Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE

Performance And Reliability Analysis Of Computer Systems (an Example-based Approach Using The Sharpe Software. Published in: IEEE Transactions on.
Table of contents

In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop.

A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

Analysis of software rejuvenation using Markov regenerative stochastic Petri net. Energy control in dependable wireless sensor networks: Journal of Risk and Reliability , He is the scientific leader of the PON01 SIGMA project, that intends to develop cloud solutions to manage environmental multi-risk critical situations. He is the scientific director of Inquadro s. This novel scenario is building a new life paradigm positively tuning various aspects of daily activities in relation to not only individuals but also companies, governments, etc.

However, though expected and appreciated, this new scenario is posing relevant challenges on the research community that must be suitably addressed to guarantee a successful and profitable worldwide deployment. From a wide perspective the problem to be solved is mainly oriented to find out how available information data access and connectivity user access may collaborate the best. In fact, the new scenario depicted above can be also formulated as the interaction between IoT Internet of Things and data worlds, being aware that IoTmay serve as either a data producer or consumer prosumer , and that this data is not only large but also unstructured and varied.

Indeed, the interaction among both worlds is the reason not only permitting, but alsofuellingand promoting,an emerging collaborative scenario, where data along with the technologies to obtain and smartly handle this data,profit fromboth new tools aiming at increasing the most the benefits citizens may obtain from this scenario and novel automatic, autonomous, faster and betterdecision-making processes. Xavi Masip got MSc and Ph. His research includes participation in several national and European projects as well as contracts with the private sector. His research focuses on the areas of broadband communications, cloud networking, SDN, programmable networks, QoS management and provision, traffic engineering and multilayer networks, smart cities, smart vehicles, social innovation, IoT and open, adaptable user-customizable network architectures.

Calls Call for Papers. We also bring two examples with Exponential and Weibull distribution of failures. As it is shown in Tables 7 , 8 and 9 , the interval enclosures for mean time to failure bound corrresponding real values mean time to failure. The results brought in Tables 8 and 9 , for example, the n value employed was to guarantee precision in the interval enclosures.

Antonio Puliafito

So, the conditional probability presented in 2. Based on the Equation 2. The interval enclosure for the real-valued hazard rate function in an instant of time t is given by: The Table 10 illustrates the signatures of interval functions that encapsulate the real-valued hazard rate function for Exponential, Weibull and Normal distributions. The Table 11 illustrates the variation of the hazard rate interval widths and related execution times, since p increases. Similarly, we bring another two examples with Normal and Weibull distribution of failures. As the computation process of hazard rate function is similar to reliability function, the execution time of interval enclosure for hazard rate function has the same order of magnitude as the one of reliability enclosure.

MIT Commencement Program 2005 - Address: Irwin M. Jacobs (Qualcomm)

This could be noted comparing the execution time in examples brought by Section 2. Complex systems 5 are compound by a set of components that act together to perform a specified function that could not be done with the absence of one of its components. We suppose that a component can present two distinct and not simultaneous states: In the last one, we consider that a component has at least one fault that does not allow its correct operation. Thus, the state of each component can be represented by the discrete random variable that is assigned to two different values. Let x i be the state of the i-esimo component of a specified system.

Consider the complex system L with n components.

Antonio Puliafito

As well as its components, L also holds one of the two mentioned states. The following configurations are addressed in this section: The Figure 1 a indicates that one complex system with components in series 5 , 13 exhibits only one critical path. Let L be the complex system compound by n components in series. So, considering that L has two components, if only one component fails L also will fail.

Thus, assuming that the components' failure process of L are independent, the reliability function of L is given by. The Figure 1 b outlines the parallel configuration 5 , 13 , also called redundant. Systems with this configuration fail whether all of its components are in an unavailable state in a specified instant of time. Let L be a system formed by n components connected in parallel. Therefore, there are n critical paths in the redundant configuration. Thus, the probability that L does not fail in the period of time [0, t ] is given by.

This section is devoted to validate the proposed reliability enclosures for complex systems. In this way, the SHARPE software 9 , 23 was employed in such manner that the real values of reliability function were generated and compared with corresponding interval enclosures.

The Matlab software enables two formats to display the computed intervals. The first one is format short which results fixed-decimal format with 4 decimal digits after decimal point numbers The last option, format long, yields fixed-decimal format with 15 decimal digits after the decimal point numbers. Therefore, to confirm whether the obtained intervals bound the values computed by SHARPE, we use interval computation with format short configuration.

In order to perform the validation of intervals with format long configuration, we use values computed by the previously specified computational platform, which results binary64 numbers displayed, in this paper, with 16 decimal digits after the decimal point. The computation of the real-valued reliability function was based on systems that have components with series and parallel configurations simultaneously. According to 5 , the reliability function computation of these systems involve their decomposition into subsystems.

Citations en double

Thus, the reliability function's evaluation of each subsystems are done and, then, the obtained values are combined in such way that the reliability function of all the system is calculated. Due to this computation process, the reliability metrics calculated related to complex systems is prone to errors caused by the propagation of round-off and truncation errors.


  1. Performance and reliability analysis of computer systems.
  2. IP-адрес данного ресурса заблокирован в соответствии с действующим законодательством..
  3. Reliability and Performability - Selection of References, by Dr. Milos Manic;
  4. VIXEN (VIXEN Series Book 1).

Let W be the system outlined in Figure 2 compound by three subsystems: W 1 formed uniquely by the component block0 , W 2 formed by the components block1, block3, block4 and block5 connected in parallel and W 3 formed by the components block2, block6, block7, block8 and block9 also connected in parallel.

We can observe that the interval enclosure with format single configuration contains the value of We also note that the computed interval with double precision encapsulates the real value obtained by the specified computational platform. This might forbidden that comparisons among two systems with different reliability function values close to the real number 1 could be done, since the computed values are rounded to the same punctual value.

Let M be the system outlined in Figure 3 compound by five subsystems: M 1 formed uniquely by the component block0 , M 2 formed by the components block1, block4 and block5 connected in parallel , M 3 formed by the components block9, block6, block7 and block8 also connected in parallel , M 4 formed by the components block3 and block11 connected in parallel and M 5 formed by the components block10 and block12 connected in series. In this study of case Case 2 , we also observe that the computed intervals with single and long configuration enclose the real values obtained by the SHARPE software and the specified computational platform, respectively.

In this case, for instance, if we use the reliability value computed by the SHARPE, any multiplication using this numeric value will also result in zero. On other hand, the use of the calculated intervals avoids this type of numeric problem. Let G be the system outlined in Figure 4 compound by three subsystems: G 1 formed uniquely by the component block0 , G 2 formed by the components block3 and block5 connected in parallel and G 3 formed by the parallel composition of the subsystems block1 and block6 in series, block2 and block7 in series and block4 and block8 also in series.

As we could observe in Cases 1 and 2, in the Case of study 3 formed by components with Weibull distribution of failures , we also validate that the computed intervals for Weibull distribution enclose the real values obtained by the SHARPE software and the specified computational platform. Let V be the system outlined in Figure 5 compound by three subsystems: V 1 formed uniquely by the component block0 , V 2 formed by the components block3 and block5 connected in parallel and V 31 formed by the components block6, block1, block2, block4, block7 in parallel.

Therefore, the Case of study 4 only presents comparison between the real value computed by the specified computational platform and the reliability enclosure with long configuration. As could be observed in the other cases, the interval enclosure obtained for the complex system bounds the related punctual value.

This paper presented a wide set of interval enclosures definitions for reliability metrics, that controls round-off and truncation errors produced by the computation of the corresponding real values. The intervals are obtained using high accuracy in the floating-point system employed. In order to summary this article, the Tables 18 and 19 outline the main original contributions of this paper.

Table 18 shows interval enclosures definitions for reliability metrics. Table 19 presents implementations' signatures for interval enclosures, considering Exponential, Weibull and Normal distributions. In Table 18 , the interval proposed for reliability function is defined in The interval definitions proposed for mean time to failure and hazard rare function are original contributions of this work.