Iterative Proximal Algorithm Research Articles

Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above problems, in this paper, the task offloading problem is formulated as an optimization problem, and Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared to recent advanced strategies, namely, Ant Colony Optimization (ACO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG), Deep Meta Reinforcement Learning-based Offloading (DRMO), Iterative Proximal Algorithm (IPA), and Parallel Random Forest (PRF).

A Many-to-Many Demand and Response Hybrid Game Method for Cloud Environments

In this article, we design a service mechanism for profits optimization between multiple cloud providers and multiple cloud customers ( <i>many-to-many</i> ). We explore this problem from the perspective of game theory but take a different approach compared with existing cloud resource pricing game methods. First, we regard the relationships among multiple cloud customers as an evolutionary game, and formulate the competitions among the multiple cloud providers as a noncooperative game. Eventually, we form a hybrid game model in which the strategy of each customer and each cloud provider is affected not only by the other side but also by customers or cloud providers other than themselves. Second, based on the hybrid game model, we simulate the bargaining process between cloud providers and customers by controlling supply and demand allocation, and try to ultimately achieve a balanced supply and demand state, i.e., a win-win situation. For each cloud customer and provider, we design a utility function. A customer’s utility involves net profits and the cloud providers’ bidding strategies, and a cloud provider’s utility involves net profits and the cloud customers’ demand strategies. Both sides attempt to maximize their own profits under the influences of each other. We prove that our proposed strategies enable each of the two games to converge to their own equilibrium. Finally, the strategies of cloud customers and providers can be implemented through an iterative proximal algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {IPA}$</tex-math></inline-formula> ) and a distributed iterative algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {DIA}$</tex-math></inline-formula> ). The experimental results validate our methods and show that the proposed method can benefit both multiple cloud providers and customers.

A Game Approach to Multi-Servers Load Balancing with Load-Dependent Server Availability Consideration

In this paper, we focus on request migration strategies among multi-servers for load balancing. Different from the general load balancing problem, we consider it under a distributed, non-cooperative, and competitive environment. Due to the mentioned characteristics, we view our problem from a game theoretic perspective and formulate it into a non-cooperative game among the multiple servers, in which each server is informed with incomplete information of other servers. For each server, we define its expected response time as a disutility function and try to minimize its value. We also take into account server availability, which impacts the processing capacity of a server and thus its disutility. We solve the problem by employing variational inequality (VI) theory and prove that there exists a Nash equilibrium solution set for the formulated game. Then, we propose an iterative proximal algorithm (IPA) to compute a Nash equilibrium solution. The convergence of the IPA algorithm is also analyzed and we find that it converges to a Nash equilibrium. Finally, we conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our proposed IPA algorithm converges to a Nash equilibrium very quickly and significantly decreases the disutilities of all servers by configuring a proper request migration strategy.

Graphical-model based high dimensional generalized linear models

We consider the problem of both prediction and model selection in high dimensional generalized linear models. Predictive performance can be improved by leveraging structure information among predictors. In this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph structure among the predictors. The graphic information among predictors is incorporated node-by-node using a decomposed representation and the sparsity is encouraged both within and between the decomposed components. We propose an efficient iterative proximal algorithm to solve the optimization problem. Statistical convergence rates and selection consistency for the doubly sparse regularized estimator are established in the ultra-high dimensional setting. Specifically, we allow the dimensionality grows exponentially with the sample size. We compare the estimator with existing methods through numerical analysis on both simulation study and a microbiome data analysis.

RETRACTED ARTICLE: Meta-heuristic firefly approach to multi-servers load balancing with independent and dependent server availability consideration

Load balancing is the foremost confront in a cloud environment. Load balancing is assisted to disseminate dynamic workload across many nodes to guarantee that not a single node gets overloaded. In the existing work Iterative Proximal Algorithm is introduced for the load balancing. This current work concentrates on request migration criteria between multiple servers for load balancing. It varies from common load balancing crisis, assume that it is under a disseminated, competitive environment, and is non-cooperative. For every server, its projected response time is taken to be a dis-utility function and its value is reduced. But load balancing with dependent and independent servers is a confronting errand. In order to resolve this challenge, Meta-heuristic scheme is carried out based on firefly algorithm to balance load on multiple servers. The main contribution of this research work is to perform the load balancing to improve the computational efficiency of the task submitted by the users. The anticipated multi-server load balancing is carried out based on dependent and independent tasks. The jobs comprise of various interdependent errands in which independent tasks might be processed in multiple cores of the VM or multiple VMs. The errands come through the server’s run-time in arbitrary time intervals for different loads. This crisis is resolved by using variation inequality (VI) theory and confirming that there prevails Nash equilibrium resolution set for the devised game. After this, a Nash equilibrium resolution for multi-server load balancing is calculated by using an Iterative Proximal algorithm (IP) is anticipated with Meta heuristic Firefly Optimization Algorithm. Convergence of IPA algorithm is analyzed so that it gets converged to Nash equilibrium. At last, many numerical computations are performed to confirm theoretical analysis.

A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

In this letter, we consider the direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling in application to uniform linear arrays (ULAs). A novel method is proposed that treats the DOA estimation as a block sparse signal reconstruction problem with a modified array manifold matrix which utilizes the information of entire array output. A block smoothed $\ell _{0}$ norm approximation technique is introduced to solve the problem. Finally, an iterative proximal algorithm is proposed. Simulation results are presented that show the superiority of the proposed method against other known techniques.

Game theory-based optimization of distributed idle computing resources in cloud environments

With the rapid cloud technological advancement and economic growth, more and more organizations have purchased cloud resources for daily business operations besides building their own IT infrastructures. Thus, it is very important to understand the economy of cloud computing. In this paper, we mainly focus on examining the private idle computing resources owned by various organizations who are willing to form a network of ad hoc cloud provider and sell the services to cloud users. In such a case, the organizations cannot only meet their own demands, but also sell their idle computing resources in the form of ad hoc cloud. Naturally, the organizations, as provider, aim at maximizing their own profit through adjusting business costs and sale prices. Due to the uncertainty of the amount of idle computing resources, dynamic pricing is challenging. We approach the problem from the perspective of game theory and formulate it as a non-cooperative game among multiple organizations, i.e., the game player. For each player, a utility function is used to represent its profits. The players choose request strategies and sales service strategies to maximize the utility function. This paper has proved that there exists Nash equilibrium for this game problem. We proposed an iterative proximal algorithm (IPA) for calculating the Nash equilibrium. After analyzing the convergence of the IPA, we found that the algorithm converges to the Nash equilibrium solution when reasonable conditions are satisfied and conforms to the theoretical proof. Experimental results show that our proposed algorithm can quickly converge to a stable state, and by calculating the appropriate service (resource) request strategies and selling service strategies for all organizations, organizations' profit are increased compared to without IPA algorithm.

Request Balancing Among Users in Multiple Autonomous Cloud Provider Environments

As an increasing number of providers are offering cloud service reservations, an increasingly competitive market has emerged. While most of these providers charge users for metered usage based on fixed prices, they cannot make a timely response to demand changes and may risk losing users. Hence, we highlight the importance of an autonomous provider who self-adjusts the price according to the latest demands of the cloud market. Extensive research has shown that this is not a problem for a single provider. However, when it comes to multiple providers, the main challenge is coordinating the interactions between users and users, providers and providers, as well as users and providers. In this paper, we focus on request balancing among users in the multiple-autonomous-provider environment. Since the request strategy and payment of each user are affected by decisions of other users, we take game theory to address this problem and formulate it into a noncooperative game among multiple users, without sharing specific strategies. Each user tries to maximize its utility, which combines the net profit with time efficiency. The variational inequality theory is employed and proved that there exists a Nash equilibrium solution set for the game. Then, an iterative proximal algorithm is proposed to find the Nash equilibrium solution, and its convergence is proven. Simulation results demonstrate that the proposed scheme makes requests balanced and increases users' utilities, and also shifts demands from peak to off-peak periods.

Strategy Configurations of Multiple Users Competition for Cloud Service Reservation

In this paper, we focus on strategy configurations of multiple users to make cloud service reservation. We consider the problem from a game theoretic perspective and formulate it into a non-cooperative game among the multiple cloud users, in which each user is informed with incomplete information of other users. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value. We solve the problem by employing variational inequality (VI) theory and prove that there exists a Nash equilibrium solution set for the formulated game. Then, we propose an iterative proximal algorithm (IPA), which is designed to compute a Nash equilibrium solution. The convergence of the IPA algorithm is also analyzed and we find that it converges to a Nash equilibrium if several conditions are satisfied. Finally, we conduct some numerical calculations to verify our theoretical analysis. The experimental results show that our proposed IPA algorithm converges to a stable state very quickly and improves the utilities of all users to certain extent by configuring a proper request strategy.