An Optimization Framework for Migrating and Deploying Multiclass Enterprise Applications Into the Cloud

Cloud computing has been widely used in various industries in recent years. However, when migrating enterprise applications into the cloud, enterprise users face a problem with minimizing migration time and cloud resource providers face a dilemma of resource allocation problem, with the objective of maximizing the migration utility of enterprise users while minimizing the cost of cloud resource providers. In order to achieve them, this paper considered cloud migration objectives including cloud migration time, cloud migration utility, and cloud data center cost, and proposed a resource allocation model for enterprise applications migration into the cloud. The model is divided into two stages: the bandwidth allocation for enterprise applications migration to the cloud and the physical resource allocation of cloud resource providers for enterprise applications deployment into the cloud. In the first stage, we aim to minimize the cloud migration time for enterprise applications, and propose a scheme of bandwidth allocation for each component of applications. In the second stage, we present the resource allocation of cloud resource providers and propose a gradient-based algorithm which can achieve optimal resource allocation. Finally, we give some numerical simulation results to illustrate the performance of the proposed algorithm.

In this paper, the optimal placement and dynamic resource allocation problem has been investigated for multi-UAV enhanced reconfigurable intelligent surface (RIS) assisted wireless network with uncertain time-varying wireless channels. This paper aims to stimulate the potential of RIS by adding mobility to RIS through unmanned aerial vehicles (UAV). A novel UAV optimal placement and dynamic resource allocation technique needs to be developed jointly. A novel online rein-forcement learning based optimal resource allocation algorithm has been designed. Firstly, a deep Q-learning based K-means clustering algorithm is utilized to optimize the deployment of the multi-UAV. Then, an online actor-critic reinforcement learning algorithm is developed to learn the optimal transmit power control as well as mobile RIS phase shift control policy. Compared with conventional learning algorithms, the developed algorithm can learn the optimal resource allocation and multi-UAV placement for mobile RIS-assisted wireless networks in real-time even with uncertain and time-varying wireless channels. Eventually, numerical simulations are provided to demonstrate the effectiveness of developed schemes.

The continuous decrease in good quality water and land resources and concurrent increase in global population accentuates the need of optimal allocation of these resources to fulfilling the rising food requirements. This study presents the formulation and application a management model for the optimal allocation of available good quality water and land resources to maximize the farm revenue of a canal command area. A groundwater balance constraint was imposed on the model, which moderates the irrigation-induced environmental problems of waterlogging and salinization, while making the optimal allocation of resources. The model results show a reduction in mustard, rice, and gram crop areas against an increase in sorghum, millets, and wheat areas. The net annual revenue from the command area increased by about 18 % under the optimal allocation plans. The farmers and stakeholders concerned in the actual agricultural production process are suggested to use groundwater and canal water conjunctively to maximizing the farm income. This strategy would also mitigate the hydrological imbalances to the groundwater system without installing costly drainage systems which is not viable as the quality of groundwater is poor and drainage water may cause a serious disposal problem. The developed model can be used as a reliable decision tool for taking the farm and regional level decisions of optimal land and water resources allocation and is able to solve the irrigation-induced environmental problems of agricultural systems.

Cognitive Radio Ad Hoc Networks (CRAHN) is the infrastructure-less network model of Cognitive radios developed in an ad hoc manner. Regulating resource allocation in CRAHN is considered to be an energy constrained problem. Many researches have been performed for allocating spectrum in an efficient way using various protocols. In this paper, the Spectrum-Map-Empowered Opportunistic Routing (SMOR) model has been utilized as the fundamental model for routing and an energy efficient optimal spectrum allocation solution is provided. In the proposed model, the previously modified SMOR model is enhanced for the main objective of energy efficient and optimal resource allocation using Vertex search algorithm with a gradient-based approximation. Initially, the resource allocation problem is modelled into a non-convex optimization problem. The power allocation, data rate adaptation, channel allocation, and user scheduling policies are optimized for maximization of the energy efficiency during data transmission. The proposed Vortex search algorithm resolves this optimization problem by determining the training interval for the channel estimation and power consumption. The experimental results prove that the proposed Vertex search based modified SMOR (VS-M-SMOR) model provides efficient routing with energy efficient optimal resource allocation.

In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot‐switching ratio. The optimal problem is then solved by using the time‐sharing strong duality theorem and Lagrange dual method. It is found that with the proposed optimal resource allocation scheme, the receiver should perform EH in the region of medium signal‐to‐noise ratio (SNR), whereas switching to information decoding (ID) is performed when the SNR is larger or smaller. The proposed resource allocation scheme is compared with the traditional time switching (TS) resource allocation scheme for the SWIPT systems with the nonlinear EH model. Numerical results show that the proposed resource allocation scheme significantly improves the system performance in energy efficiency.

Resource allocation is a fundamental problem in the design of wireless communication systems. The mutual interference among users, which is a major factor of limiting the performance of communication systems,can be efciently mitigated by carefully allocating wireless resources such as transmission power, transmission waveforms, and frequency bands. Current mathematical theories and methods for the design of communication systems lag behind the rapid development of communication technologies, and sometimes infuence their developments and applications. Most of the resource allocation problems arising from wireless communications boil down to non-convex nonlinear constrained optimization problems with special structures. On one hand, these optimization problems are often non-convex and highly nonlinear, and thus are difcult to solve; on the other hand,these problems have their own special structures such as(hidden) convexity and separability. This paper focuses on the optimal physical layer resource allocation problems for the multi-user interference channel, especially on characterizing the computational complexity of these problems, and designing efcient algorithms(satisfying some practical requirements like distributed implementation) for these problems by the use of their special structures.

In a companion paper (see ?Resource Allocation for Downlink Cellular OFDMA Systems-Part I: Optimal Allocation,? IEEE Trans. Signal Process., vol. 58, no. 2, pp. 720-734, Feb. 2010), we characterized the optimal resource allocation in terms of power control and subcarrier assignment, for a downlink sectorized OFDMA system impaired by multicell interference. In our model, the network is assumed to be one dimensional (linear) for the sake of analysis. We also assume that a certain part of the available bandwidth is likely to be reused by different base stations while that the other part of the bandwidth is shared in an orthogonal way between these base stations. The optimal resource allocation characterized in Part I is obtained by minimizing the total power spent by the network under the constraint that all users' rate requirements are satisfied. It is worth noting that when optimal resource allocation is used, any user receives data either in the reused bandwidth or in the protected bandwidth, but not in both (except for at most one pivot-user in each cell). We also proposed an algorithm that determines the optimal values of users' resource allocation parameters. As a matter of fact, the optimal allocation algorithm proposed in Part I requires a large number of operations. In the present paper, we propose a distributed practical resource allocation algorithm with low complexity. We study the asymptotic behavior of both this simplified resource allocation algorithm and the optimal resource allocation algorithm of Part I as the number of users in each cell tends to infinity. Our analysis allows to prove that the proposed simplified algorithm is asymptotically optimal, i.e., it achieves the same asymptotic transmit power as the optimal algorithm as the number of users in each cell tends to infinity. As a byproduct of our analysis, we characterize the optimal value of the frequency reuse factor. Simulations sustain our claims and show that substantial performance improvements are obtained when the optimal value of the frequency reuse factor is used.

Multi-access for multi-mode terminals is possible in heterogeneous networks environment which becomes a critical issue for transmission in parallel with meeting user quality of service (QoS) performance requirements and throughput maximization. The previous optimal resource allocation schemes, which do not consider service type, may allocate scarce radio resources inefficiently. To solve this, we propose an optimal resource allocation algorithm with distinguishing the services traffic into two classes: Delay-Constraint (DC) and Best-Effort (BE). In our work, we formulate a mathematical optimal model to support such heterogeneous service requirements in multi-radio access scenario. Then, we develop a optimal radio resource allocation algorithm that achieves the goal of maximizing the total system throughput in heterogeneous networks, while efficiently satisfying the QoS requirement for DC services traffic and fairness for BE services traffic. Simulation results show that the proposed service traffic differentiation based radio resource allocation algorithm significantly outperforms other existing schemes.

Plant Resource Allocation, edited by F.A. Bazzaz and J. Grace

The design, analysis and optimization of cooperative/relaying communication systems have recently become a very active research area within both the information theory as well as communications engineering societies. It is now well understood that relaying strategies can improve the coverage of wireless networks by providing higher data rates or better transmission reliability to user terminals at the edge of a wireless cell, or terminals having faded connectivity with the base station. Relaying technologies are also becoming part of the telecommunication standards. Although we can find studies, in the academic literature, on advanced relaying schemes, which are based on user terminals cooperating to help each other while applying decentralized resource allocation strategies, the first actual deployment step which will take place within the 3GPP long-term evolution (LTE)-advanced standard is based on fixed access points to do the relaying and within a centralized scheme in which the e-nodeB (base station with backhaul connection) takes the scheduling and resource allocation decisions. One major objective in 3GPP evolution is to utilize the scarce wireless system resources efficiently because achieving the high quality of service (QoS) targets through over-provisioning is uneconomical due to the relatively high cost for transmission capacity in cellular access networks. Our objective here is to obtain the optimal (from an information theory perspective) resource allocation schemes taking into considerations the system constraints that are relevant to the LTE-advanced standard. We have been able to derive optimal resource allocation polices that are provably based on closed-form formulations which are practical for implementation. They include the policies for (i) transmission mode selection (i.e. deciding whether the user needs the assistance of a relay or not), (ii) power allocation for the base station and the relays, and (iii) criterion for user scheduling over the available air-link resource units. Simulation results demonstrate that our proposed resource allocation scheme provides considerable throughput gains especially for users receiving low power through the direct link with the base station.

Hybrid metaheuristic technique for optimal container resource allocation in cloud

We study resource allocation algorithm design for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) downlink network with hybrid energy harvesting base station (BS). Specifically, an energy harvester and a constant energy source driven by a non-renewable resource are used for supplying the energy required for system operation. We first consider a deterministic offline system setting. In particular, assuming availability of non-causal knowledge about energy arrivals and channel gains, an offline resource allocation problem is formulated as a non-convex optimization problem over a finite horizon taking into account the circuit energy consumption, a finite energy storage capacity, and a minimum required data rate. We transform this non-convex optimization problem into a convex optimization problem by applying time-sharing and exploiting the properties of non-linear fractional programming which results in an efficient asymptotically optimal offline iterative resource allocation algorithm for a sufficiently large number of subcarriers. In each iteration, the transformed problem is solved by using Lagrange dual decomposition. The obtained resource allocation policy maximizes the weighted energy efficiency of data transmission (weighted bit/Joule delivered to the receiver). Subsequently, we focus on online algorithm design. A conventional stochastic dynamic programming approach is employed to obtain the optimal online resource allocation algorithm which entails a prohibitively high complexity. To strike a balance between system performance and computational complexity, we propose a low complexity suboptimal online iterative algorithm which is motivated by the offline algorithm. Simulation results illustrate that the proposed suboptimal online iterative resource allocation algorithm does not only converge in a small number of iterations, but also achieves a close-to-optimal system energy efficiency by utilizing only causal channel state and energy arrival information.

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.

Optimal cross-layer resource allocation in fog computing: A market-based framework

Cloud data centres, which are characteristic of dynamic workloads, if not optimized for energy consumption, may lead to increased heat dissipation and eventually impact the environment adversely. Consequently, optimizing the usage of energy has become a hard requirement in today's cloud data centres wherein the major part of energy consumption is mostly attributed to computing and cooling systems. Motivated by which this paper proposes an online algorithm for dynamic resource allocation, namely, temperature aware online dynamic resource allocation algorithm (TARA). TARA demonstrates a novel algorithm design to adapt dynamic resource allocation based on the temperature of a data centre using computational fluid dynamics (CFD). Also, TARA demonstrates a new dynamic resource reclaim strategy for making efficient resource allocations leading to efficient energy consumptions in dynamic environments. The proposed algorithm provides optimal resource allocation considering energy efficiency without being overwhelmed by online dynamic workloads. The optimal energy-efficient dynamic resource allocation for online workloads eventually optimizes the computing and cooling energy consumption. We show through theoretical analysis the correctness, efficiency and optimality bounds given as $TARA(P) \leq 2OPT(P)$, relative to the optimal solution provided by offline dynamic resource allocation algorithm $(OPT(P))$. We show through empirical analysis that the proposed method is efficient and significantly saves energy by 26\% when the data centre utilization is 100\% compared to batched reclaim. The performance analysis shows significant improvement in optimizing computing and cooling efficiency. TARA can be used in multiple areas of on-demand dynamic resource allocation in cloud computing like resource allocation for virtual machine creation, resource allocation for virtual machine migrations, and virtual resources assignment for elastic cloud applications.