服务水平协议感知的云中心利润最大化资源配置

针对云计算中心优化资源配置以实现利润最大化的问题,提出基于服务水平协议(SLA)感知的云中心服务器数量和运行速率优化的分析模型,同时考虑了云中心能耗成本、服务器租用成本、用户等待时间、SLA违规惩罚等重要因素。通过数值仿真分析了服务器数量、运行速率变化对云中心利润的影响。数值仿真结果表明:在请求速率固定时,通过优化服务器数量、运行速率可使云中心获得最大利润;而随着请求速率的增加,优化服务器数量和运行速率将会使云中心利润呈线性增长。分析结果可为云服务提供商获得更好的收益提供参考方法。     

Economic production quantity for profit maximization

In certain circumstances a relationship between unit contribution and producing output may exist in the classical economic production quantity (EPQ) production. We discuss an EPQ model with unit contribution dependent on production output and formulate the problem as signomial geometric problem (SPG) We show that a closed form optimal solution is readily obtainable for surch an SGP problem. We also illustrate that it is simple and easy to perform certain types of post-optimality sensitivity analysis on the optimal solution. An example is worked out in detail to demonstrate the viability of using geometric programming as an efficient solution method for dealing with a certain class of inventory optimization problems.     

Identifying valuable customers on social networking sites for profit maximization

With the tremendous popularity of social networking sites (SNS) in this era of Web 2.0, enterprises have begun to explore the feasibility of using SNS as platforms to conduct targeted marking and reputation management. Given huge number of users on SNS, how to choose appropriate users as the targets is the key for enterprises to conduct cost-effective targeted marketing and reputation management on SNS. This paper introduces a novel model for effectively identifying the most valuable users from SNS. Furthermore, we propose to use an optimization technique named semidefinite programming (SDP) to identify the most valuable customers that can generate the maximum of total profit. Our empirical evaluation on a real data set extracted from a popular SNS shows that the proposed model achieves much higher profits than benchmark methods. This study opens doors to more effective targeted marketing and reputation management on SNS.     

Dynamic proof of retrievability based on public auditing for coded secure cloud storage

Multimedia Tools and Applications - Cloud storage represents a cloud delivered-service model which draws the attention of organizations and individuals due to its uncounted attractive advantages....     

Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization

Adequate allocation of buffers in transfer lines is crucial to the optimization of line throughput and work in process (WIP) inventory. Their optimal allocation is subject to specific constraints, associated costs, and revenue projections. In this paper, we implement a combined artificial immune system optimization algorithm in conjunction with a decomposition method to optimally allocate buffers in transfer lines. The aim of the buffer allocation problem (BAP) is to achieve optimal system performance under buffers space constraints. Maximizing line throughput does not necessarily achieve maximum profit. In this study the immune decomposition algorithm (IDA) is used to determine optimal buffer allocation for maximum line throughput and maximum line economic profit.Results of extensive series of tests carried out to compare, in production lines with different characteristics, the performances of the proposed method and those of other algorithms are presented.     

A deep reinforcement learning framework for continuous intraday market bidding

Machine Learning - The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In...     

SLA negotiation and enforcement policies for revenue maximization and client classification in cloud providers

In Cloud Computing markets, owners of computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) to be provided as well as the economic conditions are established in a Service-Level Agreement (SLA). The common objective of a Cloud provider is to maximize its economic profit. However, there are situations in which providers must differentiate the SLAs with respect to the type of client that is willing to access the resources or the agreed QoS, e.g. when the hardware resources are shared between users of the company that own the resources and external users.This article proposes two sets of policies to manage SLAs with respect to the business objectives of a Cloud provider: Revenue Maximization or classification of clients. The criterion to classify clients is established according to the relationship between client and provider (external user, internal or another privileged relationship) and the QoS that the client purchases (cheap contracts or extra QoS by paying an extra fee). The validity of the policies is demonstrated through exhaustive experiments.     

Evolving cooperative bidding strategies in a power market

This paper presents an evolutionary algorithm to generate cooperative strategies for individual buyers in a competitive power market. The paper explores how buyers can lower their costs by using an evolutionary algorithm that evolves their group sizes and memberships. The evolutionary process uncovers interesting agent behaviors and strategies for collaboration. The developed agent-based model uses PowerWorld simulator to incorporate the traditional physical system characteristics and constraints while evaluating individual agent’s behavior, actions and reactions on market dynamics. Simulation results on IEEE 14-bus system show that the evolutionary approach evolves mutually beneficial strategies that enhance the buyer’s profitability. The buyers learn to achieve substantial cost savings by forming groups and adjusting their demand curves, without sacrificing much in desired power consumption.     

电力市场中发电企业竞价行为的RePast仿真

不完全信息,寡头垄断的电力市场中,发电企业竞价上网,面临的关键是如何采用最优的申报策略以获得最大的发电收益。基于多主体的建模思路和方法,将复杂适应系统理论与博弈理论相结合,利用RePast平台对发电企业的竞价过程进行了建模仿真研究。模型考虑了发电企业群体成员的自学习性及个体之间的交互行为。仿真过程中,各参与主体在各次竞价中积累知识,不断修正各自的预测函数,以达到最优收益。仿真结果验证了该方法的有效性。     

Privacy-preserving public auditing for educational multimedia data in cloud computing

Nowadays, as distance learning is being widly used, multimedia data becomes an effective way for delivering educational contents in online educational systems. To handle the educational multimedia data efficiently, many distance learning systems adopt a cloud storage service. Cloud computing and storage services provide a secure and reliable access to the outsourced educational multimedia contents for users. However, it brings challenging security issues in terms of data confidentiality and integrity. The straightforward way for the integrity check is to make the user download the entire data for verifying them. But, it is inefficient due to the large size of educational multimedia data in the cloud. Recently many integrity auditing protocols have been proposed, but most of them do not consider the data privacy for the cloud service provider. Additionally, the previous schemes suffer from dynamic management of outsourced data. In this paper, we propose a public auditing protocol for educational multimedia data outsourced in the cloud storage. By using random values and a homomorphic hash function, our proposed protocol ensures data privacy for the cloud and the third party auditor (TPA). Also, it is secure against lose attack and temper attack. Moreover, our protocol is able to support fully dynamic auditing. Security and performance analysis results show that the proposed scheme is secure while guaranteeing minimum extra computation costs.     

An ICA based approach for solving profit based unit commitment problem market

With the advent of paralleling and implementation of restructuring in the power market, some routine rules and patterns of traditional market should be accomplished in a way different from the past. To this end, the unit commitment (UC) scheduling that has once been aimed at minimizing operating costs in an integrated power market, is metamorphosed to profit based unit commitment (PBUC) by adopting a new schema, in which generation companies (GENCOs) have a common tendency to maximize their own profit. In this paper, a novel optimization technique called imperialist competitive algorithm (ICA) as well as an improved version of this evolutionary algorithm are employed for solving the PBUC problem. Moreover, traditional binary approach of coding of initial solutions is replaced with an improved integer based coding method in order to reduce computational complexity and subsequently ameliorate convergence procedure of the proposed method. Then, a sub-ICA algorithm is proposed to obtain optimal generation power of thermal units. Simulation results validate effectiveness and applicability of the proposed method on two scenarios: (a) a set of unimodal and multimodal standard benchmark functions, (b) two GENCOs consist of 10 and 100 generating units.     

云计算架构下的动态副本管理策略

为有效管理云计算环境中的数据副本,减少系统带宽消耗、最小化响应时间和平衡负载,提出了一种动态副本管理策略.通过建立文件可用性和副本数量间的关系模型来计算系统应维持的最小副本数量;基于数据被请求访问的次数和传输花费进行副本放置;数据被请求时结合节点间带宽和节点效用选择副本.实验结果表明了该策略的正确性有效性.     

Dynamic searchable symmetric encryption for storing geospatial data in the cloud

International Journal of Information Security - We present a dynamic searchable symmetric encryption scheme allowing users to securely store geospatial data in the cloud. Geospatial data sets often...     

省级电子招标投标公共服务平台的功能结构探讨

为了按照国家发改委等八部委联合颁布的《电子招标投标办法》要求建立省级电子招标投标公共服务平台,研究探讨了平台功能的规划和该服务平台模块的设置,以适应和满足全省电子招标投标活动需求。从平台建设的必要性、平台功能规划、平台模块设置等方面进行了探讨,以此为省级电子招标投标公共服务平台的建设提供参考。     

面向公有云的数据完整性公开审计方案

针对云数据完整性公开审计中隐私泄漏给第三方审计者（TPA）以及云存储服务器（CSS）发起替代攻击的问题,提出一种面向公有云的数据完整性公开审计方案。该方案首先利用哈希值混淆方法,模糊化云存储服务器返回的证据,以防止TPA分析证据计算出原始数据;然后,在审计过程中,由TPA自行计算出文件Merkle哈希树（MHT）对应挑战请求所选数据块的覆盖树,并与CSS返回的覆盖树作结构匹配,以防止云存储服务器用其他已有数据响应审计挑战。实验结果表明,该方案解决了现有方案隐私问题及攻击问题后,在计算开销、存储开销和通信开销方面的性能不会有数量级变化。     

混合云市场的计算资源交易模型

基于计算资源共享模型的研究,提出了混合云计算资源市场HCRM（Hybrid Cloud Resource Market）的功能架构,研究了市场管理层的交易管理机制,给出了计算资源的供需质量模型,设计了一种质量感知的双向拍卖算法QaDA（Quality-aware Double Auction）。仿真运行结果表明,与普通的连续双向拍卖算法CDA（Continuous Double Auction）相比,QaDA不仅可以引导用户合理定价,也能获得较高的匹配成功率和较高的交易总额。     

Utilizing unreliable public resources for higher profit and better SLA compliance in computing utilities

Computing utilities are emerging as an important part of the infrastructure for outsourcing computer services. Fundamental to outsourcing is the notion of quality of service, which is defined by service level agreements (SLAs) between the computing utilities and clients. One of the major objectives of computing utilities is to maximize their net profit while maintaining customer loyalty. To achieve this objective, the computing utilities should meet or exceed their SLA constraints most of the time. Defining the SLAs conservatively might be one way of easily achieving these goals. However, by tuning the SLA parameters conservatively the computing utility might under utilize its resources with a resultant loss of revenue. Therefore, we can see two main issues with SLA management: designing SLAs competitively so that expected revenue for the computing utility is maximized and maintaining the operating conditions such that SLAs are satisfied with very high probability. In this paper, we show that inducting unreliable public resources into a computing utility enables more competitive SLAs while maintaining higher levels of run time compliances as well as maximizing profit. Our scheduling algorithms assume that idle cycles from public resources are available in plenty, therefore, the performance gains do not incur any additional financial cost. However, there is communication overhead when public resources from a wide area network is included. This overhead is kept to the minimum by enabling the scheduler work without any monitoring on the public resources.     

Multi-tenant intrusion detection system for public cloud (MTIDS)

Cloud computing is an innovative paradigm technology that is known for its versatility. It provides many creative services as requested, and it is both cost efficient and reliable. More specifically, cloud computing provides an opportunity for tenants to reduce cost and raise effectiveness by offering an alternative method of service utilization. Although these services are easily provided to tenants on demand with minor infrastructure investment, they are significantly exposed to intrusion attempts since the services are offered under the administration of diverse supervision over the Internet. Moreover, the security mechanisms offered by cloud providers do not take into consideration the variation of tenants’ needs as they provide the same security mechanism for all tenants. So, meeting tenants’ security requirements are still a major challenge for cloud providers. In this paper, we concentrate on the security service offered to cloud tenants and service providers and their infrastructure to restrain intruders. We intend to provide a flexible, on-demand, scalable, and pay-as-you-go multi-tenant intrusion detection system as a service that targets the security of the public cloud. Further, it is designed to deliver appropriate and optimized security taking into consideration the tenants’ needs in terms of security service requirements and budget.