Pattern Analysis and Intelligent Systems
narges jafari; Farhad Soleimanian Gharehchopogh
Volume 6, Issue 3 , August 2020, Pages 119-132
Abstract
Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the ...
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Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.
Computer Networks and Distributed Systems
Bharat Mahaur; Aishwarya Gupta
Volume 6, Issue 3 , August 2020, Pages 133-144
Abstract
Vehicular Ad-Hoc Networks (VANETs) is a novel technology that has recently emerged and due to its swift changing topology and high mobility nature, it has become problematic to design an efficient routing protocol in VANETs’ amongst both moving and stationary units. Also, the existing routing algorithms ...
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Vehicular Ad-Hoc Networks (VANETs) is a novel technology that has recently emerged and due to its swift changing topology and high mobility nature, it has become problematic to design an efficient routing protocol in VANETs’ amongst both moving and stationary units. Also, the existing routing algorithms are not very effective to satisfy all requirements of VANETs. This paper explores the need of a reliable routing and proposes an approach that makes use of an extended restricted greedy forwarding mechanism to select the next forwarding vehicle on basis of its average relative velocity and neighborhood density with its own neighboring vehicles. We also use static PCR junction node which forwards the packet to correct road segment vehicle based upon the relative information. The objective of this paper is to increase route reliability by increasing throughput with considerable end to end delay. Simulation results show that the proposed approach IJDRP outperforms existing GPCR and E-GyTAR.
Computer Networks and Distributed Systems
Derdus Kenga; Vincent Oteke Omwenga; Patrick Job Ogao
Volume 6, Issue 3 , August 2020, Pages 145-154
Abstract
The ability to measure the energy consumed by cloud infrastructure is a crucial step towards the development of energy efficiency policies in the cloud infrastructure. There are hardware-based and software-based methods of measuring energy usage in cloud infrastructure. However, most hardware-based energy ...
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The ability to measure the energy consumed by cloud infrastructure is a crucial step towards the development of energy efficiency policies in the cloud infrastructure. There are hardware-based and software-based methods of measuring energy usage in cloud infrastructure. However, most hardware-based energy measurement methods measure the energy consumed system-wide - including the energy lost in transit. In an environment such as the cloud, where energy consumption can be a result of different components, it is important to isolate the energy, which is consumed as a result of executing application workloads. This information can be crucial in making decisions such as workload consolidation. In this paper, we propose an experimental approach of measuring power consumption as a result of executing application workloads in IaaS cloud. This approach is based on Intel’s Running Average Power Limit (RAPL) interface. Application workload is obtained from Phoronix Test Suite (PTS)’ 7zip and aio-stress. To demonstrate the feasibility of this approach, we have described an approach, which can be used to study the effect of workload consolidation on CPU and I/O's power performance by varying the number of Virtual Machines (VMs) . Power is measured in watts. Performance of CPU is measured in Million Instructions per Second (MIPS) and I/O performance (as a result of processing data intensive) is measured in MB/s. Our results on the effect of workload consolidation has been compared with previous research and was found to be consistent. This shows that the proposed method of measuring power consumption is accurate.
Computer Networks and Distributed Systems
Hanieh Ghorashi; Meghdad Mirabi
Volume 6, Issue 3 , August 2020, Pages 155-168
Abstract
Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental ...
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Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different resources of a distributed system in order to optimize resource utilization and response time. In this paper, an optimization-based method for task scheduling is presented in order to improve the efficiency of cloud computing. In the proposed approach, three criteria for scheduling, including the task execution time, the task transfer time, and the cost of task execution have been considered. Our method not only reduces the execution time of the overall tasks but also minimizes the maximum time required for task execution. We employ the Multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) for solving the scheduling problem. To evaluate the efficiency of the proposed method, a real cloud environment is simulated, and a similar method based on Multi-Objective Particle Swarm Optimization is applied. Experimental results show the superiority of our approach over the baseline technique.
Software Engineering and Information Systems
Asieh Ghanbarpour; Hassan Naderi; Soheil ZareMotlagh
Volume 6, Issue 3 , August 2020, Pages 169-186
Abstract
Abstract—Keyword Search is known as a user-friendly alternative for structured languages to retrieve information from graph-structured data. Efficient retrieving of relevant answers to a keyword query and effective ranking of these answers according to their relevance are two main challenges in ...
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Abstract—Keyword Search is known as a user-friendly alternative for structured languages to retrieve information from graph-structured data. Efficient retrieving of relevant answers to a keyword query and effective ranking of these answers according to their relevance are two main challenges in the keyword search over graph-structured data. In this paper, a novel scoring function is proposed, which utilizes both the textual and structural features of answers in order to produce a more accurate order of answers. In addition, a query processing algorithm is developed based on information spreading technique to enumerate answers in approximate order. This algorithm is further improved by allowing a skewed development toward more promising paths and enables a more efficient processing of keyword queries. Performance evaluation through extensive experiments on a standard benchmark of three real-world datasets shows the effectiveness and efficiency of the proposed algorithms.Index Terms—Information retrieval, Database, Keyword search, Relevant answers, Information spreading.
Software Engineering and Information Systems
shahrzad Oveisi; Mohammad Nadjafi; Mohammad Ali Farsi; Ali moeini; Mahmood Shabankhah
Volume 6, Issue 3 , August 2020, Pages 187-200
Abstract
One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software ...
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One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software risks based on the FMEA indices with respect to reduce the risk level by means of experts’ opinions. To this end, TOPSIS as one of the most applicable methods of prioritizing and ordering the significance of events has been used. Since uncertainty in the data is inevitable, the entropy principle has been applied with the help of fuzzy theory to overcome this problem to weigh the specified indices.The applicability and effectiveness of the proposed approach is validated through a real case study risk analysis of an Air/Space software system. The results show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision making of our software system that is in the early stages of software life cycle. After obtaining the events and assessing their risk using the existing method, finally, suggestions are given to reduce the risk of the event with a higher risk rating.