1,297 research outputs found
An optimal VM Placement, Energy Efficient and SLA at Cloud Environment - A Comparative Analysis
In the cloud computing framework, computing resources can be increased or decreased in response to the users’ different application loads. The data is stored and the applications are running on the servers in the clouds. Users do not have to worry about lost or corrupt data. The clouds can distribute computing resources according to the users’ needs or preferences to provide fl exible management. Users do not have to buy expensive computing devices. They only need to pay for the computing services provided by the clouds. Cloud computing provides a platform for computational experiments with abundant computing and storage resources. The system can be considered as a whole and the control and management decisions are sent as services to agents. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level
Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments
With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas.
With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation.
Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool.
The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction
Machine Learning Algorithms for Provisioning Cloud/Edge Applications
MenciĂłn Internacional en el tĂtulo de doctorReinforcement Learning (RL), in which an agent is trained to make the most
favourable decisions in the long run, is an established technique in artificial intelligence. Its
popularity has increased in the recent past, largely due to the development of deep neural
networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The
latter have been used to solve previously insurmountable problems, such as playing the
famed game of “Go” that previous algorithms could not. Many such problems suffer the
curse of dimensionality, in which the sheer number of possible states is so overwhelming
that it is impractical to explore every possible option.
While these recent techniques have been successful, they may not be strictly necessary
or practical for some applications such as cloud provisioning. In these situations, the
action space is not as vast and workload data required to train such systems is not
as widely shared, as it is considered commercialy sensitive by the Application Service
Provider (ASP). Given that provisioning decisions evolve over time in sympathy to
incident workloads, they fit into the sequential decision process problem that legacy RL
was designed to solve. However because of the high correlation of time series data, states
are not independent of each other and the legacy Markov Decision Processes (MDPs)
have to be cleverly adapted to create robust provisioning algorithms.
As the first contribution of this thesis, we exploit the knowledge of both the application
and configuration to create an adaptive provisioning system leveraging stationary Markov
distributions. We then develop algorithms that, with neither application nor configuration
knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning
systems. Our Q-Learning algorithms factor in the correlation between states and the
consequent transitions between them to create provisioning systems that do not only
adapt to workloads, but can also exploit similarities between them, thereby reducing
the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps
given that we restructure the state and action spaces to avoid the curse of dimensionality
without the need for the function approximation approach taken by deep Q-Learning
systems.
A crucial use-case of future networks will be the support of low-latency applications
involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC)
architecture, in which computing capabilities can be located close to the network edge,
where the data is generated. Provisioning for such applications therefore entails migrating
them to the most suitable location on the network edge as the users move. In this thesis,
we also tackle this type of provisioning by considering vehicle platooning or Cooperative
Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm
can be adapted to minimize the number of migrations required to effectively run such
an application on MEC hosts, which may also be subject to traffic from other competing
applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierĂa Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell
Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks
These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, ETrain and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, roundtrip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.publishedVersio
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
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