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In order to realize globalization of cloud computing, joint use of different services of different cloud providers has become an inevitable trend. The geo-distributed cloud consists of several different clouds, providing a general environment for cloud computing. In data placement, many recently proposed data placement algorithms unilaterally use a single performance index to evaluate the performance of the algorithm. In task scheduling, when tasks are allocated with excess cloud resources, resources are wasted. When little cloud resources are allocated to the complex task, cause the overall progress of the system to stagnate, the overall progress of the system is stalled. For solving the above problems, the data placement method and the task scheduling method are proposed. In the proposed data placement scheme, multiple performance indicators are considered. The detection of the straggling nodes and the reasonable allocation of cloud resources are taken into account when the task is scheduled. For proving the superiority of the proposed methods, extensive experiments are conducted. In terms of the data placement, when the number of files is set as 800, the safety level of the proposed data placement algorithm is 7.0, which is 27.3% higher than that of the IDP algorithm, 45.8% higher than that of the GA-DPSO algorithm and 16.7% higher than that of the H2DP algorithm. As for the task scheduling, the percentage improvement in the time overhead of the proposed task scheduling method is the lowest, which implies that the time overhead of the proposed task scheduling algorithm is closest to the optimal time and is the shortest. [ABSTRACT FROM AUTHOR]

With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost. [ABSTRACT FROM AUTHOR]

• A WP-MEC system with N WDs and one HAP is considered, where WDs and HAP are single antenna devices. • The goal is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. • An alternating direction multiplier method (ADMM) based distributed optimization method is proposed. • Experimental results show that the proposed method greatly increases the weighted sum computation rate while keeping the energy consumption at a low level. In this paper, a wireless powered mobile edge computing (WP-MEC) system is considered, in which a hybrid access point integrated with MEC servers can charge N wireless devices (WDs) by broadcasting radio-frequency signals, and the time division multiple access (TDMA) protocol is used for task offloading of WDs. The goal of this paper is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. To solve this optimization problem, an alternating direction multiplier method (ADMM) based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, which are solved by N WDs. Experimental results show that the proposed method outperforms the benchmarks and greatly increases the weighted sum computation rate while keeping the energy consumption at a low level under the premise of time complexity O (N). [ABSTRACT FROM AUTHOR]

This article proposes context-aware integrated scheme for mobile cloud service allocation, which can provide desirable cloud services to mobile cloud clients according to the mobile cloud contexts. The article makes use of various contexts information in the mobile cloud environment, such as the mobile cloud user's preferences, the battery levels and the parameters of cloud datacenter servers to improve the performance of mobile cloud. Interplay coupling of the mobile cloud users and the cloud datacenter supplier is used to achieve global optimization of the mobile cloud system. The article integrates energy-based service provisioning, cloud virtual resource allocation and dynamic load balancing. The integrated scheme can adapt to dynamic context information changes of the mobile cloud system such as device energy consumption, bandwidth and server load without compromising mobile application's quality of service. Based on the proposed model, the context-aware integrated mobile cloud service allocation algorithm is proposed, it uses the mobile cloud service profile to select the services among the available service suppliers to enhance the mobile cloud user's quality of experience. The efficiency of the context-aware integrated mobile cloud service allocation algorithm is tested by the experiments. [ABSTRACT FROM AUTHOR]

The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy consumption and shorter total delay. To address the problem that mobile devices cannot handle many computationally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application requirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency; the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks. [ABSTRACT FROM AUTHOR]

ABSTRACT The paper proposes hybrid cloud service selection optimization scheme. Hybrid cloud service selection optimization is distributed to and performed at two levels: hybrid cloud user agent- hybrid cloud service agent at hybrid cloud service layer and hybrid cloud service agent- hybrid cloud agent at hybrid cloud resource layer. Interactions among hybrid cloud user agent, hybrid cloud service agent, public cloud agents and private cloud agents are mediated by means of market mechanisms. Two level hybrid cloud service selection optimization maximizes the interests of hybrid cloud user agents, hybrid cloud service agent, public cloud agents and private cloud agents. Hybrid cloud service selection process includes two parts: hybrid cloud service provisioning and cloud resource allocation. The paper presents a two-level hybrid cloud service selection algorithm used to perform service provisioning and resource allocation. In the simulations, compared with other related algorithm, our proposed hybrid cloud service selection algorithms achieve the better performance with acceptable overhead. © 2015 Wiley Periodicals, Inc. Comput. Appl. Eng. Educ. Comput Appl Eng Educ 23:645-657, 2015; View this article online at ; DOI [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and the explosive growth of network applications, traditional computer networks have ushered in unprecedented challenges and opportunities. To solve the communication delay between controllers and switches and the communication problem between controllers due to link failure in the network, this paper considers the delay between controllers, the delay problem between controllers and switches, and the reliability problem. It proposes a dynamic controller placement method based on delay and load optimization. A multi-objective optimization model based on link failure is constructed, and the multi-objective optimization problem with constraints is solved by improving the controller prevention algorithm with spectral clustering. Meanwhile, this paper proposes a resource allocation method based on task delay and reliability constraints to solve the problem of considerable task completion delay and wasted computational resources due to uneven resource allocation of edge servers. A model based on task latency and dynamic constraints is constructed, and a heuristic ant colony algorithm solves an adaptive allocation scheme for computing resources. The experimental results show that the proposed resource allocation algorithm weighs the delay between controllers, the communication delay between controllers and switches, and the reliability and gives a reasonable controller placement scheme and controller locations. The proposed resource allocation algorithm can optimize computing resources and reduce task completion delay. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a flexible layered control policy for sensor resource allocation in a sensor grid. In order to allocate sensor resources in the system to maximize the sensor grid utility, different controllers are deployed at three levels: a job-level controller, an application group controller, and a sensor grid system controller. At the lowest levels, job-level controllers perform fast, frequent, local adaptation for optimizing a single sensor grid application at a time, while, at the highest levels, sensor grid system controllers perform less frequent control actions to optimize all applications. Sensor grid system control considers all sensor grid applications in response to large system changes at coarse time granularity. Sensor grid system control exploits the interlayer coupling of the resource layer and the application layer to achieve a system-wide optimization based on the sensor grid users’ preferences. Job-level control adapts a single application to small changes at fine granularity. The layered control system uses a set of utility functions to evaluate the performance of sensor grid applications and groups. The control system chooses control actions that would result in a higher level of utility. In the simulation, a performance evaluation of the algorithm is carried out. [Copyright &y& Elsevier]

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Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

This paper proposes a hierarchical control system in grid virtual organization. The hierarchical system can be decomposed into multiple application groups, which can be further decomposed into multiple applications. At the top of the hierarchy, the global controller controls the gross allocation of resources to the groups. At the next level down, the group controller coordinates the local deployments of all applications that consume the local allocation of resources. At the lowest level, the local controllers adjust the local resource usages to optimize the utility of single application. The hierarchical control system considers all applications and coordinates all layers of grid architecture upon any changes. According to different time granularity, we adopt a different control scheme. The global control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity, while local control adapts a single application to small changes at fine granularity. This paper adopts utility-driven cross layer optimization for grid applications to find a system wide optimization and solves the cross-layer optimization by using pricing based decomposition. A set of hierarchical utility functions is used to measure the performance of the grid system that follows the system, group and application hierarchy. This paper uses total utility to measure the overall quality of grid system. The experiments are conducted to test the performance of the hierarchical control algorithms. [ABSTRACT FROM AUTHOR]

The article considers the resource allocation and scheduling problem in a grid computing environment. The article proposes system optimisation scheduling (SOS) that provides a potential solution of joint optimisation of objectives for both the resource and application layer, which combine both application-oriented and resource-oriented scheduling benefits. Grid systems will strive to find an optimal relation between user satisfaction and resource utilisation. Utility functions are used to express grid user's Quality of Service requirement, resource provider's benefit function and system's objectives. In order to verify the efficiency of the proposed scheduling algorithm, we compare the performance of application optimisation scheduling, resource optimisation scheduling, SOS with a traditional Round-Robin algorithm. The simulations study the effect of the request rate and task-to-resource ratio on the different scheduling algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a market-oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This allows the integration of different optimization objectives into allocation process. This paper is targeted to solve the above issues by using utility-based optimization scheme. We decompose the optimization problem into two levels of sub problems so that the computational complexity is reduced. Two market levels converge to its optimal points; a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation. The experiments show that scheduling based on pricing directed resource allocation involves less overhead and leads to more efficient resource allocation than conventional Round-Robin scheduling. [Copyright &y& Elsevier]

Limited battery capacity and poor computing capability of wireless devices have been longstanding performance limitations in the Internet of Things (IoT) era. Employing Wireless Energy Transfer (WET) technology, wireless devices of the Mobile Edge Computing (MEC) systems can be released from these limitations and achieve a better quality of experience (QoE). This paper considered a wireless powered mobile edge computing (WP-MEC) system with one mobile device, where a double antenna hybrid access point (HAP) (integrated with a MEC server) transmits wireless energy to the device and communicates with the wireless terminal to assist in its data processing. We investigated the average computation rate maximization problem in this system and proposed an online service rate maximization (OSRM) algorithm to tackle this problem. In each time slot, the proposed algorithm optimally decides the time allocation policy and the CPU-frequency for the mobile device. Simulation results show that the proposed algorithm can balance the time average computation rate and the task buffer queue length, and outperforms the benchmark schemes. [ABSTRACT FROM AUTHOR]