Evaluating load balancing policies for performance and energy-efficiency

Nowadays, more and more increasingly hard computations are performed in challenging fields like weather forecasting, oil and gas exploration, and cryptanalysis. Many of such computations can be implemented using a computer cluster with a large number of servers. Incoming computation requests are then, via a so-called load balancing policy, distributed over the servers to ensure optimal performance. Additionally, being able to switch-off some servers during low period of workload, gives potential to reduced energy consumption. Therefore, load balancing forms, albeit indirectly, a trade-off between performance and energy consumption. In this paper, we introduce a syntax for load-balancing policies to dynamically select a server for each request based on relevant criteria, including the number of jobs queued in servers, power states of servers, and transition delays between power states of servers. To evaluate many policies, we implement two load balancers in: (i) iDSL, a language and tool-chain for evaluating service-oriented systems, and (ii) a simulation framework in AnyLogic. Both implementations are successfully validated by comparison of the results.Comment: In Proceedings QAPL'16, arXiv:1610.0769

Computing response time distributions using iterative probabilistic model checking

System designers need to have insight in the response times of service systems to see if they meet performance requirements. We present a high-level evaluation technique to obtain the distribution of services completion times. It is based on a high-level domain-specific language that hides the underlying technicalities from the system designer. Under the hood, probabilistic real-time model checking technology is used iteratively to obtain precise bounds and probabilities. This allows reasoning about nondeterministic, probabilistic and real-time aspects in a single evaluation. To reduce the state spaces for analysis, we use two sampling methods (for measurements) that simplify the system model: (i) applying an abstraction on time by increasing the length of a (discrete) model time unit, and (ii) computing only absolute bounds by replacing probabilistic choices with non-deterministic ones. We use an industrial case on image processing of an interventional X-ray system to illustrate our approach

Computing Response Time Distributions Using Iterative Probabilistic Model Checking

System designers need to have insight in the response times of service systems to see if they meet performance requirements. We present a high-level evaluation technique to obtain the distribution of services completion times. It is based on a high-level domain-specific language that hides the underlying technicalities from the system designer. Under the hood, probabilistic real-time model checking technology is used iteratively to obtain precise bounds and probabilities. This allows reasoning about nondeterministic, probabilistic and real-time aspects in a single evaluation. To reduce the state spaces for analysis, we use two sampling methods (for measurements) that simplify the system model: (i) applying an abstraction on time by increasing the length of a (discrete) model time unit, and (ii) computing only absolute bounds by replacing probabilistic choices with non-deterministic ones. We use an industrial case on image processing of an interventional X-ray system to illustrate our approach