Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantjuages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, a new evolutionary algorithm is proposed which named CSA to schedule the tasks in Cloud computing. CSA algorithm is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. The simulation results demonstrated that when the value of Pa is low, the speed and coverage of the algorithm become very high. I. INTRODUCTIONCloud computing evolved through the recent advancements in hardware, virtualization technology, distributed computing, and service delivery over the Internet. While Cloud computing may not involve a lot of new technologies, it certainly represents a new way of managing IT. In many cases, this will not only change the workflow within the IT organization, it will often result in a complete reorganization of the IT department. Cost savings and scalability can be highly achieved from cloud computing [1]. The "Cloud" metaphor is a reference to the ubiquitous availability and accessibility of computing resources via Internet technologies [2]. Generally, cloud computing services can be categorized into three main types of services: Infrastructure as a Service, Platform as a Service and Software as a Service. These services can then be accessed through a cloud client, which could be a web browser, mobile app, and so on [3]. Cloud computing provides implementation agility, lower capital expenditure, location independence, resource pooling, broad network access, reliability, scalability, elasticity, and ease of maintenance [4]. The scheduling of a task workflow in a distributed computing platform is a well-known NP-hard problem [5]. The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of Manuscript received June 3, 2014; revised October 9, 2014. tasks in a Cloud computing platform [6]. For this reason, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks in Grid and Cloud environments [7]. Recently, many papers are published which used evolutionary algorithms like genetic, ant colony, bee colony and PSO for optimization problems. Due to the advantages of Cuckoo Search Algorithm (CSA) [8], this paper addresses a task scheduling problem in a homogeneous Cloud infrastructure considering the minimization of the total waiting time of the tasks based on the CSA. CSA is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fru...

Purpose This research specifies the factors impacting on the success of supply chain management (SCM) systems in the organizations. This paper aims to assess the effect of knowledge sharing, the vehicular ad hoc network (VANET), radio frequency identification technology (RFID) and near field communications (NFC) and the social capabilities of information technology (IT) and information and communication technology (ICT)on the success of the SCM systems and the simplification of the SCM challenges and other factors affecting its success. Design/methodology/approach A questionnaire is designed for measuring the elements of the proposed model. The questionnaires are revised by experts with experiences in SCM. For statistical analysis, SPSS 24.0 and SMART- PLS (partial least squares) 3.2.6 software package are used. The structural equation modeling (SEM) analysis procedure is conducted in two stages. The reliability analysis and confirmatory factor for analyzing the dimensions and items are included in the first stage. The second stage involves evaluating the assumptions through the SEM. Findings The results have depicted that four variables (knowledge sharing, VANET, RFID and NFC, and the social capabilities of using IT) affect the success of SCM systems. Originality/value This research specifies the factors impacting on the success of SCM in the organizations. These technologies aid companies in improving their performance in the SCM and facilitating coherence and collaboration.

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The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja’s simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.