Application centric load balancing for distributed systems using genetic algorithm scheduling

This thesis proposes a GA based scheduling algorithm for a heterogeneous distributed computing environment. It uses the application centric load balancing system. The proposed system removes all those network delay assumptions and considers the allocation problem for both computing resources and network resources. It addresses the generalized workload i.e. direct acyclic graph (DAG) kind of workload that is composed of sub jobs with internal dependencies. Rather than allocate applications simply according to their arrival time, we introduce GA scheduling strategy into our load balancing system to find the proper applications allocating schedule, which uses the available resources more efficiently. With introduction of GA scheduling into both application level and process level, certain improvements on the practicability, accuracy and performance are expected. Instead of using constant GA parameters, our proposed algorithm dynamically adjusts the key parameters, such as crossover rate and mutation rate, adapting them to the quality of generations. Later, we will implement more new ideas, such as gender assignment, fertility rate and aging into our GA algorithm to achieve better performance. \u27Keywords.\u27 Load balancing, network delay, workload simulator, Adaptive Genetic Algorithm scheduling etc

On the benefits of tasking with OpenMP

Tasking promises a model to program parallel applications that provides intuitive semantics. In the case of tasks with dependences, it also promises better load balancing by removing global synchronizations (barriers), and potential for improved locality. Still, the adoption of tasking in production HPC codes has been slow. Despite OpenMP supporting tasks, most codes rely on worksharing-loop constructs alongside MPI primitives. This paper provides insights on the benefits of tasking over the worksharing-loop model by reporting on the experience of taskifying an adaptive mesh refinement proxy application: miniAMR. The performance evaluation shows the taskified implementation being 15–30% faster than the loop-parallel one for certain thread counts across four systems, three architectures and four compilers thanks to better load balancing and system utilization. Dynamic scheduling of loops narrows the gap but still falls short of tasking due to serial sections between loops. Locality improvements are incidental due to the lack of locality-aware scheduling. Overall, the introduction of asynchrony with tasking lives up to its promises, provided that programmers parallelize beyond individual loops and across application phases.Peer ReviewedPostprint (author's final draft

Mixed integer goal programming model for flexible job shop scheduling problem (FJSSP) with load balancing / Shirley Sinatra Gran

In manufacturing-related industries, scheduling of resources and tasks play an important role in improving efficiency and productivityas well as reducing costs. Job shop scheduling problem (JSSP) concerns with the problem whereby there is only one machine that can process one type of operation. The flexible job shop scheduling problem (FJSSP) is an extension of t he job shop scheduling problem. FJSSP allows an operation to be processed by any machine out of a set of alternative machines. Thus, the objectives of this study are to analyze the production schedules and operations of the machines in FJSSP, to construct a load balancing constraint function, to formulate a Mixed Integer Goal Programming (MIGP) model to solve FJSSP with load balancing; and to propose an optimal production job shop scheduling strategies based on the solution model. The MIGP model formulated is to solve FJSSP with three objective functions, which are to minimize the makespan, the total machining time and the mean absolute deviation of the total machining time to achieve machine’s load balancing. The model was solved by implementing the pre-emptive goal programming approach and using the Microsoft Excel Solver Add-Ins. The novelties of this study are the introduction of the objective function that minimizes the mean absolute deviation of the total machining time and therefore producing balanced load (total machining time) among machines used in the FJSSP. Data from benchmark problem instances for the general FJSSP with total flexibility by Kacem, Hammadi and Borne has been used in the computational experiments. Optimal solutions were found for the FJSSP involved. The results obtained proved that the proposed solution approach gives competitive results as compared to the metaheuristics approaches

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