Over the past decade several frameworks such as Google MapReduce have been developed that allow data processing with unprecedented scale due to their high scalability and fault tolerance. However, these systems provide both new and existing challenges for workload balancing that have not yet been fully explored. The MapReduce model in particular has some inherent limitations when it comes to wo...

in this paper, the staff assignment problem considering cross-training of caregivers in health care systems is addressed to determine which staff should be cross-trained for each service and how they should be assigned to services. a bi-objective non-linear mathematical programming model is presented where the first objective function aims to minimize workload balancing, cross-training as well ...

Load balancing of the Internet bank can be implemented by reduction of the workload of the bottleneck Web server. Load balancing improves both a performance of the computer system and the reliability of a server network. An evolutionary algorithm improved by introducing a tabu search procedure is discussed for solving multi-criteria optimization problem of finding a set of Pareto-suboptimal tas...

Load balance is one of the major challenges for efficient supercomputing, especially for applications that exhibit workload variations. Various dynamic load balancing and workload partitioning methods have been developed to handle this issue by migrating workload between nodes periodically during the runtime. However, on today’s top HPC systems – and even more so on future exascale systems – ru...

Most intermediate and high-level vision algorithms manipulate symbolic features. A key operation in these vision algorithms is to search symbolic features satisfying certain geometric constraints. Parallelizing this symbolic search needs a non-trivial algorithmic technique due to the unpredictable workload. In this paper, we propose load balancing strategies for parallelizing symbolic search op...

Workload and resource management are essential functionalities in the software infrastructure for grid computing. The management and scheduling of dynamic grid resources in a scalable way requires new technologies to implement a next generation intelligent grid environment. This work demonstrates that AI technologies can be utilised to achieve effective workload and resource management. A combi...

The ever-increasing operational cost of geographically distributed datacenters has become a critical issue for cloud service providers. In order to cut the electricity cost of geographically distributed datacenters, several workload management schemes have been proposed, such as Electricity price-aware InteR-datacenter load balancing (EIR), which reduces the electricity cost of active servers b...

Online games and location-based services now form the potential application domains for the P2P paradigm. In P2P systems, balancing the workload is essential for overall performance. However, existing load balancing techniques for P2P systems were designed for stationary data. They can produce undesirable workload allocations for moving objects that is continuously updated. In this paper, we pr...

This paper addresses the problem of scheduling and load balancing in heterogeneous computational grids. We proposed a two-level load balancing policy for the multi-cluster grid environment where computational resources are dispersed in different administrative domains or clusters which are located in different local area networks. The proposed load balancing policy takes into account the hetero...

Load balancing in publish/subscribe (pub/sub) broker networks is challenging as the workload is multi-dimensional and content-dependent. In this paper we present the framework design of a middleware , called Shuffle, to achieve optimal load balancing in a pub/sub broker network. Shuffle features a suite of active workload management schemes within a single overlay topology on message parsing, m...