H. Momeni; N. Mabhoot
Abstract
Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations ...
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Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations to perform the tasks are critical. In real-time applications, missing the deadlines for the tasks will cause catastrophic consequences; thus, real-time task scheduling in cloud computing environment is an important and essential issue. Furthermore, energy-saving in cloud data center, regarding the benefits such as reduction of system operating costs and environmental protection is an important concern that is considered during recent years and is reducible with appropriate task scheduling. In this paper, we present an energy-aware task scheduling approach, namely EaRTs for real-time applications. We employ the virtualization and consolidation technique subject to minimizing the energy consumptions, improve resource utilization and meeting the deadlines of tasks. In the consolidation technique, scale up and scale down of virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely Energy-aware Task Scheduling in Cloud Computing(ETC), Vertical VM Scale Up(V2S), Horizontal VM Scale up(HVS) and Physical Machine Scale Down(PSD). We present the formal model of the proposed approach using Timed Automata to prove precisely the schedulability feature and correctness of EaRTs. We show that our proposed approach is more efficient in terms of deadline hit ratio, resource utilization and energy consumption compared to other energy-aware real-time tasks scheduling algorithms.
C.3. Software Engineering
N. Rezaee; H. Momeni
Abstract
Model checking is an automatic technique for software verification through which all reachable states are generated from an initial state to finding errors and desirable patterns. In the model checking approach, the behavior and structure of system should be modeled. Graph transformation system is a ...
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Model checking is an automatic technique for software verification through which all reachable states are generated from an initial state to finding errors and desirable patterns. In the model checking approach, the behavior and structure of system should be modeled. Graph transformation system is a graphical formal modeling language to specify and model the system. However, modeling of large systems with the graph transformation system suffers from the state space explosion problem which usually requires huge amounts of computational resources. In this paper, we propose a hybrid meta-heuristic approach to deal with this searching problem in the graph transformation system because meta-heuristic algorithms are efficient solutions to traverse the graph of large systems. Our approach, using Artificial Bee Colony and Simulated Annealing, replaces a full state space generation, only by producing part of it checking the safety, and finding errors (e.g., deadlock). The experimental results show that our proposed approach is more efficient and accurate compared to other approaches.
