Luciana Arantes

Towards QoS-Oriented SLA Guarantees for Online Cloud Services

Cloud Computing provides a convenient means of remote on-demand and pay-per-use access to computing resources. However, its ad hoc management of quality-of-service and SLA poses significant challenges to the performance, dependability and costs of online cloud services. The paper precisely addresses this issue and makes a threefold contribution. First, it introduces a new cloud model, the SLAaaS (SLA aware Service) model. SLAaaS enables a systematic integration of QoS levels and SLA into the cloud. It is orthogonal to other cloud models such as SaaS or PaaS, and may apply to any of them. Second, the paper introduces CSLA, a novel language to describe QoS-oriented SLA associated with cloud services. Third, the paper presents a control theoretic approach to provide performance, dependability and cost guarantees for online cloud services, with time-varying workloads. The proposed approach is validated through case studies and extensive experiments with online services hosted in clouds such as Amazon EC2. The case studies illustrate SLA guarantees for various services such as a MapReduce service, a cluster-based multi-tier e-commerce service, and a low-level locking service.

MRSG - A MapReduce simulator over SimGrid

MapReduce is a parallel programming model to process large datasets, and it was inspired by the Map and Reduce primitives from functional languages. Its first implementation was designed to run on large clusters of homogeneous machines. Though, in the last years, the model was ported to different types of environments, such as desktop grid and volunteer computing. To obtain a good performance in these environments, however, it is necessary to adapt some framework mechanisms, such as scheduling and data distribution algorithms. In this paper we present the MRSG simulator, which reproduces the MapReduce work-flow on top of the SimGrid simulation toolkit, and provides an API to implement and evaluate these new algorithms and policies for MapReduce. To evaluate the simulator, we compared its behavior against a real Hadoop MapReduce deployment. The results show an important similarity between the simulated and real executions.

SLA guarantees for cloud services

Quality-of-service and SLA guarantees are among the major challenges of cloud-based services. In this paper we first present a new cloud model called SLAaaS - SLA?aware Service. SLAaaS considers QoS levels and SLA as first class citizens of cloud-based services. This model is orthogonal to other SaaS, PaaS, and IaaS cloud models, and may apply to any of them. More specifically we make three contributions: (i) we provide a novel domain specific language that allows to describe QoS-oriented SLA associated with cloud services; (ii) we present a general control-theoretic approach for managing cloud service SLA; (iii) we apply the proposed language and control approach to guarantee SLA in various case studies, ranging from cloud-based MapReduce service, to locking service, and higher-level e-commerce service; these case studies successfully illustrate SLA management with different QoS aspects of cloud services such as performance, dependability, financial energetic costs. We provide a domain specific language that allows to describe SLA in cloud services.We present a general control-theoretic approach for managing cloud service SLA.We apply our approach on MapReduce, locking, and e-commerce services.

Hierarchical token based mutual exclusion algorithms

Mutual exclusion is a basic block of distributed synchronization algorithms. One of the challenges in highly distributed environments (like peer-to-peer or Grid configurations) is to provide scalable synchronizations taking into account the hierarchical network topology. This paper proposes hierarchical mutual exclusion algorithms. These algorithms are extensions of the Naimi-Trehel token algorithm, reducing the cost of latency and the number of message exchanges between far hosts. We propose three main extensions : (1) hierarchical proxy-based approach; (2) aggregation of requests; and (3) token preemption by closer hosts. We compared the performance of these algorithms on an emulated Grid testbed. We study the impact of each of the extensions, showing that the combination of them can greatly improve performance of the original algorithm.

MRA++: Scheduling and data placement on MapReduce for heterogeneous environments

MapReduce has emerged as a popular programming model in the field of data-intensive computing. This is due to its simplistic design, which provides ease of use for programmers, and its framework implementations such as Hadoop, which have been adopted by large business and technology companies. In this paper we make some improvements to the Hadoop MapReduce framework by introducing algorithms that are suitable for heterogeneous environments. The goal is to efficiently perform data-intensive computing in heterogeneous environments. The need for these adaptations derives from the fact that, following the framework design proposed by Google, Hadoop is optimized to run in large homogeneous clusters. Hence we propose MRA++, a new MapReduce framework design that considers the heterogeneity of nodes during data distribution, task scheduling and job control. MRA++establishes a training task to gather information prior to the data distribution. However, we show that the delay introduced in the setup phase is offset by the effectiveness of the mechanisms and algorithms, that achieve performance gains of more than 70% in 10 Mbps networks.

Distributed mutual exclusion algorithms for grid applications: A hierarchical approach

The majority of current distributed mutual exclusion algorithms are not suited for parallel or distributed applications on a Grid as they do not consider the heterogeneity of latency on Grids. We propose two distributed mutual exclusion algorithms, based on Naimi-Trehels token-based algorithm, which take into account latency gaps, especially those between local and remote clusters of machines. Our first algorithm exploits cluster locality by giving higher priority to critical section requests issued from nodes of the same cluster when compared to those from remote nodes. Our second algorithm adds a router layer to the first algorithm, bringing it closer to Grid network topology. Viewing each cluster as a single node, the Naimi-Trehel algorithm is applied to this router layer. Redirection of inter-cluster messages to clusters nodes is then minimized.

Service Level Agreement for Distributed Mutual Exclusion in Cloud Computing

In Cloud Computing, Service Level Agreement (SLA) is a contract that defines a level and a type of QoS between a cloud provider and a client. Since applications in a Cloud share resources, we propose two tree-based distributed mutual exclusion algorithms that support the SLA concept. The first one is a modified version of the priority-based Kanrar-Chaki algorithm [1] while the second one is a novel algorithm, based on Raymond algorithm [2], where a deadline is associated with every request. In both cases, our aim is to improve Critical Section execution rate and to reduce the number of SLA violations, which, for the first algorithm represents the number of priority inversions (i.e. a higher priority request is satisfied after a lower one) and for the second one, the number of requests whose deadline is not respected. Performance evaluation results show that our solutions significantly reduce SLA violations avoiding message overhead.

Eventual Leader Election in Evolving Mobile Networks

Many reliable distributed services rely on an eventual leader election to coordinate actions. The eventual leader detector has been proposed as a way to implement such an abstraction. It ensures that, eventually, each process in the system will be provided by an unique leader, elected among the set of correct processes in spite of crashes and uncertainties. A number of eventual leader election protocols were suggested. Nonetheless, as far as we are aware of, no one of these protocols tolerates a free pattern of node mobility. This paper proposes a new protocol for this scenario of dynamic and mobile unknown networks.

A fault-tolerant token-based mutual exclusion algorithm using a dynamic tree

This article presents a fault tolerant extension for the Naimi-Trehel token-based mutual exclusion algorithm. Contrary to the extension proposed by Naimi-Trehel, our approach minimizes the use of broadcast support by exploiting the distributed queue of token requests kept by the original algorithm. It also provides good fairness since, during failure recovery, it tries to preserve the order in which token requests would have been satisfied had the failure not occurred.

Eventually Strong Failure Detector with Unknown Membership

The distributed computing scenario is rapidly evolving for integrating self-organizing and dynamic wireless networks. Unreliable failure detectors (FDs) are classical mechanisms that provide information about process failures and can help systems to cope with the high dynamics of these networks. A number of failure detection algorithms have been proposed so far. Nonetheless, most of them assume a global knowledge about the membership as well as a fully communication connectivity; additionally, they are time-based, requiring that eventually some bound on the message transmission will permanently hold. These assumptions are no longer appropriate to the new scenario. This paper presents a new FD protocol that implements a new class of detectors, namely ⋄ SM, which adapts the properties of the ⋄ S class to a dynamic network with an unknown membership. It has the interesting feature of being time-free, so that it does not rely on timers to detect failures; moreover, it tolerates the mobility of nodes and message losses.