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.

Consistency-based service level agreements for cloud storage

Abstract: Choosing a cloud storage system and specific operations for reading and writing data requires developers to make decisions that trade off consistency for availability and performance. Applications may be locked into a choice that is not ideal for all clients and changing conditions. Pileus is a replicated key-value store that allows applications to declare their consistency and latency priorities via consistency-based service level agreements (SLAs). It dynamically selects which servers to access in order to deliver the best service given the current configuration and system conditions. In application-specific SLAs, developers can request both strong and eventual consistency as well as intermediate guarantees such as read-my-writes. Evaluations running on a worldwide test bed with geo-replicated data show that the system adapts to varying client-server latencies to provide service that matches or exceeds the best static consistency choice and server selection scheme.

A system, method and article of manufacture for managing an environment of a development architecture framework

Abstract: A system, method and article of manufacture are provided for managing an environment in a development architecture framework. Service of a system is managed based on service level agreements and/or operations level agreements. A plurality of system management operations are performed. The system management operations include start-up and shut-down operations, back-up and restore operations, archiving operations, security operations, and performance monitoring operations. Service is planned in order to anticipate and implement changes in the system.

Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

Abstract: Cloud computing represents a novel on-demand computing approach where resources are provided in compliance to a set of predefined non-functional properties specified and negotiated by means of Service Level Agreements (SLAs). In order to avoid costly SLA violations and to timely react to failures and environmental changes, advanced SLA enactment strategies are necessary, which include appropriate resource-monitoring concepts. Currently, Cloud providers tend to adopt existing monitoring tools, as for example those from Grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics e.g., system up and down time to high-level application specific SLA parameters e.g., system availability. In this paper we present a novel framework for managing the mappings of the Low-level resource Metrics to High-level SLAs (LoM2HiS framework). The LoM2HiS framework is embedded into FoSII infrastructure, which facilitates autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the enactor component to act so as to avert the threats. We discuss the conceptual model of the LoM2HiS framework, followed by the implementation details. Finally, we present the first experimental results and a proof of concept of the LoM2HiS framework.

Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraints

Abstract: We formulate a stochastic-demand version of the single-stage lot-sizing problem with time-varying demand, incorporating a service-level constraint on the probability of a stockout. Three strategies are studied. The "static uncertainty" strategy, in which lot-sizing decisions for every period must be made at the beginning of period 1, is shown to yield an equivalent deterministic problem with time-varying demands for which optimal or good heuristic solutions exist. The procedure by which this equivalent problem is obtained is computationally simple. The "dynamic uncertainty" strategy allows subsequent lot sizes to be chosen on the basis of demands that have become known at a later point in time. The "static-dynamic" uncertainty approach combines features of the above two strategies and yields an equivalent linear program for any given order schedule. Relationships are suggested between these strategies and various aspects of rolling horizon production planning. Arguments are given that in such an environment, the static uncertainty strategy is the most straightforward to modify and "roll along" as new demands become known. Good results are found when this procedure is applied to some 300-period stochastic-demand problems using rolling horizons of between 2 and 12 periods in length.