Calin Curescu

A Bidding Algorithm for Optimized Utility-Based Resource Allocation in Ad Hoc Networks

This article proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality of service (QoS) levels for each end-to-end flow are expressed using resource-utility functions, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as bandwidth resources defined by maximal cliques of mutual interfering links. We propose an entirely novel resource allocation algorithm that employs auction mechanisms where flows are bidding for resources. The bids depend both on the flows utility function and the intrinsically derived shadow prices. Then we combine it with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. We also show that the problem can be formulated as a linear programming problem. Thus we can compare the performance of our scheme to the centralized optimal LP solution, registering results very close to the optimum. We isolate the performance of the price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that is more feasible for ad hoc environments. Experimental results of a comparison with the state-of-the-art approach based on Kellys utility maximization framework show that our approach exhibits superior performance for networks with both increased mobility or increased allocation period.

High availability in clouds: systematic review and research challenges

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.

Handling Performance Sensitive Native Cloud Applications with Distributed Cloud Computing and SLA Management

Cloud computing has been used as a platform to reduce cost and increase deployment flexibility for traditional enterprise three-tier web, and some video streaming applications. Typically these types of applications have fairly simple and self-understood performance requirements. Fine-grained constraints on the computation, storage, and networking resources are required support mission-critical enterprise use-cases at a reasonable cost. They are spelled out by service level agreements (SLAs) between the application and the cloud platform. Moreover, new distributed cloud platforms allow for additional deployment patterns, supporting more performance sensitive applications. For example, a specific gaming component will benefit being deployed in the proximity of the (mobile) end-user due to low-latency requirements. In this paper, we motivate the need for more complex performance requirement support with two use cases, electric utility metering and control and public safety. We describe an application management tool, called the Abstract Service Manager (ASM), which is designed to allow the expression of performance requirements in the automated deployment of distributed cloud-native applications. Together with a distributed cloud orchestration system, the ASM automatically mitigates the complexity of constructing performance sensitive applications and their deployment on a distributed cloud.

Risk-based model for availability estimation of SAF redundancy models

Cloud outages, no matter how long, are responsible for large financial losses. Cloud providers look for solutions that provide high availability even in failure cases. Redundancy has been often used as an alternative; however it has a cost, and depending on the redundancy model, we can have different levels of availability. Beyond that, adding more resources also means adding more complexity and more point of failures. In this paper, we propose a risk-based model for estimating the availability of a redundancy setup. In order to analyze our model, we conduct Monte Carlo simulations and results show that for all models, the number of applications we deploy in a Cloud environment impacts on its availability.

Prototyping a high availability PaaS: Performance analysis and lessons learned

With cloud computing consolidation, Platform-as-a-Service (PaaS) has been used as a solution for developing applications with low cost and maximum flexibility. However, an open challenge related to PaaS is the proper handling of multi-tier and stateful applications with support for high availability (HA); and scalability can be considered an essential feature for HA. However, dealing with several instances of the same application that access its state in a common area is not a simple task. This paper presents a novel PaaS framework, named NoPaaS, that supports the deployment of multi-tier and stateful applications assuring their availability according to the Service Availability Forum (SAF) redundancy model. The primary goal of this work is to present NoPaaS framework and prototype, and highlight challenges and open issues when providing multi-tier and stateful applications in high availability clouds.

Failover time evaluation between checkpoint services in multi-tier stateful applications

Cloud applications are offered to users with high availability and minimal data loss. Any (hardware or software) failure must be detected and recovered quickly, in order to maintain customer trust and avoid financial losses. When we are dealing with multi-tier and stateful applications, the failure recovery process is a big challenge because the whole state of the failed application must be retrieved and restored in a new instance. This process is named as failover; it can be performed by a checkpoint service at application-level or at system-level. Depending on the location of the checkpoint data storage, it can be classified as non-collocated, collocated warm, or collocated hot. This work presents an evaluation between these two checkpoint services in both virtualized and physical environments, considering a multi-tier and stateful application.