A universal fairness evaluation framework for resource allocation in cloud computing

Media streaming applications have recently attracted large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is difficult to provide streaming distribution with guaranteed QoS relying only on central resources at the content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., VoD provider) can use to obtain resources on-demand. Since a media content provider is charged for amount of resources (bandwidth) rented from the cloud, an open problem is to decide on the right amount of resources allocated in the cloud and their reservation time such that the financial cost on the content provider is minimized. We consider a practical pricing model that is based on a non-linear tariff (i.e., a pricing scheme that depends non-linearly on the resources purchased or time reserved). We formulate the optimization problem based on prediction of future streaming demand. We then propose a simple (easy to implement) algorithm for resource allocation that exploits the non-linearity in the price contract, while ensuring that sufficient resources is reserved in the cloud without incurring wastage. The results of our numerical evaluation and simulations show that the proposed algorithm mimics the optimum solution very well.

With the advancement of information technology need to perform computing tasks everywhere and all the time there, in cloud computing environments and heterogeneous users have access to different sources with different characteristics that these resources are geographically in different areas. Due to this, the allocation of resources in cloud computing comes to the main issue is considered a major challenge to achieve high performance. Due to the nature of cloud computing is a distributed system to account, comes to business, economic methods such as auctions are used to allocate resources for decentralization. As an important economic bilateral hybrid auction model is the perfect solution for the allocation of resources in cloud computing, on the other hand, providers of cloud resources similarly, their sources of supply combined addressing. One of the problems auction two-way combination with maximum benefit for the parties to the transaction is the efficient allocation of resources to the problem of determining an auction winner is known. Given that the winning auction is NP-Hard. It results in a problem, several methods have been proposed to solve it. In this dissertation, taking into account the strength simulated annealing algorithm, a modified version of it is proposed for solving the winner determination in combinatorial double auction problem in cloud computing. The proposed approach is simulated along with genetic and simulated annealing algorithms and the results show that the proposed approach finds better solutions than the two mentioned algorithms.

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.

The applications that are being installed and run on the mobile devices need a platform of services offered on-demand and be reliable. These services are promised by cloud computing, where resource allocation plays a major role in providing the services and delivering the quality of service (QoS) to the cloud users. The task of provisioning the resources to the cloud users becomes challenging considering the proliferation in the number of users using the cloud on every day basis. The users in this context of proliferation can be considered as mobile nodes that need faster access to communicate among different devices and services. In this paper, the resource allocation on cloud is considered as a game between the clients or the users of the cloud submitting their tasks onto the cloud. Further, a utility maximisation approach as a game theoretic model for an efficient resource allocation in cloud computing is explored in this paper.

Group technology-based model and cuckoo optimization algorithm for resource allocation in cloud computing

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nt Colony, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> hale, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ine-Cosine, and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> enry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

In diverse parallel and distributed computing systems, resource allocation is the progression of distributing consumer tasks for processing elements during execution in which some performance intentions are optimised. This research explains about the innovative resource allocation algorithm for the computing grid environment. In the scheduling problem of independent task in cloud computing, summarise other scheduling algorithms introduce a modified fuzzy C-means (MFCM) clustering algorithm. Our algorithm abstract resource into a model to analyse these characteristics of resources with the MFCM algorithm. From that our proposed technique could decrease a execution time and memory space allocation of the system. For the optimal selection of virtual machines hybrid whale genetic (HWGA) optimisation algorithm is used. Since the virtual machines are optimally selected on the basis of feature values, our proposed method provides reduced load balancing as well as improved parallel execution of tasks.

Although cloud computing offers many benefits, such as flexibility, scalability, and profitability, some small and medium enterprises (SMEs) are still unable to fully utilize cloud resources, such as memory, computing power, storage, and network bandwidth. This reduces their productivity and increases their expenses. Therefore, the central objective of this paper was to examine the key challenges related to the allocation of cloud computing resources in small and medium enterprises. The method used for this study is based upon qualitative research using 12 interviews with 12 owners, managers, and experts in cloud computing in four countries: the United States of America, the United Kingdom, India, and Pakistan. Our results, based on our empirical data, show 11 key barriers to resource allocation in cloud computing that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this research contributes to the body of knowledge concerning cloud computing technology and offers valuable understanding of the cloud computing resource allocation approaches employed by small and medium enterprises (SMEs). In practice, this research is useful to aid SMEs in implementing successful and sustainable strategies for allocating cloud computing resources.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

It is proposed that resource allocation algorithm considers rate proportion among the multicast groups in multicast orthogonal frequency division multiplexing (OFDM) downlink systems in this paper. However, it is minor significance to enforce the rate proportion strictly in actual mobile communication system. To solve this problem, the resource allocation algorithm is proposed to satisfy the rate proportion approximately in this text, including sub carrier allocation algorithm and power allocation algorithm. The former is achieved by modifying the existing unicast sub carrier assignment algorithm, the latter is Water-filling algorithm, which is capable of maximize the capacity when sub carrier assignment is determined. Simulation results show that compared to conventional algorithms, the proposed resource allocation algorithm has a good performance in capacity as well as rate proportion, which is a realistic method to solve the resource allocation in actual mobile communication system. In addition, we also discussed the capacity has a close relation with the number of multicast group.

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.