Rahul Ghosh

End-to-End Performability Analysis for Infrastructure-as-a-Service Cloud: An Interacting Stochastic Models Approach

Handling diverse client demands and managing unexpected failures without degrading performance are two key promises of a cloud delivered service. However, evaluation of a cloud service quality becomes difficult as the scale and complexity of a cloud system increases. In a cloud environment, service request from a user goes through a variety of provider specific processing steps from the instant it is submitted until the service is fully delivered. Measurement-based evaluation of cloud service quality is expensive especially if many configurations, workload scenarios, and management methods are to be analyzed. To overcome these difficulties, in this paper we propose a general analytic model based approach for an end-to-end perform ability analysis of a cloud service. We illustrate our approach using Infrastructure-as-a-Service (IaaS) cloud, where service availability and provisioning response delays are two key QoS metrics. A novelty of our approach is in reducing the complexity of analysis by dividing the overall model into sub-models and then obtaining the overall solution by iteration over individual sub-model solutions. In contrast to a single one-level monolithic model, our approach yields a high fidelity model that is tractable and scalable. Our approach and underlying models can be readily extended to other types of cloud services and are applicable to public, private and hybrid clouds.

A scalable availability model for Infrastructure-as-a-Service cloud

High availability is one of the key characteristics of Infrastructure-as-a-Service (IaaS) cloud. In this paper, we show a scalable method for availability analysis of large scale IaaS cloud using analytic models. To reduce the complexity of analysis and the solution time, we use an interacting Markov chain based approach. The construction and the solution of the Markov chains is facilitated by the use of a high-level Petri net based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. We compare the solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant. Additionally, we provide closed form solutions of the sub-models and show that our approach can handle very large size IaaS clouds.

Modeling and performance analysis of large scale IaaS Clouds

For Cloud based services to support enterprise class production workloads, Mainframe like predictable performance is essential. However, the scale, complexity, and inherent resource sharing across workloads make the Cloud management for predictable performance difficult. As a first step towards designing Cloud based systems that achieve such performance and realize the service level objectives, we develop a scalable stochastic analytic model for performance quantification of Infrastructure-as-a-Service (IaaS) Cloud. Specifically, we model a class of IaaS Clouds that offer tiered services by configuring physical machines into three pools with different provisioning delay and power consumption characteristics. Performance behaviors in such IaaS Clouds are affected by a large set of parameters, e.g., workload, system characteristics and management policies. Thus, traditional analytic models for such systems tend to be intractable. To overcome this difficulty, we propose a multi-level interacting stochastic sub-models approach where the overall model solution is obtained iteratively over individual sub-model solutions. By comparing with a single-level monolithic model, we show that our approach is scalable, tractable, and yet retains high fidelity. Since the dependencies among the sub-models are resolved via fixed-point iteration, we prove the existence of a solution. Results from our analysis show the impact of workload and system characteristics on two performance measures: mean response delay and job rejection probability.

Scalable Analytics for IaaS Cloud Availability

In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approach can handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared.

Quantifying Resiliency of IaaS Cloud

Cloud based services may experience changes – internal, external, large, small – at any time. Predicting and quantifying the effects on the quality-of-service during and after a change are important in the resiliency assessment of a cloud based service. In this paper, we quantify the resiliency of infrastructure-as-a-service (IaaS) cloud when subject to changes in demand and available capacity. Using a stochastic reward net based model for provisioning and servicing requests in a IaaS cloud, we quantify the resiliency of IaaS cloud w.r.t. two key performance measures – job rejection rate and provisioning response delay.We demonstrate the application of optical combs to implement tunable programmable microwave photonic filters. We design well-known multi-tap microwave photonic filters; however, the utilization of an optical comb with a dispersive medium enables scaling of these filters to a large number of taps. We use optical line-by-line pulse shaping to program tap weights, which allows us to shape the filters bandpass. Our scheme is simple and easily implementable, which provides filters with arbitrary tap weights. As an example, we implement filters with Gaussian apodized tap weights, which achieve more than 35-dB sidelobe suppression. Our experiments provide usable bandwidth, free of sampling spurs, over a Nyquist zone of 5 GHz, equal to half of our 10-GHz comb repetition frequency. Furthermore, we introduce a simple new technique, based on a programmable optical delay line, to uniformly tune the passband center frequency across the free spectral range (FSR) of the filter, ideally without changing the bandpass shape. We demonstrate this scheme by tuning the filter over a full FSR, equal to 10.4 GHz in our experiments.

Link layer multicasting with smart antennas: No client left behind

Wireless link layer multicast is an important service primitive for emerging applications, such as live video, streaming audio, and other content telecasts. The broadcast nature of the wireless channel is amenable to multicast because a single packet transmission may be received by all clients in the multicast group. However, in view of diverse channel conditions at different clients, the rate of such a transmission is bottlenecked by the rate of the weakest client. Multicast throughput degrades severely. Attempts to increase the data rate result in lower reliability and higher unfairness. This paper utilizes smart beamforming antennas to improve multicast performance in wireless LANs. The main idea is to satisfy the stronger clients with a high-rate omnidirectional transmission, followed by high-rate directional transmission(s) to cover the weaker ones. By selecting an optimal transmission strategy (using dynamic programming), we show that the multicast throughput can be maximized while achieving a desired delivery ratio at all the clients. We use testbed measurements to verify our main assumptions. We simulate our protocol in Qualnet, and observe consistent performance improvements over a range of client topologies and time-varying channel conditions.

Resilience in computer systems and networks

The term resilience is used differently by different communities. In general engineering systems, fast recovery from a degraded system state is often termed as resilience. Computer networking community defines it as the combination of trustworthiness (dependability, security, performability) and tolerance (survivability, disruption tolerance, and traffic tolerance). Dependable computing community defined resilience as the persistence of service delivery that can justifiably be trusted, when facing changes. In this paper, resilience definitions of systems and networks will be presented. Metrics for resilience will be compared with dependability metrics such as availability, performance, performability. Simple examples will be used to show quantification of resilience via probabilistic analytic models.

Stochastic Model Driven Capacity Planning for an Infrastructure-as-a-Service Cloud

From an enterprise perspective, one key motivation to transform the traditional IT management into Cloud is the cost reduction of the hosted services. In an Infrastructure-as-a-Service (IaaS) Cloud, virtual machine (VM) instances share the physical machines (PMs) in the providers data center. With large number of PMs, providers can maintain low cost of service downtime at the expense of higher infrastructure and other operational costs (e.g., power consumption and cooling costs). Hence, determining the optimal PM capacity requirements that minimize the overall cost is of interest. In this paper, we show how a cost analysis and optimization framework can be developed using stochastic availability and performance models of an IaaS Cloud. Specifically, we study two cost minimization problems to address the capacity planning in an IaaS Cloud: (1) what is the optimal number of PMs that minimizes the total cost of ownership for a given downtime requirement set by service level agreements? and, (2) is it more economical to use cheaper but less reliable PMs or to use costlier but more reliable PMs for insuring the same availability characteristics? We use simulated annealing, a well-known stochastic search algorithm, to solve these optimization problems. Results from our analysis show that the optimal solutions are found within reasonable time.

Power-performance trade-offs in IaaS cloud: A scalable analytic approach

Optimizing for performance is often associated with higher costs in terms of capacity, faster infrastructure, and power costs. In this paper, we quantify the power-performance trade-offs by developing a scalable analytic model for joint analysis of performance and power consumption for a class of Infrastructure-as-a-Service (IaaS) clouds with tiered service offerings. The tiered service offerings are provided by configuring physical machines into three pools with different response time and power consumption characteristics. Using interacting stochastic sub-models approach, we quantify power-performance trade-offs. We summarize our modeling approach and highlight key results on the effects of physical machine pool configurations on consumed power and achievable performance in terms of response time and ability to service requests. The approach developed here can be used to manage power consumption and performance by judiciously configuring physical machine pools.

System resiliency quantification using non-state-space and state-space analytic models ☆

Abstract Resiliency is becoming an important service attribute for large scale distributed systems and networks. Key problems in resiliency quantification are lack of consensus on the definition of resiliency and systematic approach to quantify system resiliency. In general, resiliency is defined as the ability of (system/person/organization) to recover/defy/resist from any shock, insult, or disturbance [1] . Many researchers interpret resiliency as a synonym for fault-tolerance and reliability/availability. However, effect of failure/repair on systems is already covered by reliability/availability measures and that of on individual jobs is well covered under the umbrella of performability [2] and task completion time analysis [3] . We use Laprie [4] and Simoncini [5] s definition in which resiliency is the persistence of service delivery that can justifiably be trusted, when facing changes. The changes we are referring to here are beyond the envelope of system configurations already considered during system design, that is, beyond fault tolerance. In this paper, we outline a general approach for system resiliency quantification. Using examples of non-state-space and state-space stochastic models, we analytically–numerically quantify the resiliency of system performance, reliability, availability and performability measures w.r.t. structural and parametric changes.