An Energy-Efficient Resource Allocation Scheme for Mobile Ad Hoc Computational Grids

Abstract

Due to recent advancements in mobile computing and communication technologies, mobile ad hoc computational Grids are emerging as a new computing paradigm, enabling innovative applications through sharing of computing resources among mobile devices without any pre-existing network infrastructure. Energyefficient resource allocation is one of the key issues in mobile ad hoc computational Grids due to limited battery life of mobile nodes. To reduce energy consumption, we propose a hybrid power-based resource allocation scheme for allocation of interdependent tasks to nodes within mobile ad hoc computational Grid. The basic idea is to exploit dependencies and task type, and allocate interdependent tasks to nodes accessible at minimum transmission power. We also propose a power-based algorithm to search a group of closest nodes to allocate a set of interdependent tasks. Compared to traditional algorithms, complexity of proposed algorithm depends on number of transmission power levels rather than number of nodes within a Grid. The scheme is validated in a simulation environment using various workloads and parameters.

Context-aware multi-objective resource allocation in mobile cloud

“…However, due to recent enhancements in mobile computing and wireless communication technologies, many researchers have turn to mobile grid and mobile cloud computing where mobile devices are resource providers. There are many studies in Mobile Ad-Hoc Grid such as [21,22], investigate the task allocation problem among nearby mobile devices which does not require any pre-configured infrastructure. The resource allocation problem in those studies is somehow similar to our work, but most of the approaches presented in mobile grid only consider the completion time or energy consumption as objectives.…”

“…The resource allocation problem in those studies is somehow similar to our work, but most of the approaches presented in mobile grid only consider the completion time or energy consumption as objectives. For example, [21] tries to minimize the consumed power satisfying deadline constraint of applications.…”

Cluster based Optimization Routing Strategy for Data Communication in Mobile Computing

“…In case multiple data paths exist for single data packets, routing strategy will decide feasible path to minimize transmission time for a data communication in mobile computing. Some of the routing algorithm and or methods have been reported in the literature, such as Routing Scheme [1,2,3,5,19,20], Salesman Problem [4], Grid Computing [6,8], Shortest paths in restricted graph [7], Task Allocation Scheme [9], Single system image [10], Performance and cost in a cloud computing system [11], Resource allocation policy [12], Job scheduling in mobile grids [13,14], Enhancing the Overall Performance of Distributed Computing System [15], Remote execution for mobile computing [16] and Resource allocation scheme for mobile ad hoc computational grids [17,18]. This research paper introduces a routing strategy by implementing a routing algorithm to get maximum optimization of data communication that will also enhance the performance of mobile computing as well.…”

Network Aware Resource Allocation Scheme for Mobile Ad Hoc Computational Grid

“…The schemes are validated in a simulated environment using various workloads and parameters.A distributed computing infrastructure which allows mobile robots to share computing resources on demand without pre-existing network infrastructure is known as a mobile ad hoc computational Grid [4]. The bandwidth at different network portions varies over the time and different nodes often experience different connection quality at the same time due to traffic load and constrained communication environment.…”

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Mobile ad hoc computational Grids are characterized by unreliable and dynamic communication environment. First, we describe our previous resource allocation scheme [4] based on transmission power control mechanism which aims to improve energy efficiency and network capacity. Therefore, an effective and robust resource allocation scheme should consider connection quality between nodes in addition to other factors such as transmission power, processing speed and task size.

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