In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

/pdf/a-comparative-analysis-of-multi-criteria-decision-making-4gxhp4h1b4.pdf

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

One of the operational challenges in the Internet of Things (IoT) is load balancing, which is the focus of interest of this article. We propose a three-phase enhanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc on-demand distance vector (enhanced-AODV) routing protocol for multiwireless sensor networks (multi-WSNs). The three phases are categorized based on traffic priority, namely: 1) high priority; 2) low priority; and 3) ordinary network traffic. The network architecture is divided into chains, i.e., local and public chains, where the cluster heads (CHs) and base stations (BSs) are used, respectively, to manage the network traffic based on priority information with alternative route allocation. Moreover, our three-phase enhanced-AODV protocol provides traffic categorization with alternatives route allocation to minimize energy consumption and prolong the lifetime of participating devices in the network. The proposed model is implemented in the simulation environment to overview results statistics in terms of network lifetime, prioritize traffic, computation and communication costs, latency, and packet lost ratio (PLR). Findings from the simulation suggest that our scheme achieves 15% improvement in network lifetime, 17% latency, 22% PLR, and approximately 10% in the computation and communication costs of the network, in comparison to three other similar protocols.

Enhanced-AODV: A Robust Three Phase Priority-Based Traffic Load Balancing Scheme for Internet of Things

Load balancing has been a key concern for locally distributed multiprocessor systems. The emergence of computational grid extends this problem, such as scalability, heterogeneity of computing resources and considerable communication delay. In this paper, we study the problem of scheduling a large number of CPU-intensive jobs on such systems. The time spent by a job in the system is considered as the main issue that needs to be minimized. The proposed dynamic algorithm of scheduling jobs consists of two policies: Instantaneous Distribution Policy (IDP) and Load Adjustment Policy (LAP). Our algorithm does not address directly the load balancing problem since it is completely unrealistic in such large environments, but we will show that even a non-perfectly load balanced system can behave reasonably well by taking into account the jobs' time demands. The proposed algorithm is evaluated by a series of simulations.

Towards decentralized load balancing in a computational grid environment

Tasks allocation is an important step for obtaining high performance in distributed computing system DCS. This article attempts to develop a mathematical model for allocating the tasks to the proce...

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

Load balancing is a very important and complex problem in computational grids. In load balancing, jobs should be effectively distributed among resources in order to minimize the average completion time and maximize the utilization of all resources even those with low reliabilities and capacities. However, using the less reliable and slow resources implies worse completion time, whereas always selecting the powerful and reliable resources undermines the utilization of other resources. So, it is essential to develop an efficient load balancing method which makes a good tradeoff between these criteria in a way that satisfies the quality of service of jobs and fairly distributes jobs between resources based on their reliabilities and capacities. This paper proposes an efficient multicriteria load balancing method using technique for order preference by similarity to ideal solution which treats load balancing as a multi criteria decision making problem. Also, an effective weighting mechanism is proposed, which adaptively adjusts the weights of the considered criteria according to the system’s current state and jobs’ characteristics. This mechanism can make an efficient tradeoff between the considered criteria and accurately reflect the importance of each one. By simulation, the proposed method was evaluated and compared with other approaches from the literature. In the range of examined parameters’ values, the simulation results show that proposed method minimizes the average completion time by 8.7–15.7%, increases the throughput ratio up to 15.8–19.4%, and maximizes the load balancing level by 7.68–20.1%.

A reliable, TOPSIS-based multi-criteria, and hierarchical load balancing method for computational grid

Reliable and efficient hierarchical organization model for computational grid

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A reliable, TOPSIS-based multi-criteria, and hierarchical load balancing method for computational grid

Reliable and efficient hierarchical organization model for computational grid