The Experts below are selected from a list of 1986 Experts worldwide ranked by ideXlab platform
Mohamed Abouhawwash - One of the best experts on this subject based on the ideXlab platform.
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energy aware whale optimization algorithm for real time task scheduling in multiprocessor Systems
Applied Soft Computing, 2020Co-Authors: Mohamed Abdelbasset, Doaa Elshahat, Kalyanmoy Deb, Mohamed AbouhawwashAbstract:Abstract The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.
Mohamed Abdelbasset - One of the best experts on this subject based on the ideXlab platform.
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energy aware whale optimization algorithm for real time task scheduling in multiprocessor Systems
Applied Soft Computing, 2020Co-Authors: Mohamed Abdelbasset, Doaa Elshahat, Kalyanmoy Deb, Mohamed AbouhawwashAbstract:Abstract The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.
Ju Gyun Kim - One of the best experts on this subject based on the ideXlab platform.
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high performance cycle detection scheme for Multiprocessing Systems
Lecture Notes in Computer Science, 2004Co-Authors: Ju Gyun KimAbstract:This paper presents a non-blocking deadlock detection scheme with immediate cycle detection in Multiprocessing Systems. It assumes an expedient state and a special case where each type of resource has one unit and each request is limited to one resource unit at a time. Unlike the previous deadlock detection schemes, this new method takes O(1) time for detecting a cycle and O(n+m) time for blocking or handling resource release where n and m are the number of processes and that of resources in the system. The deadlock detection latency is thus minimized and is constant regardless of n and m. However, in a Multiprocessing system, the operating system can handle the blocking or release in parallel on a separate processor, thus not interfering with user process execution. To some applications where deadlock is concerned, a predictable and zerolatency deadlock detection scheme could be very useful.
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an algorithmic approach on deadlock detection for enhanced parallelism in Multiprocessing Systems
Proceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis, 1997Co-Authors: Ju Gyun KimAbstract:This paper presents a non-blocking deadlock detection scheme with immediate knot detection in Multiprocessing Systems. We assume an expedient state and a special case where each request is limited to one resource unit at a time. Unlike the previous deadlock detection schemes, this new method, using some different data structures takes O(1) time for detecting a knot and O(nm) time for blocking or handling resource release where n and m are the number of processes and that of resources in the system. The deadlock detection latency is thus minimized and is constant regardless of n and m. However, in a Multiprocessing system, the operating system can handle the blocking or release on-the-fly running on a separate processor, thus not interfering with user process execution. To some applications where deadlock is concerned, a predictable and zero-latency deadlock detection scheme could be very useful.
Kalyanmoy Deb - One of the best experts on this subject based on the ideXlab platform.
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energy aware whale optimization algorithm for real time task scheduling in multiprocessor Systems
Applied Soft Computing, 2020Co-Authors: Mohamed Abdelbasset, Doaa Elshahat, Kalyanmoy Deb, Mohamed AbouhawwashAbstract:Abstract The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.
Doaa Elshahat - One of the best experts on this subject based on the ideXlab platform.
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energy aware whale optimization algorithm for real time task scheduling in multiprocessor Systems
Applied Soft Computing, 2020Co-Authors: Mohamed Abdelbasset, Doaa Elshahat, Kalyanmoy Deb, Mohamed AbouhawwashAbstract:Abstract The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.
