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.

High availability in clouds: systematic review and research challenges

Abstract: Cloud Computing has been used by different types of clients because it has many advantages, including the minimization of infrastructure resources costs, and its elasticity property, which allows services to be scaled up or down according to the current demand. From the Cloud provider point-of-view, there are many challenges to be overcome in order to deliver Cloud services that meet all requirements defined in Service Level Agreements (SLAs). High availability has been one of the biggest challenges for providers, and many services can be used to improve the availability of a service, such as checkpointing, load balancing, and redundancy. Beyond services, we can also find infrastructure and middleware solutions. This systematic review has as its main goal to present and discuss high available (HA) solutions for Cloud Computing, and to introduce some research challenges in this area. We hope this work can be used as a starting point to understanding and coping with HA problems in Cloud.

An energy and deadline aware resource provisioning, scheduling and optimization framework for cloud systems

Abstract: Cloud computing has attracted significant attention due to the increasing demand for low-cost, high performance, and energy-efficient computing. Profit maximization for the cloud service provider (CSP) is a key objective in the large-scale, heterogeneous, and multi-user environment of a cloud system. This paper addresses the problem of minimizing the operation cost of a cloud system by maximizing its energy efficiency while ensuring that user deadlines as defined in Service Level Agreements are met. The workload in the cloud system can be modeled as independent batch requests or as task graphs with dependencies. This paper adopts the latter modeling approach, which provides more opportunities for energy and performance optimizations, thus enabling the CSP to meet user deadlines at lower operation costs. However, these optimizations require additional supporting efforts e.g., resource provisioning, virtual machine placement, and task scheduling, which are addressed in a holistic manner in the proposed framework. In the envisioned cloud environment, users can construct their own services and applications based on the available set of virtual machines, but are relieved from the burden of resource provisioning and task scheduling. The CSP will then exploit data parallelism in user workloads to create an energy and deadline-aware cloud platform.

Linear-Quadratic Optimal Control of Periodic-Review Perishable Inventory Systems

Abstract: In this brief, the problem of inventory control in systems with perishable goods is addressed from the control-theoretic perspective. In the analyzed setting, the deteriorating stock used to fulfill unknown, time-varying demand is replenished with delay from a remote supply source. In order to eliminate the threat of the bullwhip effect (amplified demand variations translated to the ordering signal), we propose to use the benefits of linear-quadratic optimal control. In contrast to the earlier approaches to inventory management of perishable goods, mainly based on heuristics and static optimization, we apply formal methodology of discrete-time dynamical optimization, and solve the optimal control problem analytically. This allows us to formulate and strictly prove a number of advantageous properties of the designed controller, e.g., we demonstrate that it ensures full demand satisfaction in the system with arbitrary delay and any bounded demand pattern with unknown statistics. The proposed controller outperforms the classical order-up-to policy in terms of higher service level, smaller holding costs, and smaller order-to-demand variance ratio.

E2EProf: Automated End-to-End Performance Management for Enterprise Systems

Abstract: Distributed systems are becoming increasingly complex, caused by the prevalent use of Web services, multi-tier architectures, and grid computing, where dynamic sets of components interact with each other across distributed and heterogeneous computing infrastructures. For these applications to be able to predictably and efficiently deliver services to end users, it is therefore, critical to understand and control their runtime behavior. In a datacenter environment, for instance, understanding the end-to-end dynamic behavior of certain IT subsystems, from the time requests are made to when responses are generated and finally, received, is a key prerequisite for improving application response, to provide required levels of performance, or to meet service level agreements (SLAs). The E2EProf toolkit enables the efficient and nonintrusive capture and analysis of end-to-end program behavior for complex enterprise applications. E2EProf permits an enterprise to recognize and analyze performance problems when they occur - online, to take corrective actions as soon as possible and wherever necessary along the paths currently taken by user requests - end-to-end, and to do so without the need to instrument applications - nonintrusively. Online analysis exploits a novel signal analysis algorithm, termed pathmap, which dynamically detects the causal paths taken by client requests through application and backend servers and annotates these paths with end-to-end latencies and with the contributions to these latencies from different path components. Thus, with pathmap, it is possible to dynamically identify the bottlenecks present in selected servers or services and to detect the abnormal or unusual performance behaviors indicative of potential problems or overloads. Pathmap and the E2EProf toolkit successfully detect causal request paths and associated performance bottlenecks in the RUBiS ebay-like multi-tier Web application and in one of the datacenter of our industry partner, Delta Air Lines.