Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarm

A Reliability-Aware Approach for Resource Efficient Virtual Network Function Deployment

OAPA Network function virtualization (NFV) is a promising technique aimed at reducing capital expenditures (CAPEX) and operating expenditures (OPEX), and improving the flexibility and scalability of an entire network. In contrast to traditional dispatching, NFV can separate network functions from proprietary infrastructure and gather these functions into a resource pool that can efficiently modify and adjust service function chains (SFCs). However, this emerging technique has some challenges. A major problem is reliability, which involves ensuring the availability of deployed SFCs, namely, the probability of successfully chaining a series of virtual network functions (VNFs) while considering both the feasibility and the specific requirements of clients, because the substrate network remains vulnerable to earthquakes, floods and other natural disasters. Based on the premise of users & #x2019; demands for SFC requirements, we present an Ensure Reliability Cost Saving (ER & #x005F;CS) algorithm to reduce the CAPEX and OPEX of telecommunication service providers (TSPs) by reducing the reliability of the SFC deployments. The results of extensive experiments indicate that the proposed algorithms perform efficiently in terms of the blocking ratio, resource consumption, time consumption and the first block

Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization

© 2014 IEEE. Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing

Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm

Live Migration for Multiple Correlated Virtual Machines in Cloud-based Data Centers

With the development of cloud computing, virtual machine migration is emerging as a promising technique to save energy, enhance resource utilizations, and guarantee Quality of Service (QoS) in cloud datacenters. Most of existing studies on the virtual machine migration, however are based on a single virtual machine migration. Although there are some researches on multiple virtual machines migration, the author usually does not consider the correlation among these virtual machines. In practice, in order to save energy and maintain system performance, cloud providers usually need to migrate multiple correlated virtual machines or migrate the entire virtual datacenter (VDC) request. In this paper, we focus on the efficient online live migration of multiple correlated VMs in VDC requests, for optimizing the migration performance. To solve this problem, we propose an efficient VDC migration algorithm (VDC-M). We use the US-wide NSF network as substrate network to conduct extensive simulation experiments. Simulation results show that the performance of the proposed algorithm is promising in terms of the total VDC remapping cost, the blocking ratio, the average migration time and the average downtime.This research is partially supported by the National Grand Fundamental Research 973 Program of China under grant (2013CB329103), Natural Science Foundation of China grant (61271171, 61571098), China Postdoctoral Science Foundation (2015M570778), the Fundamental Research Funds for the Central Universities (ZYGX2013J002), Guangdong Science and Technology Project (2012B090400031, 2012B090500003, 2012B091000163), and National Development and Reform Commission Project

Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis