Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn Distributed Algorithms for Cost-Efficient Data Center Placement in Cloud Computing
74
0
0.0
(
0
)
Added by
Wee Peng Tay
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The increasing popularity of cloud computing has resulted in a proliferation of data centers. Effective placement of data centers improves network performance and minimizes clients perceived latency. The problem of determining the optimal placement of data centers in a large network is a classical uncapacitated $k$-median problem. Traditional works have focused on centralized algorithms, which requires knowledge of the overall network topology and information about the customers service demands. Moreover, centralized algorithms are computationally expensive and do not scale well with the size of the network. We propose a fully distributed algorithm with linear complexity to optimize the locations of data centers. The proposed algorithm utilizes an iterative two-step optimization approach. Specifically, in each iteration, it first partitions the whole network into $k$ regions through a distributed partitioning algorithm; then within each region, it determines the local approximate optimal location through a distributed message-passing algorithm. When the underlying network is a tree topology, we show that the overall cost is monotonically decreasing between successive iterations and the proposed algorithm converges in a finite number of iterations. Extensive simulations on both synthetic and real Internet topologies show that the proposed algorithm achieves performance comparable with that of centralized algorithms that require global information and have higher computational complexity.
rate research
Read More
Recently, storage of huge volume of data into Cloud has become an effective trend in modern day Computing due to its dynamic nature. After storing, users deletes their original copy of the data files. Therefore users, cannot directly control over that data. This lack of control introduces security issues in Cloud data storage, one of the most important security issue is integrity of the remotely stored data. Here, we propose a Distributed Algorithmic approach to address this problem with publicly probabilistic verifiable scheme. Due to heavy workload at the Third Party Auditor side, we distributes the verification task among various SUBTPAs. We uses Sobol Random Sequences to generates the random block numbers that maintains the uniformity property. In addition, our method provides uniformity for each subtasks also. To makes each subtask uniform, we uses some analytical approach. For this uniformity, our protocols verify the integrity of the data very efficiently and quickly. Also, we provides special care about critical data by using Overlap Task Distribution Keys.
The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.
In this paper, we consider an Unmanned Aerial Vehicle (UAV)-assisted cellular system which consists of multiple UAV base stations (BSs) cooperating the terrestrial BSs. In such a heterogeneous network, for cellular operators, the problem is how to determine the appropriate number, locations, and altitudes of UAV-BSs to improve the system sumrate as well as satisfy the demands of arbitrarily flash crowds on data rates. We propose a data-driven 3D placement of UAV-BSs for providing an effective placement result with a feasible computational cost. The proposed algorithm searches for the appropriate number, location, coverage, and altitude of each UAV-BS in the serving area with the maximized system sumrate in polynomial time so as to guarantee the minimum data rate requirement of UE. The simulation results show that the proposed approach can improve system sumrate in comparison with the case without UAV-BSs.
Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects data with reduced latency, location awareness and dynamic computation. By displacing workloads from the central cloud to the edge devices, fog computing overcomes communication bottlenecks avoiding raw data transfer to the central cloud, thus paving the way for the next generation IoT-based applications. In this paper we study scheduling and placement of applications in fog computing, which is key to ensure profitability for the involved stakeholders. We consider a scenario where the emerging microservice architecture allows for the design of applications as cascades of coupled microservice modules. It results into a mixed integer non linear problem involving constraints on both application data flows and computation placement. Due to the complexity of the original problem, we resort to a simplified version, which is further solved using a greedy algorithm. This algorithm is the core placement logic of the FogAtlas platform, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed solution, showing it attains performance close to the optimal solution and, in our real implementation, it scales well with respect to the number of served applications.
In this letter, we study the on-demand UAV-BS placement problem for arbitrarily distributed users. This UAV-BS placement problem is modeled as a knapsack-like problem, which is NP-complete. We propose a density-aware placement algorithm to maximize the number of covered users subject to the constraint of the minimum required data rates per user. Simulations are conducted to evaluate the performance of the proposed algorithm in a real environment with different user densities. Our numerical results indicate that for various user densities our proposed solution can service more users with guaranteed data rates compared to the existing method, while reducing the transmit power by 29%.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
