Random Approach to Optimization of Overlay Public-Resource Computing Systems

The growing need for computationally demanding systems triggers the development of various network-oriented computing systems organized in a distributed manner. In this work we concentrate on one kind of such systems, i.e. public-resource computing systems. The considered system works on the top of an overlay network and uses personal computers and other relatively simple electronic equipment instead of supercomputers. We assume that two kinds of network flows are used to distribute the data in the public-resource computing systems: unicast and peer-to-peer. We formulate an optimization model of the system. After that we propose random algorithms that optimize jointly the allocation of computational tasks and the distribution of the output data. To evaluate the algorithms we run numerical experiments and present results showing the comparison of the random approach against optimal solutions provided by the CPLEX solver

Heuristic Algorithms for Optimization of Task Allocation and Result Distribution in Peer-to-Peer Computing Systems

Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very close to an optimum and can be used for larger problem instances than those solvable by CPLEX or other ILP solvers

CloudMedia: When cloud on demand meets video on demand

Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.published_or_final_versionThe 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-27

Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version

Data storage solutions for the federation of sensor networks

In the near future, most of our everyday devices will be accessible via some network and uniquely identified for interconnection over the Internet. This new paradigm, called Internet of Things (IoT), is already starting to influence our society and is now driving developments in many areas. There will be thousands, or even millions, of constrained devices that will be connected using standard protocols, such as Constrained Application Protocol (CoAP), that have been developed under certain specifications appropriate for this type of devices. In addition, there will be a need to interconnect networks of constrained devices in a reliable and scalable way, and federations of sensor networks using the Internet as a medium will be formed. To make the federation of geographically distributed CoAP based sensor networks possible, a CoAP Usage for REsource LOcation And Discovery (RELOAD) was recently proposed. RELOAD is a peer-to-peer (P2P) protocol that ensures an abstract storage and messaging service to its clients, and it relies on a set of cooperating peers that form a P2P overlay network for this purpose. This protocol allows to define so-called Usages for applications to work on top of this overlay network. The CoAP Usage for RELOAD is, therefore, a way for CoAP based devices to store their resources in a distributed P2P overlay. Although CoAP Usage for RELOAD is an important step towards the federation of sensor networks, in the particular case of IoT there will be consistency and efficiency problems. This happens because the resources of CoAP devices/Things can be in multiple data objects stored at the overlay network, called P2P resources. Thus, Thing resource updates can end up being consuming, as multiple P2P resources will have to be modified. Mechanisms to ensure consistency become, therefore, necessary. This thesis contributes to advances in the federation of sensor networks by proposing mechanisms for RELOAD/CoAP architectures that will allow consistency to be ensured. An overlay network service, required for such mechanisms to operate, is also proposed.Num futuro próximo, a maioria dos nossos dispositivos do dia-a-dia estarão acessíveis através de uma rede e serão identificados de forma única para poderem interligar-se através da Internet. Este novo paradigma, conhecido hoje por Internet das Coisas (IoT), já está a começar a influenciar a nossa sociedade e está agora a impulsionar desenvolvimentos em inúmeras áreas. Teremos milhares, ou mesmo milhões, de dispositivos restritos que utilizarão protocolos padrão que foram desenvolvidos de forma a cumprir determinadas especificações associadas a este tipo de dispositivos, especificações essas que têm a ver com o facto destes dispositivos terem normalmente restrições de memória, pouca capacidade de processamento e muitos possuirem limitações energéticas. Surgirá ainda a necessidade de interligar, de forma fiável e escalonável, redes de dispositivos restritos.(…

Hybrid algorithms for independent batch scheduling in grids

Grid computing has emerged as a wide area distributed paradigm for solving large-scale problems in science, engineering, etc. and is known as the family of eScience grid-enabled applications. Computing planning of incoming jobs efficiently with available machines in the grid system is the main requirement for optimised system performance. One version of the problem is that of independent batch scheduling, in which jobs are assumed to be independent and are scheduled in batches aimed at minimising the makespan and flowtime. Given the hardness of the problem, heuristics are used to find high quality solutions for practical purposes of designing efficient grid schedulers. Recently, considerable efforts were spent in implementing and evaluating not only stand-alone heuristics and meta-heuristics, but also their hybridisation into even higher level algorithms. In this paper, we present a study on the performance of two popular algorithms for the problem, namely Genetic Algorithms (GAs) and Tabu Search (TS) and two hybridisations involving them, namely, the GA (TS) and GA-TS, which differ in the way the main control and cooperation among GA and TS are implemented. The hierarchic and simultaneous optimisation modes are considered for the bi-objective scheduling problem. Evaluation is done using different grid scenarios generated by a grid simulator. The computational results showed that the hybrid algorithm outperforms both the GA and TS for the makespan parameter, but not for the flowtime parameter.Peer ReviewedPostprint (author's final draft

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

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.Comment: 22 Pages, 4 Figures, 4 Tables, in pres