Showing papers on "Service level published in 2017"

Mobile traffic forecasting for maximizing 5G network slicing resource utilization

Abstract: The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk).

Toward cloud computing QoS architecture: analysis of cloud systems and cloud services

Abstract: Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service U+0028 QoS U+0029 by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.

Impact of the use of electric vehicles in collaborative urban transport networks: A case study

Abstract: Transportation is one of the essential services in cities that contribute to the quality of life. As a result, efficient methods for transport planning have become increasingly important. Decision makers have considered collaborative strategies to reduce the overall cost of the supply process and to improve the efficiency and effectiveness of urban logistics systems. This paper assesses the implementation of an electric fleet of vehicles in collaborative urban distribution of goods, in order to reduce environmental impacts while maintaining a level of service. An approach using mathematical modeling with multiple objectives, for tactical and operational decision-making, is proposed to explore the relationship between the delivery cost and the sustainability impact. This approach has been validated using real-data taken from the city of Bogota, Colombia. Similarly, theoretical experiments in other countries have been performed to analyse the impact of the use of electric vehicles in the configuration of the transport network.

Delay-aware VNF placement and chaining based on a flexible resource allocation approach

Abstract: Network Function Virtualization (NFV) is a promising technology that is receiving significant attention in both academia and the industry. NFV paradigm proposes to decouple Network Functions (NFs) from dedicated hardware equipment, offering a better sharing of physical resources and providing more flexibility to network operators. However, in such environment, efficient management mechanisms are crucial to address the problem of Placement and Chaining of Virtual Network Functions (PC-VNF). In this paper, we introduce a PC-VNF model based on a flexible resource allocation approach that takes into account service requirements in terms of latency, in addition to traditional connectivity and resource utilization. This is particularly important for emerging 5G services such as ultrareliable, low latency and massive machine type communications. The end-to-end performance needs to meet the user expectations as well as service requirements to provide the desired QoS/QoE. Our main goal is to determine the optimal VNF placement minimizing resource consumption while providing specific latency (i.e., end-to-end delay) and avoiding violation of Service Level Agreements (SLA) by constraining allocated resources to a given VNF to reach its required performance. Results show that our approach achieves the required latency with better resources utilization compared to the classical approaches, with a reduction of up to 40% of resource consumption and a higher rate of accepted requests by recovering 15 to 60 % of the rejected requests.

Priority Based Dynamic Resource Allocation In Cloud Computing

Abstract: Today Cloud computing is on demand as it offers dynamic flexible resource allocation, for reliable and guaranteed services in pay-as-you-use manner, to Cloud service users So there must be a provision that all resources are made available to requesting users in efficient manner to satisfy their needs This resource provision is done by considering the Service Level Agreements (SLA) and with the help of parallel processing Recent work considers various strategies with single SLA parameter Hence by considering multiple SLA parameter and resource allocation by preemption mechanism for high priority task execution can improve the resource utilization in Cloud In this paper we propose an algorithm which considered Preemptable task execution and multiple SLA parameters such as memory, network bandwidth, and required CPU time An obtained experimental results show that in a situation where resource contention is fierce our algorithm provides better utilization of resources

Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria

Abstract: The Inventory Routing Problem has been mainly studied in recent decades under an economic performance perspective. In this paper, we develop a multi-objective mathematical framework for the IRP to link: (i) the economic performance, (ii) the achieved server level in terms of shortage and delivery delays and (iii) the environmental footprint. The framework developed addresses the uncertainty by considering fuzzy distributions for certain problem inputs, such as the demand and the transportation costs. We show the negative impact on the economic performance when service level targets are exogenously chosen without coordination with the logistics components (inventory and distribution).

Pricing and service decision of dual-channel operations in an O2O closed-loop supply chain

Abstract: Purpose The online direct selling mode has been widely accepted by enterprises in the O2O era. However, the dual-channel (online/offline, forward/backward) operations of the closed-loop supply chain (CLSC) changed the relationship between manufacturers and retailers, thus resulting in channel conflict. The purpose of this paper is to take a dual-channel operations of CLSC as the research target, where a manufacturer sells a single product through a direct e-channel as well as a conventional retail channel; the retailer are responsible for collecting used products in the reverse supply chain and the manufacturer are responsible for remanufacturing. Design/methodology/approach The authors build a benchmark model of dual-channel price and service competition and take the return rate, which is considered to be related to the service level of the retailer, as the function of the service level to extend the model in the reverse SC. The authors then analyze the optimal pricing and service decision under centralization and decentralization, respectively. Finally, with the revenue-sharing factor, wholesale price and recycling price transfer payment coefficient as contract parameters, the paper also designs a revenue-sharing contract led by the manufacturer and explores in what situation the contract could realize the Pareto optimization of all players. Findings In the baseline model, the results show that optimal price and service level correlate positively in centralization; however, the relation relies on consumers’ price sensitivity in decentralization. In the extension model, the relationship between price and service level also relies on the relative value of increased service cost and remanufacturing saved cost. When the return rate correlates with the service level, a recycling transfer payment can elevate the service level and thus raise the return rate. Through analyzing the parameters in revenue-sharing contract, a point can be reached where lowering the wholesale price and raising the transfer payment coefficient will promote retailers to share revenue. Practical implications Many enterprises establish the dual-channel distribution system both online and offline, which need to understand how to resolve their channel conflict. The conflict is especially strong in CLSC with remanufacturing. The result helps the node enterprises realize the coordination of the dual-channel CLSC. Originality/value It takes into account the fact that there are two complementary relationships, such as online selling and offline delivery; used product recycling and remanufacturing. The authors optimize the strategy of product pricing and service level in order to solve channel conflict and double marginalization in the closed-loop dual-channel distribution network.

Investigating synchromodality from a supply chain perspective

Abstract: Greater use of multimodal transportation can substantially improve the environmental performance of freight transportation. Despite strenuous efforts by public policy-makers to alter the freight modal split, most companies still rely heavily on road transportation, and modal shifts to rail and water have remained modest at best. In this paper we argue that this is partly the result of a failure to take a holistic supply chain view of the modal shift process. Synchromodality provides a framework within which shippers can manage their supply chains more flexibly to increase the potential for shifting mode. On the basis of a literature review, we broaden the conventional focus of multimodal transportation to give it a supply chain dimension, and propose the concept of ‘Synchromodality from a Supply Chain Perspective’ (SSCP). Using a case study we show that when the supply chain impacts are taken into account, it is possible to significantly increase the share of intermodal rail transportation within a corridor, without necessarily increasing total logistics cost or reducing the service level. In this way the environmental impact of freight activities can be significantly reduced.

A Framework for Pre-computated Multi-Constrained Quickest QoS Path Algorithm

Abstract: The era of advance computer networks and advance communication leads the technology to new heights and the algorithms used for the routing needs to be updated side by side parallel with the advancement. This paper presents the new multi-constrained routing algorithm which gives quality of service full-fill the service level agreements with the user. Finding a multi-constrained path is a NP-complete problem, but still the presented algorithm gives the feasible path for the routing in a given polynomial time. Furthermore the performance comparison shows the best results with presented approach and existed approach.

Distribution Based Workload Modelling of Continuous Queries in Clouds

Abstract: Resource usage estimation for managing streaming workload in emerging applications domains such as enterprise computing, smart cities, remote healthcare, and astronomy, has emerged as a challenging research problem. Such resource estimation for processing continuous queries over streaming data is challenging due to: (i) uncertain stream arrival patterns, (ii) need to process different mixes of queries, and (iii) varying resource consumption. Existing techniques approximate resource usage for a query as a single point value which may not be sufficient because it is neither expressive enough nor does it capture the aforementioned nature of streaming workload. In this paper, we present a novel approach of using mixture density networks to estimate the whole spectrum of resource usage as probability density functions. We have evaluated our technique using the linear road benchmark and TPC-H in both private and public clouds. The efficiency and applicability of the proposed approach is demonstrated via two novel applications: i) predictable auto-scaling policy setting which highlights the potential of distribution prediction in consistent definition of cloud elasticity rules; and ii) a distribution based admission controller which is able to efficiently admit or reject incoming queries based on probabilistic service level agreements compliance goals.

Agent-based simulation of a shared, autonomous and electric on-demand mobility solution

Abstract: Recent technological progress in automation and electrification gives room to create sustainable, costumer-orientated and economical transportation solutions. For vehicle-based systems, on-demand mobility services are considered to play an important role. To support decision making in an early stage of development, we propose a mobility on-demand simulation that reflects its system behavior on an operational level. Its core forms a discrete event simulation combined with a multi-agent approach. The main entities are fully-automated electric taxis, a central dispatcher and customers. All agents live in a shared environment consisting of a street network and charging infrastructure. The dispatching center handles matchmaking between customers and vehicles with the help of a Contract Net Protocol. Taxis compete for customer transportation requests broadcasted by the dispatcher. Routing and charging decisions are made individually by each agent. A simulation study evaluates three mobility on-demand services for Munich. The basic scenario analyzes a limited service area, where a shared vehicle fleet is responsible for the entire local traffic demand. The second scenario considers additional commuter trips entering or leaving this zone. The third scenario investigates a citywide operation. Key findings confirm the feasibility of operating a shared autonomous vehicle fleet with both high service levels and vehicle utilization.

Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry

Abstract: In today’s competitive business environment, service providers have a strong objective to satisfy the customers with low cost to ensure a patronage/loyalty. Performance measurement defines the information or feedback on actions to meeting strategic objectives and client satisfaction. Generally, performance evaluation of the service provider is a time consuming complicated process, depends customer satisfaction. Over the past two decades several researchers have proposed methods to measure service and quality performance in order to improve the performance efficiency of the organization, since there is a considerable room exists. Hence, in this paper, we analyse efficient and inefficient levels of service performance using data envelopment analysis (DEA) and balance scorecard (BSC) techniques, to bridge the exist gap. The DEA approach has been used to measure the performance of automobile dealers from different areas to know their service levels and also treats the quality of service by making use of different cross-efficiency data envelopment analysis models to discriminate the units. Then, a BSC approach analyzes which aspects of decision making units are inefficient, grounded on four perspectives like as; customers, financial, internal business process and learning and growth, based on the study carried out on ten automobile dealers from various areas. The results identify that dealers are inefficient in learning about customer’s growth, which help the dealers to transform from inefficient into efficient. In addition, this study also focused on various insights related to performance evaluation and provide some useful recommendations which can be practiced in future.

MRCP-RM: A Technique for Resource Allocation and Scheduling of MapReduce Jobs with Deadlines

Abstract: Resource allocation and scheduling on clouds are required to harness the power of the underlying resource pool such that the service provider can meet the quality of service requirements of users, which are often captured in service level agreements (SLAs). This paper focuses on resource allocation and scheduling on clouds and clusters that process MapReduce jobs with SLAs. The resource allocation and scheduling problem is modelled as an optimization problem using constraint programming, and a novel MapReduce Constraint Programming based Resource Management algorithm (MRCP-RM) is devised that can effectively process an open stream of MapReduce jobs where each job is characterized by an SLA comprising an earliest start time, a required execution time, and an end-to-end deadline. A detailed performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals using both simulation and experimentation on a real system. The experiments on a real system are performed on a Hadoop cluster (deployed on Amazon EC2) that runs our new Hadoop Constraint Programming based Resource Management algorithm (HCP-RM) that incorporates a technique for handling data locality. The results of the performance evaluation demonstrate the effectiveness of MRCP-RM/HCP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines ( P ) and also provide insights into system behaviour and performance. In the simulation experiments, it is observed that MRCP-RM achieves on average an 82 percent lower P compared to a technique from the existing literature when processing a synthetic workload from Facebook. Furthermore, in the experiments performed on a Hadoop cluster deployed on Amazon EC2, it is observed that HCP-RM achieved on average a 63 percent lower P compared to an EDF-Scheduler for a wide variety of workload and system parameters experimented with.