Service level

About: Service level is a research topic. Over the lifetime, 7647 publications have been published within this topic receiving 126093 citations. The topic is also known as: service level.

A Tutorial on Machine Learning for Failure Management in Optical Networks

Abstract: Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers’ service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

A stochastic service network model with application to hospital facilities.

Abstract: This paper presents a methodology for estimating expected utilization and service level for a class of capacity constrained service network facilities operating in a stochastic environment. A semi-Markov process describes the flows of customers (patients) through a network of service units. We model the case where one of the units has finite capacity and no queues are allowed to form. We show that the expected level of utilization and service can be computed from a simple linear relationship based on (a) the equilibrium arrival rates at each unit which are associated with the case of infinite capacity, (b) mean holding times for each unit, and (c) the probability that the finite capacity unit is at full capacity. We use Erlang's loss formula to calculate the probability of full capacity, show this calculation to be exact for two cases, and recommend its use as an approximation in the general case. We test the accuracy of the approximation on a set of published data. In the discussion, we present a techniq...

Resource Calculations with Constraints, and Placement of Tenants and Instances for Multi-tenant SaaS Applications

Abstract: Cost of customization, deployment and operation of a software application supporting multiple tenants can be lowered through multi-tenancy in a new application business model called Software as a Service (SaaS) However, there are a number of technical challenges that need to be tackled before these benefits can be realized These challenges include calculations of resource requirements for multi-tenants with applied constraints in a shared application instance, the optimal placement of tenants and instances with maximum cost savings but without violating any requirements of service level agreements for all tenants in a set of servers Moreover, previously reported capacity planning and resource allocation methods and tools are not tenant aware This paper will address and provide novel solutions to these challenges We also describe the first of a kind, a multi-tenant placement tool for application deployment in a distributed computing environment

System and method for supporting transaction and parallel services in a clustered system based on a service level agreement

Abstract: A server allocation controller provides an improved distributed data processing system for facilitating dynamic allocation of computing resources. The server allocation controller supports transaction and parallel services across multiple data centers enabling dynamic allocation of computing resources based on the current workload and service level agreements. The server allocation controller provides a method for dynamic re-partitioning of the workload to handle workload surges. Computing resources are dynamically assigned among transaction and parallel application classes, based on the current and predicted workload. Based on a service level agreement, the server allocation controller monitors and predicts the load on the system. If the current or predicted load cannot be handled with the current system configuration the server allocation controller determines additional resources needed to handle the current or predicted workload. The server cluster is reconfigured to meet the service level agreement.

Integrated inventory and transportation mode selection: A service parts logistics system

Abstract: We present an optimization-based model to gain insights into the integrated inventory and transportation problem for a single-echelon, multi-facility service parts logistics system with time-based service level constraints. As an optimization goal we minimize the relevant inventory and transportation costs while ensuring that service constraints are met. The model builds on stochastic base-stock inventory model and integrates it with transportation options and service responsiveness that can be achieved using alternate modes (namely slow, medium and fast). The results obtained through different networks show that significant benefits can be obtained from transportation mode and inventory integration.