While in the past scheduling algorithms were almost exclusively targeted at optimizing applications' make span, today they must simultaneously optimise several goals. Among these goals, energy efficiency is receiving increasing attention for environmental and financial reasons. In contrast to related work that optimises energy consumption as a single objective function, we reformulate in this paper the problem as a bi-objective optimisation by considering both make span and energy as goals. We study the potential benefits of using a Pareto-based workflow scheduling algorithm called MOHEFT using realistic energy consumption and performance models for task executions. We analyse the tradeoff solutions computed by MOHET for different workflows (different in shapes and sizes) in different execution scenarios (different resources in terms of energy consumption). The obtained results show that our bi-objective approach found in some cases schedules that reduce the energy consumption up to 85% with only 3.3% of make span concessions.

Energy efficiency has become a key issue in computer science related research and development over the last years. While most approaches focus either on hardware or on software, we propose a solution incorporating both hardware and software enabling the measurement the energy consumption of code segments executed on physical machines. Our novel approach to energy measurement and instrumentation allows for both state-of-the-art offline analysis, and innovative online measurements associated with the code being executed. We present our modular architecture which shields the users from in-depth knowledge of their energy measurement hardware, and allows the development of code for measurement and instrumentation independent of each instruments' proprietary interface. Finally, we propose an efficient method for increasing the accuracy of measurements for low sampling rate measurement devices.

Today, highly successful Massively Multiplayer Online Games (MMOGs) have millions of registered users and hundreds of thousands of active concurrent players. To sustain their highly variable load, game operators over-provision a large static infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time. This inefficient resource utilisation has negative economic impacts by preventing any but the largest hosting centres from joining the market and dramatically increases prices.In this paper, we propose a new business model of hosting and operating MMOGs based on Cloud computing principles involving four actors: resource provider, game operator, game provider, and client. Our model efficiently provisions on-demand virtualised resources to game sessions based on their dynamic client load, which dramatically decreases prices and gives small and medium enterprises the opportunity of joining the market through zero initial investment.We validate our new model and its underlying business relationships through trace-based simulations utilising six months worth of monitoring data from a real-life MMOG using emulated resources from 16 of the largest Cloud resource providers currently on the market. We demonstrate that our model can operate state-of-the-art MMOGs with an average monthly gross profit of nearly $6 million excluding game purchase prices, overheads and taxation, while being able to maintain and control the QoS offered to all clients. Finally, we show how our approach is capable of operating next generation very highly interactive MMOGs with a small increase of 5.8% in the subscription price.

Today's Massively Multiplayer Online Games (MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to overprovision for each game tens of self-owned data centers, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centers as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population size. Our model is best used for estimating the MMOG resource demand dynamically, and thus, for dynamic resource provisioning based on the game world entity distribution. We evaluate several classes of online predictors for MMOG entity distribution and propose and tune a neural network-based predictor to deliver good accuracy consistently under real-time performance constraints. We assess using trace-based simulation the impact of the data center policies on the quality of resource provisioning. We find that the dynamic resource provisioning can be much more efficient than its static alternative even when the external data centers are busy, and that data centers with policies unsuitable for MMOGs are penalized by our dynamic resource provisioning method. Finally, we present experimental results showing the real-time parallelization and load balancing of a real game prototype using data center resources provisioned using our method and show its advantage against a rudimentary client threshold approach.

Today's massively multiplayer online games (MMOGs) can include millions of concurrent players spread across the world. To keep these highly-interactive virtual environments online, a MMOG operator may need to provision tens of thousands of computing resources from various data centers. Faced with large resource demand variability, and with misfit resource renting policies, the current industry practice is to maintain for each game tens of self-owned data centers. In this work we investigate the dynamic resource provisioning from external data centers for MMOG operation. We introduce a novel MMOG workload model that represents the dynamics of both the player population and the player interactions. We evaluate several algorithms, including a novel neural network predictor, for predicting the resource demand. Using trace-based simulation, we evaluate the impact of the data center policies on the resource provisioning efficiency; we show that dynamic provisioning can be much more efficient than its static alternative.

The users of Grid-based e-learning platforms must be able to use any standard Web browser to access the e-learning and Grid resources through a Grid Portal, at any time, from any location. Standard Web security protocols, employed between a Web client and server, do not support delegation and, due to this constraint, Grid Portals are forced to give the portal special privileges to act on users' behalf on the Grid. We solved this problem by integration of the Grid authentication mechanisms with the capabilities of Web servers using an online credential repository, called MyProxy server. Our development provides a complete and concise solution for the problem of credentials management and delegation for trusted access of users to the e-learning resources, including Grid services.