1,720,971 research outputs found
Practical Aspects for Effective Monitoring of SLAs in Cloud Computing and Virtual Platforms
No full textCloud computing is transforming the software landscape. Software services are increasingly designed in modular and decoupled fashion that communicate over a network and are deployed on the Cloud. Cloud offers three service models namely Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Soft ware as- a-Service (SaaS). Although this allows better management of resources, the Quality of Service (QoS) in dynamically changing environments like Cloud must be legally stipulated as a Service Level Agreement (SLA). This introduces several challenges in the area of SLA enforcement. A key problem is detecting the root cause of performance problems which may lie in hosted service or deployment platforms (PaaS or IaaS), and adjusting resources accordingly. Monitoring and Analytic methods have emerged as promising and inevitable solutions in this context, but require precise real time monitoring data. Towards this goal, we assess practical aspects for effective monitoring of SLA-aware services hosted in Cloud. We present two real-world application scenarios for deriving requirements and present the prototype of our Monitoring and Analytics framework. We claim that this work provides necessary foundations for researching SLA-aware root cause analysis algorithms under realistic setup
Practical Aspects for Effective Monitoring of SLAs in Cloud Computing and Virtual Platforms
No full textCloud computing is transforming the software landscape. Software services are increasingly designed in modular and decoupled fashion that communicate over a network and are deployed on the Cloud. Cloud offers three service models namely Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Soft ware as- a-Service (SaaS). Although this allows better management of resources, the Quality of Service (QoS) in dynamically changing environments like Cloud must be legally stipulated as a Service Level Agreement (SLA). This introduces several challenges in the area of SLA enforcement. A key problem is detecting the root cause of performance problems which may lie in hosted service or deployment platforms (PaaS or IaaS), and adjusting resources accordingly. Monitoring and Analytic methods have emerged as promising and inevitable solutions in this context, but require precise real time monitoring data. Towards this goal, we assess practical aspects for effective monitoring of SLA-aware services hosted in Cloud. We present two real-world application scenarios for deriving requirements and present the prototype of our Monitoring and Analytics framework. We claim that this work provides necessary foundations for researching SLA-aware root cause analysis algorithms under realistic setup
Distributed predictive performance anomaly detection for virtualised platforms
No full textPredicting subsequent values of quality of service (QoS) properties is a key component of autonomic solutions. Predictions help in the management of cloud-based applications by preventing QoS breaches from happening. The huge amount of monitoring data generated by cloud platforms motivated the applicability of scalable data mining and machine learning techniques for predicting performance anomalies. Building prediction models individually for thousands of virtual machines (VMs) requires a robust generic methodology with minimal human intervention. In this work, we focus on these issues and present three main contributions. First, we compare several time series modelling approaches to evidence the predictive capabilities of these approaches. Second, we propose estimation-classification models that augment the predictive capabilities of machine learning classification methods (random forest, decision tree, and support vector machine) by combining them with time series analysis methods (AR, ARIMA and ETS). Third, we show how the data mining techniques in conjunction with Hadoop framework can be a useful, practical, and inexpensive method for predicting QoS attributes
A Protocol Development Framework for SLA Negotiations in Cloud and Service Computing
No full textAs businesses transit towards cloud and service oriented economy, agents are employed to efficiently negotiate service level agreements (SLAs) on services procured automatically to match changes in demand. This ‘pay-as-you-go’ trading model affords flexibility with reliability, but requires customized and seamless interactions enabled by negotiation protocols that best serve the market domain. To this end, we present a domain-independent framework based on a protocol development lifecycle, comprising four distinct phases namely modeling, verification, rule-based implementation and generic execution. We illustrate all phases by introducing the Simple Bilateral Negotiation Protocol (SBNP) - a multi-tier, multi-round and customizable negotiation protocol. We exemplify its adoption among chains of service providers that serve SaaS, PaaS and IaaS offerings. We show that SBNP is well-formed, deterministic and deadlock-free. We evaluate state space scalability for SBNP and verify its correctness using Linear Temporal Logic (LTL). Finally, we show that rule-based implementation allows for generic execution of multiple protocols on our negotiation platform, which provides businesses the agility to sustain competitive advantage
Structural Optimization of Reduced Ordered Binary Decision Diagrams for SLA Negotiation in IaaS of Cloud Computing
No full textIn cloud computing, an automated SLA is an electronic contract used to record the rights and obligations of service providers and customers for their services. SLA negotiation can be a time-consuming process, mainly due to the unpredictable rounds of negotiation and the complicated possible dependencies among SLAs. The operation of negotiating SLAs can be facilitated when SLAs are translated into Reduced Ordered Binary Decision Diagrams (ROBDDs). Nevertheless, an ROBDD may not be optimally structured upon production. In this paper, we show how to reduce the number of 1-paths and nodes of ROBDDs that model SLAs, using ROBDD optimization algorithms. In addition, we demonstrate the reduction of 1-paths via the application of Term Rewriting Systems with mutually exclusive features. Using the latter, ROBDDs can be generated accurately without redundant 1-paths. We apply the principles onto the negotiation of IaaS SLAs via simulation, and show that negotiation is accelerated by assessing fewer SLA proposals (1-paths), while memory consumption is also reduced
Distributed predictive performance anomaly detection for virtualised platforms
No full textPredicting subsequent values of quality of service (QoS) properties is a key component of autonomic solutions. Predictions help in the management of cloud-based applications by preventing QoS breaches from happening. The huge amount of monitoring data generated by cloud platforms motivated the applicability of scalable data mining and machine learning techniques for predicting performance anomalies. Building prediction models individually for thousands of virtual machines (VMs) requires a robust generic methodology with minimal human intervention. In this work, we focus on these issues and present three main contributions. First, we compare several time series modelling approaches to evidence the predictive capabilities of these approaches. Second, we propose estimation-classification models that augment the predictive capabilities of machine learning classification methods (random forest, decision tree, and support vector machine) by combining them with time series analysis methods (AR, ARIMA and ETS). Third, we show how the data mining techniques in conjunction with Hadoop framework can be a useful, practical, and inexpensive method for predicting QoS attributes
QoS-aware SLA-based Advanced Reservation of Infrastructure as a Service
No full textCloud computing effectively implements the vision of utility computing by employing a pay-as-you-go cost model and allowing on-demand (re-)leasing of IT resources. Small or medium-sized Infrastructure-as-a-Service providers, however, find it challenging to satisfy all requests immediately due to their limited resource capacity. In that situation, both providers and customers may benefit greatly from advanced reservation of virtual resources, i.e. virtual machines. In our work, we assume SLA-based resource requests and introduce an advanced reserva- tion methodology during SLA negotiation by using computational geometry. Thereby, we are able to verify, record and manage the infrastructure resources efficiently. Based on that model, service providers can easily verify the available capacity for satisfying the customer’s Quality-of-Service requirements. Furthermore, we introduce flexible alternative counter-offers, when the service provider lacks resources. Therefore, our mechanism increases the utilization of the resources and attempts to satisfy as many customers as possible
Metaheuristics-Based Planning and Optimization for SLA-Aware Resource Management in PaaS Clouds
No full textThe Platform as a Service (PaaS) model of Cloud Computing has emerged as an enabling yet disruptive paradigm for accelerated development of applications on the Cloud. PaaS hides administration complexities of the underlying infrastructure such as the physical or virtual machines. This abstraction is achieved through advanced automation and OS-level multi-tenant containers. However, the on-demand procurement, unpredictable workloads and auto-scaling result in rapid increase and decrease of containers. This causes undesired utilization of Cloud resources and energy wastage that can be avoided with real time planning. Hence, the main challenge of a PaaS Cloud provider is to regularly plan and optimize the placement of containers on Cloud machines. However, the service-driven constraints regarding containers and spatial constraints regarding machines make SLA-aware resource allocation non-trivial. This relatively novel "Service Consolidation" problem is a variant of multi-dimensional bin-packing and hence NP-hard. In this work, we concretely frame this problem by leveraging the definition of Machine Reassignment model proposed by Google for the ROADEF/EURO challenge and characterize it for Open Shift PaaS. We apply Metaheuristic search to discover best (re) allocation solutions on Clouds of varying scales. We compare four state of the art algorithms as problem properties change in datasets and evaluate their performance against a variety of metrics including objective function score, machines used, utilization, resource contention, SLA violations, migrations and energy consumption. Finally, we present a policy-led ranking of solutions to obscure the complexity of individual metrics and decide for the most preferred solution. Hence, we provide valuable insights for SLA-aware resource management in PaaS Clouds
QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios
No full textVirtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements
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