Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
In the cloud manufacturing environment, innovative service composition is an important way to improve the capability and\nefficiency of resource integration and realize the upgrading and transformational upgrade of the manufacturing industry. In order\nto build a stable innovative service composition, we propose a novel composite model, which uses two-way selection according to\ntheir cooperation to recommend the most suitable partners. Firstly, a rough number is applied to quantify the semantic evaluation.\nUsing the expectation of cooperative condition as reference points, prospect theory is then applied to calculate the cooperative\ndesires for both sides based on participantsâ?? psychological attitudes toward gains and losses. Next, the cooperative desires are used\nto establish the two-way selection model of innovative service composition. The solution is determined by using an improved\nteaching-learning-based optimization algorithm. Compared with traditional combined methods in the cloud manufacturing\nenvironment, the proposed model fully considers the long-neglected needs and interests of service providers. Prospect theory\ntakes psychological expectations and varying attitudes of decision makers towards gains and losses into account. Moreover, an\ninterval rough number is used to better preserve the uncertain information during semantic quantification. Experimental results\nverify the applicability and effectiveness of the proposed method....
With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy\nconsumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive.\nThus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation\noffloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In\naddition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which\nchannel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model\nand prove the existence of Nash equilibrium (NE).Thereal-time update computation offloading algorithm (RUCO) is proposed to\nobtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading\ndecision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm\nthrough experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations\nand can obtain the offloading strategy with lower cost....
Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers.\nImproving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption,\nbut the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level\nagreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in\nunknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the\nimproved ant colony algorithm to reduce the energy consumption and satisfy usersâ?? experience, which achieves the balance\nbetween energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the\ntraditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter\nthe global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional\nant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%,\nand 30% of energy consumption while satisfying user experience....
This paper will present the authorsâ?? own techniques of secret data management and\nprotection, with particular attention paid to techniques securing data services. Among the solutions\ndiscussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential)\ndata sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of\nprotecting and securing data against unauthorized acquisition. Data-sharing protocols will execute\nthe tasks of securing a special type of information, i.e., data services. The area of data protection\nwill be defined for various levels, within which will be executed the tasks of data management and\nprotection. The authorsâ?? solution concerning securing data with the use of cryptographic threshold\ntechniques used to split the secret among a specified group of secret trustees, simultaneously enhanced\nby the application of linguistic methods of description of the shared secret, forms a new class of\nprotocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring\nto the service management and securing will be dedicated to various levels of data management.\nThese levels could be differentiated both in the structure of a given entity and in its environment.\nThere is a special example thereof, i.e., the cloud management processes. These will also be subject\nto the assessment of feasibility of application of the discussed protocols in these areas. Presented\nsolutions will be based on the application of an innovative approach, in which we can use a special\nformal graph for the creation of a secret representation, which can then be divided and transmitted\nover a distributed network....
The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for largescale\ncomputing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices.\nTo investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a\ncomputation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile\ndevices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and\noffloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal\nsolution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the\nbaseline algorithm in most cases....
Loading....