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Resource Management in Cloud
Published in Sunilkumar Manvi, Gopal K. Shyam, Cloud Computing, 2021
Sunilkumar Manvi, Gopal K. Shyam
In Cloud computing, Resource Allocation (RA) is the process of assigning available resources to the needed Cloud applications over the Internet. Resource allocation starves services if the allocation is not managed precisely. Resource provisioning solves that problem by allowing the service providers to manage the resources for each individual module. Resource Allocation Strategy (RAS) is all about integrating Cloud provider activities for utilizing and allocating scarce resources within the limit of Cloud environment so as to meet the needs of the Cloud application. It requires the type and amount of resources needed by each application in order to complete a user job. The order and time of allocation of resources are also an input for an optimal RAS. An optimal RAS should avoid the following criteria as follows: Resource contention situation arises when two applications try to access the same resource at the same time.Scarcity of resources arises when there are limited resources.Resource fragmentation situation arises when the resources are isolated. There will be enough resources but not able to allocate to the needed application.Over-provisioning of resources arises when the application gets surplus resources than the demanded oneUnder-provisioning of resources occurs when the application is assigned with fewer numbers of resources than the demand.
Real-Time Operating Systems
Published in Leanna Rierson, Developing Safety-Critical Software, 2017
As the name implies, resource contention is a conflict over a shared resource, such as processor or memory. Three specific contentions that need to be dealt with in an RTOS are deadlock, starvation, and lockout. Each is described in the following:
An inventory data-driven model for predictive-reactive production scheduling
Published in International Journal of Production Research, 2023
Satie L. Takeda-Berger, Enzo M. Frazzon
The generation of the schedule can occur in two ways. The first is by calculating the complete production schedule, and the second is by using priority rules to continuously calculate the priorities of all jobs in the queue waiting to be processed (Frazzon, Kück, and Freitag 2018). Calculating the complete schedule can be very time-consuming, as it would require incorporating many parameters. Moreover, schedules may become obsolete due to stochastic effects, such as changes in processing time and due dates (Kück et al. 2017). Nevertheless, considering industrial practice, scheduling only needs to capture a global picture of resource contention and give relative priorities to jobs (Aytug et al. 2005). Thus, priority rules present a feasible solution, being easy to implement in practice, efficient execution time, and allowing for quasi-optimal solutions for special cases (Valledor et al. 2018; Zhang, Jiang, and Guo 2009). Moreover, priority rules are considered a primary approach for reactive scheduling (Bożek and Wysocki 2016).
A Machine Learning Approach for Quantifying the Design Error Propagation in Safety Critical Software System
Published in IETE Journal of Research, 2022
The view on controlling software mechanism in any of the embedded real-time system may differ from the hardware control engineer and software system engineer. Example consistent sampling rate, minimal or no resource contention is viewed as multiple tasks, scheduling issues and resource utilization by a software system engineer [17]. In our research, we have considered a simulation model of anti-lock braking system, an automotive structure as case study [22]. The execution of this system around a period of time can be modeled as a stochastic process. In the anti-lock braking system, the hidden states are the software operational state and error states and the observation states are observable and measurable parameters. The knowledge about faults, errors and failures are the most required insight for reliability prediction. Reliability modeling is the process of determining the probability of failure within that specified time. Error Propagation in software systems is one of the important attributes in understanding the behavior of failures in software systems. A considerable amount of research effort has been taken into the findings of error propagation impact [23,24]. Propagation analysis may be used to find the modules that are most vulnerable in a system, which helps in the determination of how various modules in the system affect each other in the presence of errors.
Two-level priority scheduling framework in a max-plus linear representation
Published in SICE Journal of Control, Measurement, and System Integration, 2021
Kyohei Sagawa, Yoichi Shimakawa, Hiroyuki Goto
Practical applications of MPL systems are diverse; scheduling of a railway timetable is an instance. Goverde [3] applied an MPL system to railway timetable scheduling, along with a timetable analysis based on stability theory. Meanwhile, existing railway systems have constraints that cannot be manipulated with existing frameworks. An instance is that a rapid service train do not stop at some stations, overtaking previous trains, while local trains might have different origin and destination stations. If an overtaking is planned at an intermediate station, then the orders of the relevant trains change at succeeding stations. To address these problems, Goto and Takahashi [4] consider different departure, terminal, and intermediate stations, as well as the maximum number of the latter. Yoshida et al. [5] construct a scheduling method to resolve resource contention in an MPL context. Kusunoki and Takahashi [6] describe a railway system on an MPL system considering maximum and minimum capacities with different departure, terminal, and intermediate stations.