The growing popularity of peer-to-peer (P2P) systems has necessitated the need for managing huge volumes of data efficiently to ensure acceptable user response times. Dynamically changing popularities of data items and skewed user query patterns in P2P systems may cause some of the peers to become bottlenecks, thereby resulting in severe load imbalance and consequently increased user response t...

Recently, the Cluster of Computers (COC) has been used to run large parallel programs increasingly. Task migration is a desirable and useful facility to implement Load-Balance and High-Availibility in COCs. This paper presents a quick migration protocol for MPI tasks, which allows nonmigrating tasks to execute during most of the time of migration. Process table updating and synchronization are ...

Process migration is the act of transferring a process between two machines. It enables dynamic load distribution, fault resilience, eased system administration, and data access locality. Despite these goals and ongoing research efforts, migration has not achieved widespread use. With the increasing deployment of distributed systems in general, and distributed operating systems in particular, p...

Cloud computing requires more reliable, efficient and scalable load balancing algorithm to survive. As one of the main challenges in cloud computing, load balancing facilitate dynamic workload across multiple nodes ensuring that no single node get overloaded. With proper load balancing, resource consumption is maintained at minimum level, enabling scalability, avoiding bottleneck and overprovis...

Many parallel scienti c applications have dynamic and irregular computational structure. However, most such applications exhibit persistence of computational load and communication structure. This allows us to embed measurement-based automatic load balancing framework in run-time systems of parallel languages that are used to build such applications. In this paper, we describe such a framework ...

River is a prototype I/O programming environment for large-scale cluster of workstations. The assignment of the data ow and its operators (called modules) to participating nodes is currently done statically before any processing starts. The ow is dynamic because some data can be routed to modules that are processing data faster. We introduce module migration (by migrating the underlying thread)...

This paper discusses how locality between objects a ects the performance, and proposes a software architecture for enhancing locality while keeping load-balance reasonable at the minimum sacri ce of runtime overhead. Objects are created locally by default and long-lived objects are selectively migrated during garbage collection. By enhancing locality, message passings are likely to be local and...

We develop and examine job migration policies by considering effective usage of global memory in addition to CPU load sharing in distributed systems. When a node is identified for lacking sufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote nodes with low memory allocations. If the memory space is sufficiently large, the jobs will be scheduled by a CPU-...

Servers workload in the cloud environment should be balanced in order to achieve high efficiency and reduce resources consuming. One of the solutions is based on workload prediction, and design a proper load migration and balancing strategy. For the ease of discussion, we focus on CPU workload only in this paper. Specifically, considering the characteristics of workload, such as the strong corr...

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning diff...