Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Revenue maximization problems in commercial data centers

As IT systems are becoming more important everyday, one of the main concerns is that users may face major problems and eventually incur major costs if computing systems do not meet the expected performance requirements: customers expect reliability and performance guarantees, while underperforming systems loose revenues. Even with the adoption of data centers as the hub of IT organizations and provider of business efficiencies the problems are not over because it is extremely difficult for service providers to meet the promised performance guarantees in the face of unpredictable demand. One possible approach is the adoption of Service Level Agreements (SLAs), contracts that specify a level of performance that must be met and compensations in case of failure. In this thesis I will address some of the performance problems arising when IT companies sell the service of running ‘jobs’ subject to Quality of Service (QoS) constraints. In particular, the aim is to improve the efficiency of service provisioning systems by allowing them to adapt to changing demand conditions. First, I will define the problem in terms of an utility function to maximize. Two different models are analyzed, one for single jobs and the other useful to deal with session-based traffic. Then, I will introduce an autonomic model for service provision. The architecture consists of a set of hosted applications that share a certain number of servers. The system collects demand and performance statistics and estimates traffic parameters. These estimates are used by management policies which implement dynamic resource allocation and admission algorithms. Results from a number of experiments show that the performance of these heuristics is close to optimal.EThOS - Electronic Theses Online ServiceQoSP (Quality of Service Provisioning) : British TelecomGBUnited Kingdo

Efficient Content Distribution With Managed Swarms

Content distribution has become increasingly important as people have become more reliant on Internet services to provide large multimedia content. Efficiently distributing content is a complex and difficult problem: large content libraries are often distributed across many physical hosts, and each host has its own bandwidth and storage constraints. Peer-to-peer and peer-assisted download systems further complicate content distribution. By contributing their own bandwidth, end users can improve overall performance and reduce load on servers, but end users have their own motivations and incentives that are not necessarily aligned with those of content distributors. Consequently, existing content distributors either opt to serve content exclusively from hosts under their direct control, and thus neglect the large pool of resources that end users can offer, or they allow end users to contribute bandwidth at the expense of sacrificing complete control over available resources. This thesis introduces a new approach to content distribution that achieves high performance for distributing bulk content, based on managed swarms. Managed swarms efficiently allocate bandwidth from origin servers, in-network caches, and end users to achieve system-wide performance objectives. Managed swarming systems are characterized by the presence of a logically centralized coordinator that maintains a global view of the system and directs hosts toward an efficient use of bandwidth. The coordinator allocates bandwidth from each host based on empirical measurements of swarm behavior combined with a new model of swarm dynamics. The new model enables the coordinator to predict how swarms will respond to changes in bandwidth based on past measurements of their performance. In this thesis, we focus on the global objective of maximizing download bandwidth across end users in the system. To that end, we introduce two algorithms that the coordinator can use to compute efficient allocations of bandwidth for each host that result in high download speeds for clients. We have implemented a scalable coordinator that uses these algorithms to maximize system-wide aggregate bandwidth. The coordinator actively measures swarm dynamics and uses the data to calculate, for each host, a bandwidth allocation among the swarms competing for the host's bandwidth. Extensive simulations and a live deployment show that managed swarms significantly outperform centralized distribution services as well as completely decentralized peer-to-peer systems

Optimization Algorithm for Antenna Impedance Matching in Digitally Tunable Network

In this work, we explore different methods to tune the antenna impedance in mobile devices. Mismatch from antenna impedance can cause undesirable effects such as spurious emissions, channel leakages, increased noise floor, degraded receiver sensitivity and so on. With the advancement in technology, digitally tunable reactive components are now available. Thus, a feedback system with tunable circuitry and the aperture tuning method where the component is directly embedded in the antenna design are some of the popular choices of solution. The ‘look-up table’ method is currently widely adopted in wireless industry. The hardware component chain (RF chain) contains the set-up to measure Γ_in(ratio of reflected signal to transmitted signal in dB) and a circuit to be tuned according to the values found in the look-up table. The look-up table is a pre-defined calibration chart provided by the manufacturer. It is saved in the memory of the device for permanent use during its lifespan. In this thesis, in the effort to eliminate the process of creating this look-up table and also to free up large space of memory, we approach an analytical solution to predict the exact values of the component in the tunable circuit – hence, making the procedure a one-time measurement, so called the open-loop configuration. In Chapter 3, a thorough mathematical analysis has been developed to integrate the Q factors of each component into a sample pi-circuit. In such setup, the system is expected to calculate ZL (or the antenna, load) with measured Γ_in and then compute the three capacitance values that yield the best transducer gain by conjugate matching method. However, due to many non-ideal characteristics of the components, calibrating the setup and incorporating the calibration data into analytical solution becomes very challenging. Therefore, the closed-loop configuration is more useful. It collects the empirical data of Γ_in, apply the optimization algorithm and then tune the circuitry in feedback manner, until the lowest desired Γ_in is reached. (Note that there is no difference between the closed and open loop configuration in the physical set-up. ) The purpose of this thesis is to develop the optimization algorithm used in closed-loop configuration. It involves three degrees of freedom using three Digitally Tunable Capacitors (DTCs). Accordingly, the challenge of this research points to inventing a 3D-unconstrained optimization technique that is simple enough to be implemented in a microprocessor without employing complex equation-solving libraries. In Chapter 4, the Hill-Climbing algorithm is investigated to see if it provides a suitable approach for finding the global minimum Γ_in in the 3D space gradient defined by 3 variables or DTCs. The Hill Climbing method, however, will limit its solution to finding only the local minimum within the gradient. This means that the location of the solution will change with the resolution of the gradient and the search step-size. Therefore,it is expected that Hill Climbing algorithm yields different solutions depending on the increment size and the starting location of the search. Chapter 5 develops a new algorithm that is based on Grid Searching. The main idea is to grasp the picture of the entire gradient of 3D space and zoom-in closer to the global point by iteration. The challenge lies in defining the boundary of zoom-in region without leaking the global point and leaving it behind. Also, the scanning of the reduced region in each iteration must not be too rigorous – meaning requiring too many data points. All different pi-network will have its limited coverage region on Smith Chart, of which the load impedance can be matched with. Therefore, selecting the reactive component with the suitable range of capacitance is also an important step, in order to fully utilize the work of this thesis. Apart from that, the algorithm does not require any information about the antenna, frequency of operation nor the configuration of the DTCs. Overcoming these challenges will guarantee the device to have the best optimized state of impedance match, at a specific frequency. Given that the algorithm is a 3D optimization technique, the work of this research does not only apply to tuning a pi-tuner. The three DTCs can be also integrated in the aperture tuning system. Thesis Supervisor: Professor Safieddin Safavi-Naeini.

Design of a horizontally scalable backend application for online games

Mobile game market is increasing in popularity year after year, attracting a wide audience of independent developers who must endure the competition of other more resourceful game companies. Players expect high quality games and experiences, while developers strive to monetize. Researches have shown a correlation between some features of a game and its likelihood to succeed and be a potential candidate to enter the top grossing lists. This thesis focuses on identifying the trending features found on the current most successful games, and proposes the design of a scalable, flexible and modular backend application which integrates all the services needed for fulfilling the common needs of a mobile online game. A microservice oriented architecture have been used as a basis for the system design, leading to a modular decomposition of features into small, independent, reusable services. The system and microservices design comply with the Reactive Manifesto, allowing the application to reach responsiveness, elasticity, resiliency and asynchronicity. For its properties, the application is suitable to serve on a cloud environment covering the requirements for small games and popular games with high load of traffic and many concurrent players. The thesis, in addition to the application and microservices design, includes a discussion on the technology stack for a possible implementation and recommended setup for three use case scenarios

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Linda[m] and Tiamat: Providing generative communications in a changing world

When generative communications, as exemplified by Linda [Gel85], were originally proposed, they were intended as a mechanism for coordination of parallel processes. Since that time, they have been adapted to a variety of distributed environments with great success, as can be seen in commercial systems such as T Spaces [WMLF98]. The time, space and identity decoupling afforded to coordinating entities by generative communications also seems to be ideally suited to mobile environments where devices can come and go frequently and often without warning. Such a rapidly changing environment, however, presents a new set of challenges and attempts to introduce the generative communications paradigm into these environments have, so far, met with limited success. Indeed evaluation of research platforms, such as LIME (Linda In a Mobile Environment) [PMR99.MPR01] and L[2]imbo [DFWB98] have led some to conclude that the generative communication paradigm is not well suited to mobile environments. It is my belief, however, that it is the research platforms in question, rather than the paradigm, which do not fit well with mobile environments. These platforms either attempt to impose tight constraints on an inherently loosely constrained environment, or require significant alterations to the semantics of generative communications. I believe that these systems do not work well as they are not designed around the environment, rather they are forced onto the environment. I will begin by examining why these systems do not suit their environment. This done, I will then show that the conclusions drawn from these systems, namely that generative communications are unsuitable for mobile environments, are incorrect. Further, through construction and examination of a proof of concept system built around an environment-centric design, I will show that generative communications can be provided in a mobile environment with few (minor) semantic alterations. An evaluation of some of the mechanisms used will also be presented along with characterisation of the operation of the system. A comparison with existing mobile solutions will be used to highlight how the environment-driven design results in a system which better suits the nature of the target environment