Grid Computing in Extreme Situations: Reducing Risk and Creating Resilience for IT-Infrastructures

Recent turbulences in financial markets are not only a challenge for the actors in the front offices of the related institutions, but also represent a serious challenge for the IT departments in the back offices of banks etc. We present a simulation model that shows how Grid computing increases the resilience and quality-of-service of IT infrastructure in departmentalized enterprises in the presence of shocks. Grid computing also reduces the costs deriving from the cancellation of jobs in times with a high volatility of computational load. The model can be used to find the appropriate type of IT infrastructure for different financial service institutions. Our simulations\u27 findings are also likely to encourage the introduction of Grid computing for related business branches and applications

Dual Constraint Problem Optimization Using A Natural Approach: Genetic Algorithm and Simulated Annealing

Constraint optimization problems with multiple constraints and a large solution domain are NP hard and span almost all industries in a variety of applications. One such application is the optimization of resource scheduling in a pay per use grid environment. Charging for these resources based on demand is often referred to as Utility Computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and cost constraints is tasked to the Grid Resource Broker (GRB). The GRB must use an optimization algorithm that returns an accurate result in a timely mam1er. The Genetic Algorithm and the Simulated Annealing algorithm can both be used to achieve this goal, although Simulated Annealing outperforms the Genetic Algorithm for use by the GRB. Determining optimal values for the variables used in each algorithm is often achieved through trial and error, and success depends upon the solution domain of the problem. Although this work outlines a specific grid resource allocation application, the results can be applied to any optimization problem based on dual constraints

Revenue maximization problems in commercial data centers

PhD ThesisAs 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.QoSP (Quality of Service Provisioning)British Teleco

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

Mapping Service-Level Agreements in Distributed Applications

stringent execution-time guarantees. Optrantees.S Problem The SLA allocation problem we want to solve is this: Given a value E for the application-level execution-time SLA, the values {E m , m = 1, ..., M} of the minimum execution time per component, the cost functions C m , and the visit ratios V m , find a set {E m , m = 1, ..., M} of component -level execution-time SLAs that minimizes the total cost (3) such that (4a) (4b) The upper bound on Equation 4b comes from the fact that the maximum total time spent on component m, or V m E m , cannot exceed the application -level SLA, or E. Numerical Example To illustrate the SLA-optimization problem, consider an application that has a global executiontime SLA of 20 seconds uses four components. Table 1 shows the average number of visits per component and the minimum execution times; the cost functions for each component are E V mM mm min max ##,=1,...,. m m m EVE mm m 1 100 SEPTEMBER . OCTOBER 2