Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

A Comparison of Autonomic Decision Making Techniques

Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks

Feedback Autonomic Provisioning for Guaranteeing Performance in MapReduce Systems

International audienceCompanies have a fast growing amounts of data to process and store, a data explosion is happening next to us. Currentlyone of the most common approaches to treat these vast data quantities are based on the MapReduce parallel programming paradigm.While its use is widespread in the industry, ensuring performance constraints, while at the same time minimizing costs, still providesconsiderable challenges. We propose a coarse grained control theoretical approach, based on techniques that have already provedtheir usefulness in the control community. We introduce the first algorithm to create dynamic models for Big Data MapReduce systems,running a concurrent workload. Furthermore we identify two important control use cases: relaxed performance - minimal resourceand strict performance. For the first case we develop two feedback control mechanism. A classical feedback controller and an evenbasedfeedback, that minimises the number of cluster reconfigurations as well. Moreover, to address strict performance requirements afeedforward predictive controller that efficiently suppresses the effects of large workload size variations is developed. All the controllersare validated online in a benchmark running in a real 60 node MapReduce cluster, using a data intensive Business Intelligenceworkload. Our experiments demonstrate the success of the control strategies employed in assuring service time constraints

On autonomic platform-as-a-service: characterisation and conceptual model

In this position paper, we envision a Platform-as-a-Service conceptual and architectural solution for large-scale and data intensive applications. Our architectural approach is based on autonomic principles, therefore, its ultimate goal is to reduce human intervention, the cost, and the perceived complexity by enabling the autonomic platform to manage such applications itself in accordance with highlevel policies. Such policies allow the platform to (i) interpret the application specifications; (ii) to map the specifications onto the target computing infrastructure, so that the applications are executed and their Quality of Service (QoS), as specified in their SLA, enforced; and, most importantly, (iii) to adapt automatically such previously established mappings when unexpected behaviours violate the expected. Such adaptations may involve modifications in the arrangement of the computational infrastructure, i.e. by re-designing a different communication network topology that dictates how computational resources interact, or even the live-migration to a different computational infrastructure. The ultimate goal of these challenges is to (de)provision computational machines, storage and networking links and their required topologies in order to supply for the application the virtualised infrastructure that better meets the SLAs. Generic architectural blueprints and principles have been provided for designing and implementing an autonomic computing system.We revisit them in order to provide a customised and specific viewfor PaaS platforms and integrate emerging paradigms such as DevOps for automate deployments, Monitoring as a Service for accurate and large-scale monitoring, or well-known formalisms such as Petri Nets for building performance models

Towards auto-scaling in the cloud: online resource allocation techniques

Cloud computing provides an easy access to computing resources. Customers can acquire and release resources any time. However, it is not trivial to determine when and how many resources to allocate. Many applications running in the cloud face workload changes that affect their resource demand. The first thought is to plan capacity either for the average load or for the peak load. In the first case there is less cost incurred, but performance will be affected if the peak load occurs. The second case leads to money wastage, since resources will remain underutilized most of the time. Therefore there is a need for a more sophisticated resource provisioning techniques that can automatically scale the application resources according to workload demand and performance constrains. Large cloud providers such as Amazon, Microsoft, RightScale provide auto-scaling services. However, without the proper configuration and testing such services can do more harm than good. In this work I investigate application specific online resource allocation techniques that allow to dynamically adapt to incoming workload, minimize the cost of virtual resources and meet user-specified performance objectives

SEEC: A Framework for Self-aware Computing

As the complexity of computing systems increases, application programmers must be experts in their application domain and have the systems knowledge required to address the problems that arise from parallelism, power, energy, and reliability concerns. One approach to relieving this burden is to make use of self-aware computing systems, which automatically adjust their behavior to help applications achieve their goals. This paper presents the SEEC framework, a unified computational model designed to enable self-aware computing in both applications and system software. In the SEEC model, applications specify goals, system software specifies possible actions, and the SEEC framework is responsible for deciding how to use the available actions to meet the application-specified goals. The SEEC framework is built around a general and extensible control system which provides predictable behavior and allows SEEC to make decisions that achieve goals while optimizing resource utilization. To demonstrate the applicability of the SEEC framework, this paper presents fivedifferent self-aware systems built using SEEC. Case studies demonstrate how these systems can control the performance of the PARSEC benchmarks, optimize performance per Watt for a video encoder, and respond to unexpected changes in the underlying environment. In general these studies demonstrate that systems built using the SEEC framework are goal-oriented, predictable, adaptive, and extensible