Quality Of Service Expectations Research Articles

A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios

To fulfill people's expectations for smart and user-friendly Internet of Things (IoT) applications, the quantity of processing is fast expanding, and task latency constraints are becoming extremely rigorous. On the other hand, the limited battery capacity of IoT objects severely affects the user experience. Energy Harvesting (EH) technology enables green energy to offer a continuous energy supply for IoT objects. It provides a solid assurance for the proper functioning of resource-constrained IoT objects when combined with the maturation of edge platforms and the development of parallel computing. The Markov Decision Process (MDP) and Deep Learning (DL) are used in this work to solve dynamic online/offline IoT-edge offloading scenarios. The suggested system may be used in both offline and online contexts and meets the user's quality of service expectations. Also, we investigate a blockchain scenario in which edge and cloud could work toward task offloading to address the tradeoff between limited processing power and high latency while ensuring data integrity during the offloading process. We provide a double Q-learning solution to the MDP issue that maximizes the acceptable offline offloading methods. During exploration, Transfer Learning (TL) is employed to quicken convergence by reducing pointless exploration. Although the recently promoted Deep Q-Network (DQN) may address this space complexity issue by replacing the huge Q-table in standard Q-learning with a Deep Neural Network (DNN), its learning speed may still be insufficient for IoT apps. In light of this, our work introduces a novel learning algorithm known as deep Post-Decision State (PDS)-learning, which combines the PDS-learning approach with the classic DQN. The system component in the proposed system can be dynamically chosen and modified to decrease object energy usage and delay. On average, the proposed technique outperforms multiple benchmarks in terms of delay by 4.5%, job failure rate by 5.7%, cost by 4.6%, computational overhead by 6.1%, and energy consumption by 3.9%.

Computation offloading model for smart factory

Internet of Things (IoT) is coming up in a rapid pace in various application domains. In smart factories, IoT can be deployed using sensors and actuators for taking smart manufacturing decisions. To maintain the smartness, huge computational power is required to handle the generated data by the IoT sensors. Local servers, in smart factories, usually organize the sensors/actuators and takes decisions at the local level. However, they are not equipped with enough computational power to handle all types of computational tasks and therefore some tasks need to be offloaded to the upper layer such as Cloud. Hybrid cloud i.e. public cloud along with some local servers can better handle this requirement. Recently, an additional layer called fog computing is introduced in the cloud architecture to complement it with added power. Offloading of tasks, generated by the industrial applications of IoT devices, should be done only when existing computational power of local server is not able to meet the quality requirements of the tasks. An ultimate objective of the smart factory owner is to earn revenue and for that, IoT devices need to meet their quality of service expectation. For offloading, tasks can be categorized as delay sensitive and delay tolerant and making decision on offloading by the local server is non-trivial. This work proposes an offloading decision model using game theory in a non-cooperative environment considering the categorization of tasks and it is shown that dominant strategy exists for the local server. For the performance study of the proposed model, simulation is done using iFogSim simulator. A comparative study with state-of-art exhibits that the proposed offloading scheme outperforms.

Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

Abstract Cloud computing as a model, provides an environment for execution of large computationally or data intensive task under some agreed Service Level Agreement (SLA). Today, the rate of cloud adoption by organizations has immensely increased due to its reliability, reduced cost and performance. This increase necessitates the need for efficient resource scheduling technique that ensure Quality of Service (QoS) delivery, optimize resource usage and maintain the agreed SLA. Researchers in the literature have proposed several techniques that maps complex task to cloud resources considering QoS requirements which records significant improvements with the need for more ideal solutions. This paper proposed an efficient SLA violation preventive task scheduling to optimize cloud resources, ensure QoS delivery while maintaining the agreed SLA. Processing capacity, bandwidth, storage, execution time, waiting time and task deadline are considered as QoS parameters for mapping of task to resource. The proposed method was implemented using CloudSim simulation tool and the performance result shows that the method will significantly enhance cloud performance in terms of response time and client QoS expectations.

Sequential Individual Rationality in Dynamic Ridesharing

In dynamic ridesharing systems, both operational and economic policies impact Quality-of-Service (QoS). Field experiments have found that firms benefit from proactively compensating users whose QoS expectations are violated. This motivates a broader analytical study of how behavioral perceptions of QoS impact operational and economic policy design in ridesharing systems. We introduce a novel, QoS-centric framework with the following elements: (a) users’ state-dependent utility model that bridges operational (detours) and economic effects (prices or cost shares), (b) dynamic QoS notion, sequential individual rationality, defined on the sequence of (dis)utilities from successive stages of a shared ride, influenced by behavioral drivers (reference/recency effects, loss aversion), and (c) QoS-sensitive economic objectives (profit or fairness) that endogenize users’ choices and QoS constraints. Our framework can be used to extract key operational insights from QoS-sensitive economic objectives in two different environments: (i) commercial ridesharing, which involves pricing exclusive/shared service to maximize profit (considering penalties for QoS-violations), and (ii) community carpooling, which involves designing fair cost sharing schemes. In the commercial setting, we characterize a ride’s optimal shareable region, and show that it may be optimal for a QoS-sensitive service provider to violate QoS and get penalized, no matter how strong the users’ loss aversion. In the carpooling setting, we characterize routes that admit budget-balanced, QoS-compliant cost sharing schemes, resulting in a ride’s QoS-compliant shareable region. We define sequential fairness and characterize fair, QoS-compliant schemes that bring out insightful structural properties, including a strong requirement that commuters must compensate each other for the detour-inconveniences they cause.

HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT

The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811−0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.

GreenWeb: language extensions for energy-efficient mobile web computing

Web computing is gradually shifting toward mobile devices, in which the energy budget is severely constrained. As a result, Web developers must be conscious of energy efficiency. However, current Web languages provide developers little control over energy consumption. In this paper, we take a first step toward language-level research to enable energy-efficient Web computing. Our key motivation is that mobile systems can wisely budget energy usage if informed with user quality-of-service (QoS) constraints. To do this, programmers need new abstractions. We propose two language abstractions, QoS type and QoS target, to capture two fundamental aspects of user QoS experience. We then present GreenWeb, a set of language extensions that empower developers to easily express the QoS abstractions as program annotations. As a proof of concept, we develop a GreenWeb runtime, which intelligently determines how to deliver specified user QoS expectation while minimizing energy consumption. Overall, GreenWeb shows significant energy savings (29.2% ∼ 66.0%) over Android’s default Interactive governor with few QoS violations. Our work demonstrates a promising first step toward language innovations for energy-efficient Web computing.

Quality of Service Concerns in Wireless and Cellular Networks

The increasing popularity of wireless and cellular networks multimedia traffic among the consumers has raised new demands for investigation of their underlying Quality of Service (QoS) and Quality of experience (QoE) requirements. In this work, we carry out an application-based analysis of such requirements. Using this approach, we can properly take into account the heterogeneous nature of the underlying networks and the diversity of their traffic. There are several other reasons that justify the use of this approach. For instance, different QoS expectations and user Quality of Experience (QoE) in developed and developing countries can be mentioned. Many parts of the developing world are highly dependent on wireless and cellular technologies, while the provision of socioeconomic services in industrialized countries is generally based on conventional broadband and advanced cellular systems. Clearly, the variations of the underlying networking technologies perturb the QoS and QoE. So, we also examine the relationship between network QoS and QoE to propose a conceptual mapping between them. To achieve these aims, we first evaluate QoS requirements for network-based applications over different access technologies. To reach tangible outcomes, we then focus on UMTS based 3G cellular networks and WiMAX, and analyze several network-based applications with different path loss models, varying number of active users, and diverse types of traffic. The results exhibit that variant technologies, network congestions, user perceptions, and radio channel conditions affect QoS and QoE parameters to a certain extent.

QoS-aware optimization of sensor network queries

The resource-constrained nature of mote-level wireless sensor networks (WSNs) poses challenges for the design of a general-purpose sensor network query processors (SNQPs). Existing SNQPs tend to generate query execution plans (QEPs) that are selected on the basis of a fixed, implicit expectation, for example, that energy consumption should be kept as small as possible. However, in WSN applications, the same query may be subject to several, possibly conflicting, quality-of-service (QoS) expectations concomitantly (for example maximizing data acquisition rates subject to keeping energy consumption low). It is also not uncommon for the QoS expectations to change over the lifetime of a deployment (for example from low to high data acquisition rates). This paper describes optimization algorithms that respond to stated QoS expectations (about acquisition rate, delivery time, energy consumption and lifetime) when making routing, placement, and timing decisions for in-WSN query processing. The paper shows experimentally that QoS-awareness offers significant benefits in responding to, and reconciling, diverse QoS expectations, thereby enabling QoS-aware SNQPs to generate efficient QEPs for a broader range WSN applications than has hitherto been possible.

QoS aggregation for service orchestrations based on workflow pattern rules and MCDM method: evaluation at design time and runtime

Service consumers satisfaction is considered today as one of the main concern to be ensured by service providers, especially with the spread of concurrency and the increase of functionally equivalent services. This satisfaction is closely related to quality of service (QoS) perceived by service consumers. In this context, we propose an approach to determine the satisfaction degree corresponding to the QoS of service-based applications, with regard to service consumers' QoS expectations. Our approach is based on a preference model, which is built only on the basis of service consumer's provided information. This preference model is also based on the 2-additive Choquet operator that takes into account preferential dependencies. In this paper, we target both design time and runtime aggregation of QoS of service-based applications.

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.

A contribution to software service improvement based on LSP method

With new technologies and various software solutions readily available for utilization in business environment, the main driver for achieving the competitive business advantage is becoming the quality of service. The quality of service expectations is very often managed by established service level agreement (SLA). Behind SLA, there is a real software, infrastructure, organization, culture, and people that maintain it. Therefore, it is very important to take in consideration the complexity of the software solution that is the basis of the given service, probability of an error occurrence, and all costs, and risks that will be associated with operating the service. In this paper, the important elements of the quality of the software that is considered as a service to a business function are analyzed. The proposal for the classification within specific portfolio of software services is given. The logical scoring of preferences (LSP) method is proposed to be utilized for services elementary preferences estimates and overall services comparison within each rank of services. The main objective is in achieving better quality of service within the given rank by recognizing and then emphasizing elements in the services that positively influence overall estimates of the observed services in comparison to the others in the rank. An example of the method utilization on one selected use case is given as well. Key words: Software quality, software services, software development, improvement.