Multi-Criteria Service Selection Agent for Federated Cloud

Federated cloud interconnects small and medium-sized cloud service providers for service enhancement to meet demand spikes. The service bartering technique in the federated cloud enables service providers to exchange their services. Selecting an optimal service provider to share services is challenging in the cloud federation. Agent-based and Reciprocal Resource Fairness (RRF) based models are used in the federated cloud for service selection. The agent-based model selects the best service provider using Quality of Service (quality of service). RRF model chooses fair service providers based on service providers\u27 previous service contribution to the federation. However, the models mentioned above fail to address free rider and poor performer problems during the service provider selection process. To solve the above issue, we propose a Multi-criteria Service Selection (MCSS) algorithm for effectively selecting a service provider using quality of service, Performance-Cost Ratio (PCR), and RRF. Comprehensive case studies are conducted to prove the effectiveness of the proposed algorithm. Extensive simulation experiments are conducted to compare the proposed algorithm performance with the existing algorithm. The evaluation results demonstrated that MCSS provides 10% more services selection efficiency than Cloud Resource Bartering System (CRBS) and provides 16% more service selection efficiency than RPF

On the Impact of Practical P2P Incentive Mechanisms on User Behavior

In this paper we report on the results of a large-scale measurement study of two popular peer-topeer systems, namely BitTorrent and eMule, that use practical and lightweight incentive mechanisms to encourage cooperation between users. We focus on identifying the strategic behavior of users in response to those incentive mechanisms. Our results illustrate a gap between what system designers and researchers expect from users in reaction to an incentive mechanism, and how users react to those incentives. In particular, we observe that the majority of BitTorrent users appear to cooperate well, despite the existence of known ways to tamper with the incentive mechanism, users engaging in behavior that could be regarded as cheating comprised only around 10% of BitTorrent’s population. That is, although we know that users can easily cheat, they actually do not currently appear to cheat at a large enough scale. In the eMule system, we identify several distinct classes of users based on their behavior. A large fraction of users appears to perceive cooperation as a good strategy, and openly share all the files they obtained. Other users engage in more subtle strategic choices, by actively optimizing the number and types of files they share in order to improve their standing in eMule’s waiting queues; they tend to remove files for which downloading is complete and keep a limited total volume of files shared

Doctor of Philosophy

dissertationWe develop a novel framework for friend-to-friend (f2f) distributed services (F3DS) by which applications can easily offer peer-to-peer (p2p) services among social peers with resource sharing governed by approximated levels of social altruism. Our frame- work differs significantly from typical p2p collaboration in that it provides a founda- tion for distributed applications to cooperate based on pre-existing trust and altruism among social peers. With the goal of facilitating the approximation of relative levels of altruism among social peers within F3DS, we introduce a new metric: SocialDistance. SocialDistance is a synthetic metric that combines direct levels of altruism between peers with an altruism decay for each hop to approximate indirect levels of altruism. The resulting multihop altruism levels are used by F3DS applications to proportion and prioritize the sharing of resources with other social peers. We use SocialDistance to implement a novel flash file/patch distribution method, SocialSwarm. SocialSwarm uses the SocialDistance metric as part of its resource allocation to overcome the neces- sity of (and inefficiency created by) resource bartering among friends participating in a BitTorrent swarm. We find that SocialSwarm achieves an average file download time reduction of 25% to 35% in comparison with standard BitTorrent under a variety of configurations and conditions, including file sizes, maximum SocialDistance, as well as leech and seed counts. The most socially connected peers yield up to a 47% decrease in download completion time in comparison with average nonsocial BitTorrent swarms. We also use the F3DS framework to implement novel malware detection application- F3DS Antivirus (F3AV)-and evaluate it on the Amazon cloud. We show that with f2f sharing of resources, F3AV achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the average of individual scanners. Furthermore, we show that F3AV provides the greatest diversity of mal- ware scanners (and thus malware protection) to social hubs-those nodes that are positioned to provide strategic defense against socially aware malware

On the Impact of Practical P2P Incentive Mechanisms on User Behavior

In this paper we report on the results of a large-scale measurement study of two popular peer-topeer systems, namely BitTorrent and eMule, that use practical and lightweight incentive mechanisms to encourage cooperation between users. We focus on identifying the strategic behavior of users in response to those incentive mechanisms. Our results illustrate a gap between what system designers and researchers expect from users in reaction to an incentive mechanism, and how users react to those incentives. In particular, we observe that the majority of BitTorrent users appear to cooperate well, despite the existence of known ways to tamper with the incentive mechanism, users engaging in behavior that could be regarded as cheating comprised only around 10% of BitTorrent’s population. That is, although we know that users can easily cheat, they actually do not currently appear to cheat at a large enough scale. In the eMule system, we identify several distinct classes of users based on their behavior. A large fraction of users appears to perceive cooperation as a good strategy, and openly share all the files they obtained. Other users engage in more subtle strategic choices, by actively optimizing the number and types of files they share in order to improve their standing in eMule’s waiting queues; they tend to remove files for which downloading is complete and keep a limited total volume of files shared

Doctor of Philosophy

dissertationWe propose a collective approach for harnessing the idle resources (cpu, storage, and bandwidth) of nodes (e.g., home desktops) distributed across the Internet. Instead of a purely peer-to-peer (P2P) approach, we organize participating nodes to act collectively using collective managers (CMs). Participating nodes provide idle resources to CMs, which unify these resources to run meaningful distributed services for external clients. We do not assume altruistic users or employ a barter-based incentive model; instead, participating nodes provide resources to CMs for long durations and are compensated in proportion to their contribution. In this dissertation we discuss the challenges faced by collective systems, present a design that addresses these challenges, and study the effect of selfish nodes. We believe that the collective service model is a useful alternative to the dominant pure P2P and centralized work queue models. It provides more effective utilization of idle resources, has a more meaningful economic model, and is better suited for building legal and commercial distributed services. We demonstrate the value of our work by building two distributed services using the collective approach. These services are a collective content distribution service and a collective data backup service

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Context-aware task scheduling in distributed computing systems

These days, the popularity of technologies such as machine learning, augmented reality, and big data analytics is growing dramatically. This leads to a higher demand of computational power not only for IT professionals but also for ordinary device users who benefit from new applications. At the same time, the computational performance of end-user devices increases to meet the demands of these resource-hungry applications. As a result, there is a coexistence of a huge demand of computational power on the one side and a large pool of computational resources on the other side. Bringing these two sides together is the idea of computational resource sharing systems which allow applications to forward computationally intensive workload to remote resources. This technique is often used in cloud computing where customers can rent computational power. However, we argue that not only cloud resources can be used as offloading targets. Rather, idle CPU cycles from end-user administered devices at the edge of the network can be spontaneously leveraged as well. Edge devices, however, are not only heterogeneous in their hardware and software capabilities, they also do not provide any guarantees in terms of reliability or performance. Does it mean that either the applications that require further guarantees or the unpredictable resources need to be excluded from such a sharing system? In this thesis, we propose a solution to this problem by introducing the Tasklet system, our approach for a computational resource sharing system. The Tasklet system supports computation offloading to arbitrary types of devices, including stable cloud instances as well as unpredictable end-user owned edge resources. Therefore, the Tasklet system is structured into multiple layers. The lowest layer is a best-effort resource sharing system which provides lightweight task scheduling and execution. Here, best-effort means that in case of a failure, the task execution is dropped and that tasks are allocated to resources randomly. To provide execution guarantees such as a reliable or timely execution, we add a Quality of Computation (QoC) layer on top of the best-effort execution layer. The QoC layer enforces the guarantees for applications by using a context-aware task scheduler which monitors the available resources in the computing environment and performs the matchmaking between resources and tasks based on the current state of the system. As edge resources are controlled by individuals, we consider the fact that these users need to be able to decide with whom they want to share their resources and for which price. Thus, we add a social layer on top of the system that allows users to establish friendship connections which can then be leveraged for social-aware task allocation and accounting of shared computation

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019