Exploiting CloudSim in a multiformalism modeling approach for cloud based systems

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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“…The main objectives of the proposed optimization model are identified as follows [14][15][16][17][18][19][20][21]. a.…”

Section: Proposed Task Scheduling Algorithmmentioning

confidence: 99%

Optimization model for QoS based task scheduling in cloud computing environment

Potluri

Rao

2020

IJEECS

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“…The developed system named APLOMB (Appliance for Outsourcing Middleboxes), outsources middlebox functionalities to a third party for ease of management and reduced price. In another development, a cloud computing architecture based on SDCC concepts is presented in [22] which focuses on improving services delivery features for data-intensive applications and suggests software-defined enhancements for Cloudsim [54] simulation software. This work also provides a flexible guideline for improving existing cloud models with enhanced software-defined administration features.…”

Section: Recent Developments In Sdcc Model-based Solutionsmentioning

confidence: 99%

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Abbasi

Shamshirband

et al. 2019

IEEE Access

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Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

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“…Solution can be obtained by means of simulation, analytical techniques or by applying multisolution, that is the possibility of using alternate tools, explicitly decided by the modeler or automatically chosen according to the characteristics of the model, to perform the analysis. This approach also preserves, in general, tracking of numerical results back to logical elements in the model, and can provide model-wide or submodel-wide results, such as properties of parts of the system that emerge from element-related results, and may also be used to interface existing tools with new solvers, extending their applicability [10]. Multiformalism modeling approaches may support combinatorial formalisms [125], logic modeling [25], discrete state space based formalisms [6,42], continuous state space based formalisms [6], and hybrid formalisms [8] (that may use specialized solution techniques [9]).…”

Section: Multiformalism Approachesmentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

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The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

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Exploiting CloudSim in a multiformalism modeling approach for cloud based systems

Cited by 36 publications

References 23 publications

Optimization model for QoS based task scheduling in cloud computing environment

Optimization model for QoS based task scheduling in cloud computing environment

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Towards Human Cell Simulation

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