SLA based resource allocation policies in autonomic environments

Nowadays, large service centers provide computational capacity to many customers by sharing a pool of IT resources. The service providers and their customers negotiate utility based Service Level Agreement (SLA) to determine the costs and penalties on the base of the achieved performance level. The system is often based on a multi-tier architecture to serve requests and autonomic techniques have been implemented to manage varying workload conditions. The service provider would like to maximize the SLA revenues, while minimizing its operating costs. The system we consider is based on a centralized network dispatcher which controls the allocation of applications to servers, the request volumes at various servers and the scheduling policy at each server. The dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a resource allocation scheduler for such multi-tier autonomic environments so as to maximize the profits associated with multiple class SLAs. The overall problem is NP-hard. We develop heuristic solutions by implementing a local-search algorithm. Experimental results are presented to demonstrate the benefits of our approach.     

Resource Allocation for Session-Based Two-Dimensional Service Differentiation on e-Commerce Servers

A scalable e-commerce server should be able to provide different levels of quality of service (QoS) to different types of requests based on clients' navigation patterns and the server capacity. E-Commerce workloads are composed of sessions. In this paper, we propose a session-based two-dimensional (2D) service differentiation model for online transactions: intersession and intrasession. The intersession model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. A primary performance metric of online transactions is slowdown. It measures the waiting time of a request relative to its service time. We present a processing rate allocation scheme for 2D proportional slowdown differentiation. We then introduce service slowdown as a systemwide QoS metric of an e-commerce server. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D service differentiation as an optimization of processing rate allocation with the objective of minimizing the service slowdown of the server. We prove that the derived rate allocation scheme based on the optimization guarantees client requests' slowdown to be square-root proportional to their prespecified differentiation weights in both intersession and intrasession dimensions. We evaluate this square-root proportional rate allocation scheme and a proportional rate allocation scheme via extensive simulations. Results validate that both schemes can achieve predictable, controllable, and fair 2D service differentiation on e-commerce servers. The square-root proportional rate allocation scheme provides 2D service differentiation at a minimum cost of service slowdown.     

Behavior-based reputation management in P2P file-sharing networks

Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior.     

E-ARL: An Economic incentive scheme for Adaptive Revenue-Load-based dynamic replication of data in Mobile-P2P networks

In mobile ad hoc peer-to-peer (M-P2P) networks, frequent network partitioning occurs due to peer movement or owing to peers switching ‘off’ their mobile devices. This leads to typically low data availability in M-P2P networks, thereby necessitating data replication. This work proposes E-ARL, which is a novel Economic scheme for Adaptive Revenue-Load-based dynamic replication of data in dedicated M-P2P networks with the aim of improving data availability. Thus, E-ARL considers a mobile cooperative environment, where the MPs are working towards the same goal, and the network performance is facilitated by the economic scheme. E-ARL essentially allocates replicas based on its economic scheme. Each data item has a price in virtual currency. E-ARL requires a query issuing peer to pay the price of its queried data item to the query-serving peer and a commission to relay peers in the successful query path. The main contributions of E-ARL follow. First, it uses an economic scheme for efficiently managing M-P2P resources in a context-aware manner by facilitating effective replica hosting and message relaying by peers. Second, it collaboratively performs bid-based replica allocation to facilitate better quality of service. Third, it incorporates both revenue-balancing and load-balancing to improve peer participation and performance. Fourth, it conserves the energy of low-energy MPs to facilitate network connectivity. Our performance evaluation shows that E-ARL is indeed effective in improving peer participation in M-P2P networks, thereby improving query response times, query success rates, query hop-counts and replica allocation traffic.     

Reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured P2P networks

Free riding is a major problem in peer-to-peer networks. Reputation management systems are generally employed to overcome this problem. In this paper, a new reputation based scheme called probabilistic resource allocation is proposed. This strategy probabilistically decide whether to provide the resource to requesting peer or not. Aforesaid method gives selection preference to higher reputation peers and at the same time provides some finite probability of interaction between those peers who don’t have good reputation about each other. This avoids disconnection between the aforesaid peers. The proposed scheme also introduces a new mechanism for resource distribution which not only allocates resources based on peers’ reputation but simultaneously maximizes network utility also. Algorithm for formation of interest groups based upon both similarity of interests and reputation between peers is also presented.     

Cooperative Media Data Streaming with Scalable Video Coding

Live peer-to-peer (P2P) streaming has become a promising approach for broadcasting non-interactive media content from a server to a large number of interested clients. However, it still faces many challenges such as high churn rate of peer clients, uplink bandwidth constraints of participating peers, and heterogeneity of client throuput capacities. This paper presents a new P2P network called LSONet, a collaborative peer-to-peer streaming framework for scalable layer-encoded bit streams. The contributions are the combination of the advantages of both layered conding and mesh-based packet exchange. With layered coding, it overcomes overlay bandwidth limitatioins and heterogeneity of client capacities. With mesh based overlay streaming, it can better handle peer churns, as compared to tree-based solutions. For achieving these targets, this paper employs a gossip-based data-driven scheme for partnership formation, and proposes two algorithms, optimized transmission policy (OTP) and graceful degradation scheme (GDS), for multi-layers allocation. The proposed system is completely self-organizing, and in a fully distributed fashion. Extensive simulations show that LSONet achieves higher quality of service by peer-assisted streaming and layered video coding. Also, through comparison, results show that the system outperforms some previous schemes in resource utilization and is more robust and resilient for nodes departure, which demonstrate that it is well-suited for quality adaptive live streaming applications.     

基于纳什议价的对等网络资源分配

针对对等(P2P)网络中普遍存在的自由下载问题,提出保证节点最小服务质量的一种基于纳什议价的资源分配方案。首先,建立保证节点最小服务质量的理论模型,分析表明合作博弈的节点议价权力与其最大贡献能力正相关,非合作博弈节点的议价权力与其最大贡献能力负相关,因此,合作节点比非合作节点获得更多的资源;其次,证明了合作博弈中节点的相对议价权力越大,节点获得的资源越多,收益越大,反之亦然。最后,通过仿真验证系统保证节点获得最小服务质量的前提下,合作节点获得的资源与节点的初始资源分配和纳什议价权力等因素相关;初始资源分配与节点的最大贡献能力呈正相关,并随着节点数目的增加而减少;议价权力随着节点数目的增加而下降,节点获得的资源随着节点议价权力的增加而增加。该方案与经典保证公平性的平均资源分配方案相比,合作节点能获得更多的资源。仿真结果验证了理论分析中在保证节点服务质量前提下,节点议价权力越大,获得的资源越多。     

Harmonic proportional bandwidth allocation and scheduling for service differentiation on streaming servers

To provide ubiquitous access to the proliferating rich media on the Internet, scalable streaming servers must be able to provide differentiated services to various client requests. Recent advances of transcoding technology make network-I/O bandwidth usages at the server communication ports controllable by request schedulers on the fly. In this article, we propose a transcoding-enabled bandwidth allocation scheme for service differentiation on streaming servers. It aims to deliver high bit rate streams to high priority request classes without overcompromising low priority request classes. We investigate the problem of providing differentiated streaming services at application level in two aspects: stream bandwidth allocation and request scheduling. We formulate the bandwidth allocation problem as an optimization of a harmonic utility function of the stream quality factors and derive the optimal streaming bit rates for requests of different classes under various server load conditions. We prove that the optimal allocation, referred to as harmonic proportional allocation, not only maximizes the system utility function, but also guarantees proportional fair sharing between classes with different prespecified differentiation weights. We evaluate the allocation scheme, in combination with two popular request scheduling approaches, via extensive simulations and compare it with an absolute differentiation strategy and a proportional-share strategy tailored from relative differentiation in networking. Simulation results show that the harmonic proportional allocation scheme can meet the objective of relative differentiation in both short and long timescales and greatly enhance the service availability and maintain low queueing delay when the streaming system is highly loaded.     

资源同时占有平均值分析方法的C/S系统应用

利用分层排队网络模型对具有资源同时占有的客户/服务器(Client/Server,C/S)系统建模,讨论并提出了一种改进的平均值分析方法———资源同时占有平均值分析方法用于计算响应时间、吞吐率、队列长度等系统性能参数均值。着重讨论该算法用于求解排队网络模型中具有不同选道行为、对各服务结点有不同服务需求的多类顾客的情况。     

Optimal chaining scheme for video-on-demand applications on collaborative networks

By forwarding the server stream client by client, a chaining-based scheme is a good way to reduce the server streams for streaming applications in well-connected networks. In this paper, we prove that the minimum number of required server streams in such schemes is n-k+1, where n is the number of client requests and k is a value determined by client buffer sizes and the distribution of requests. In addition, we present an optimal chaining algorithm using a dynamic buffer allocation strategy. Compared to existing chaining schemes, our scheme not only utilizes the backward (basic chaining) and/or forward (adaptive chaining) buffer, but also exploits the buffers of other clients in order to extend the chain as much as possible. In this way, more clients can be chained together and served by the same server stream. Our simulation results show that the requirements of the server streams in the presented scheme are much lower those of existing chaining schemes. We also introduce mechanisms for handling VCR functions and fault exceptions in practical applications.     

A dynamic framework for integrated management of all types of resources in P2P systems

Traditional Peer-to-Peer (P2P) systems were restricted to sharing of files on the Internet. Although some of the more recent P2P distributed systems have tried to support transparent sharing of other types of resources, like computer processing power, but none allow and support sharing of all types of resources available on the Internet. This is mainly because the resource management part of P2P systems are custom designed in support of specific features of only one type of resource, making simultaneous access to all types of resources impractical. Another shortcoming of existing P2P systems is that they follow a client/server model of resource sharing that makes them structurally constrained and dependent on dedicated servers (resource managers). Clients must get permission from a limited number of servers to share or access resources, and resource management mechanisms run on these servers. Because resource management by servers is not dynamically reconfigurable, such P2P systems are not scalable to the ever growing extent of Internet. We present an integrated framework for sharing of all types of resources in P2P systems by using a dynamic structure for managing four basic types of resources, namely process, file, memory, and I/O, in the same way they are routinely managed by operating systems. The proposed framework allows P2P systems to use dynamically reconfigurable resource management mechanisms where each machine in the P2P system can at the same time serve both as a server and as a client. The pattern of requests for shared resources at a given time identifies which machines are currently servers and which ones are currently clients. The client server pattern changes with changes in the pattern of requests for distributed resources. Scalable P2P systems with dynamically reconfigurable structures can thus be built using our proposed resource management mechanisms. This dynamic structure also allows for the interoperability of different P2P systems.     

Cloud Computing System Management Under Flat Rate Pricing

Success of cloud computing service depends on an acceptable pricing mechanism both by users and the service provider. Piece rate pricing by counting work load should be favorable for the service provider due to the QoS control and finite resource, such as computing and, communication powers. Though the pricing mechanism based on counting work load is reasonable and fair, the experiences learned from ADSL, 3G and Wi-Fi show a different story. The flat rate pricing mechanism is the winner all the way. This study proposes a flat rate pricing mechanism with congestion control, called FRPCC. In the cloud computing system, allocation of resources can be formulated as an optimization problem seeking to maximize the sum of the utility function of each user under the constraints of fairness. The piece rate pricing mechanism is easy to achieve the social welfare but is not easy to be acceptable for customers. Consequently, we propose a congestion control scheme to reach the same goal with a flat rate pricing mechanism. The proposed FRPCC approach can achieve social welfare in the cloud computing environment. Performance evaluations show efficacy of, FRPCC approach in providing social welfare under fairness and preventing congestion.     

Resource allocation optimization for quantitative service differentiation on server clusters

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers.     

Operations Research Methods for Autonomic Resource Management

Varying service demands on next generation networks requires autonomic resource management capabilities to ensure service delivery to customers, motivating the need to develop self-managing algorithms for this purpose. In this paper, we use algorithms from the Operations Research community for this purpose. First, we adapt the Transportation Model to manage distributed resources in the next generation network infrastructure. Coupled with monitoring capabilities, the proposed scheme can automatically adjust the virtual resource allocation to optimize the costs incurred by service providers in offering services to customers. Second, we propose the use of Inventory Control to predict needs for virtual resources and to pre-order required virtual resource amounts. We compare two possible Inventory Control models to manage the virtual resources involved in service delivery. We perform extensive simulations to show the performance improvements made possible by the use of the Transportation Model and Inventory Control for autonomic resource management in next generation networks.     

基于边缘缓存的下载加速方案设计与实现

针对传统网络拥塞导致网络下载速度过慢等一系列问题,文章通过对内容分发网络和P2P技术的研究,提出了一种基于边缘缓存的下载加速方案。该方案将P2P技术引入内容分发网络,利用各自优点,构建一个新型资源下载服务系统。将中心服务器上的资源推送至网络边缘,缩短了用户和资源的距离。同时,让边缘缓存服务器与邻近用户自发组建P2P下载网络,将资源更加边缘化,通过就近获取资源,提高下栽速度。实验表明,该方案不仅可以有效提高下栽速度,而且可以有效控制骨干网的网络流量。     

SALSA: QoS-aware load balancing for autonomous service brokering

The evolution towards “Software as a Service”, facilitated by various web service technologies, has led to applications composed of a number of service building blocks. These applications are dynamically composed by web service brokers, but rely critically on proper functioning of each of the composing subparts which is not entirely under control of the applications themselves. The problem at hand for the provider of the service is to guarantee non-functional requirements such as service access and performance to each customer. To this end, the service provider typically divides the load of incoming service requests across the available server infrastructure. In this paper we describe an adaptive load balancing strategy called SALSA (Simulated Annealing Load Spreading Algorithm), which is able to guarantee for different customer priorities, such as default and premium customers, that the services are handled in a given time and this without the need to adapt the servers executing the service logic themselves. It will be shown that by using SALSA, web service brokers are able to autonomously meet SLAs, without a priori over-dimensioning resources. This will be done by taking into account a real time view of the requests by measuring the Poisson arrival rates at that moment and selectively drop some requests from default customers. This way the web servers’ load is reduced in order to guarantee the service time for premium customers and provide best effort to default customers. We compared the results of SALSA with weighted round-robin (WRR), nowadays the most used load balancing strategy, and it was shown that the SALSA algorithm requires slightly more processing than WRR but is able to offer guarantees - contrary to WRR - by dynamically adapting its load balancing strategy.     

Customer oriented resource allocation framework in cognitive radio

Spectrum is among the most heavily regulated and expensive resource around the world. In an effort to improve the efficiency of its usage, alternative spectrum allocation models are being proposed. One of these approaches is the Dynamic Spectrum Access which allows the cognitive radio to use or share the spectrum in an opportunistic manner. This paper proposes a framework for an architecture where unutilized bands are relicensed to different service providers on a short term basis. This framework focuses on the resource allocation process of these leased bands to customers. The proposed model could be utilized as a decision support tool which both satisfies the quality of service requirements of individual users and achieve an efficient utilization of network resources. The hypothesis is that customer information yields an efficient customer-oriented resource management strategy; therefore the allocation process integrates the information about the customer demographics and usage behavior. Simulations with real-life scenarios in cellular networks have been performed: handoff decision, resource allocation at call initiation and profit maximization in allocation of mobile base stations. In each case, the results show that the proposed model performs better in terms of revenue increase when compared to the First-Come First-Serve based approach. The simulation results prove that the population size of a customer group is not always an indicator for higher revenue for a service provider.     

基于对等网络模式的视频点播系统设计

根据当今视频点播系统的现存问题，提出了在对等网络模式上视频点播系统的设计方案，使所有的客户既是服务的消费者又是服务的提供者，从而克服了现在视频点播系统的瓶颈问题。     

P2P技术在电信业务中的一种应用*

没有集中控制的P2P网络中的任一端点既是服务者又是使用者,在电信业务中采用P2P技术,结合P2P优势的同时需要考虑业务部署的可管理性和可运维性。将管理元数据抽象出来的分层可控的P2P业务模型给出了一种应用的方式,并通过彩铃业务提供了一种实现。