As multiprocessing becomes increasingly successful in scientific and commercial computing, parallel systems will be subjected to increasingly complex and challenging workloads. To ensure good job response and high resource utilization, algorithms are needed to allocate resources to jobs

. They operate on the search space of heuristics instead of candidate solutions and can be applied to any optimization problem. This paper em-phasizes the use of Hyper-heuristics built on top of hybridized Metaheuristics to ef-ficiently and effectively schedule jobs onto available resources in a grid environment

. In this paper, we consider this specific spectrum sharing scenario as motivation and formulate an optimization framework for power control and time-frequency resource scheduling on the LTE uplink with an aggregate interference constraint. We design and propose a novel algorithm inspired by numerical solution

Abstract — In modern computing paradigms, energy consumption is of foremost importance because it constitutes a major percentage of the operational expenses. Virtual Machines (VMs) are consolidated on a fewer number of servers to significantly reduce the overall power consumption. The idle servers are then turned off by using Dynamic threshold Voltage Scaling (DTVS). The VM monitor checks for under-utilized, partially filled, over-utilized, and empty servers. The VM monitor then transfers the tasks to suitable servers for execution if subject to a set of constraints. As a result, many servers those were under-utilized get idle and are turned off by using DTVS to save power. Simulation results affirm our study and a substantial reduction is observed in the overall power consumption of the cloud data centers. Keywords- Virtual Machine, servers, cloud computing, consolidation I.

This paper presents lottery scheduling, a novel randomized resource allocation mechanism. Lottery scheduling provides efficient, responsive control over the relative execution rates of computations. Such control is beyond the capabilities of conventional schedulers, and is desirable in systems

for the heavy energy consumption by data centers. For energy economy, the redundant elements can be powered off based on the current workload within the data center. We propose a Data Center-wide Energy-Efficient Resource Scheduling framework (DCEERS) that schedules data cen-ter resources according

-state approach, two-way signaling message exchanges, receiver-based resource reservation, and being independent from all other related components in a QOS support architecture, such as flow-specification, admission control, scheduling algorithm, and routing. As stated in the article, “RSVP is primarily a vehicle

This paper studies the problem of efficiently scheduling fully strict (i.e., well-structured) multithreaded computations on parallel computers. A popular and practical method of scheduling this kind of dynamic MIMD-style computation is “work stealing," in which processors needing work steal

into efficient horizontal microcode and good hand coding probably both require effective global compaction techniques. In this paper "trace scheduling" is developed as a solution to the global compaction problem. Trace scheduling works on traces (or paths) through microprograms. Compacting is thus done

Cilk (pronounced “silk”) is a C-based runtime system for multithreaded parallel programming. In this paper, we document the efficiency of the Cilk work-stealing scheduler, both empirically and analytically. We show that on real and synthetic applications, the “work” and “critical path ” of a Cilk