of clients in the most efficient manner poses important challenges. Especially deciding how many clients to handle on one server, and where to execute the user applications at each time is important. Assigning too many users to one server leads to customer dissatisfaction, while assigning too little leads to higher investments costs. We study different aspects to optimize the resource usage and customer satisfaction. The results of the paper indicate that the resource utilization can increase with 29% by applying the proposed optimizations. Up to 36.6% energy can be saved when the size of the online server pool is adapted to the system load by putting redundant hosts into sleep mode
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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