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Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing interest, underlying the trustworthiness of the experiments and the conclusions derived from experiments. Dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and...
Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing concern, underlying the trustworthiness of the experiments and the conclusions derived from experiments. Dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and...
Efficient low-level resource provisioning and QoS guaranteed are key challenges for cloud computing. When using virtual machines cluster to tackle various tasks scheduling, the target is to assign the tasks to each of the available nodes evenly in in premise of ensuring QoS of services, and it also means that the cloud providers should consider to reduce the load of overload nodes, and improve resource...
Grid computing, was developed by computer scientists in the mid 1990's based on ease of use and access geographically distributed resources which are dynamic and heterogeneous for solving difficult problems. These distributed resources are owned by different organizations. Grid computing provides a framework for parallel/distributed computing. For developing a grid, low-level services (secure access...
Operating Vehicle Sharing systems means periodically performing some relocation process, in order to avoid stations becoming either empty or overfilled. We first propose here a static Vehicle Sharing Rebalancing (VSR) framework which unifies preemption and non preemption. Next we present and test a decomposition algorithmic scheme which decomposes our problem into a simple minimum cost assignment...
In data centers, many tasks (services, virtual machines or computational jobs) share a single physical machine. We propose a new resource management model for such colocation. Our model uses two parameters of a task -- its size and its type -- to characterize how a task influences the performance of the other tasks allocated on the same machine. As typically a data~center hosts many similar, recurring...
New scheduling algorithms are first evaluated using simulation. In these simulations, the workload has a huge influence on the measured performance of the simulated system. Therefore, it is customary to use workload traces recorded previously from real systems. Such open-system simulations preserve all the jobs' properties. However, preserving the jobs' arrival times actually destroys the logic of...
This paper aims at the development of a cloud services provisioning framework by developing a dynamic priority job scheduler cum load-balancer for the cloud. The main motive of this paper is to provide a means of managing the job requests in a flexible and cost-effective way, both for the customer and the cloud service provider. In order to make the cloud scalable and adaptable to the changing needs...
The era of big data computing is coming. As scientific applications become more data intensive, finding an efficient scheduling strategy for massive computing in parallel and distributed systems has drawn increasingly attention. Most existing studies considered single-installment scheduling models, but very few literature involved multi-installment scheduling, especially in heterogeneous parallel...
Handling overloads in dynamic real-time embedded systems which are subject to frequent workload changes is a critical issue as it allows graceful quality of service (QoS) degradation and provides a mechanism to determine the load to be shed to handle the overload. This paper presents a novel hybrid automata based ERfair scheduling framework for a real-time system which may be subjected to transient...
Distributed system is a set of resources interconnected by a network. Grid computing systems are distributed system designed by integrating heterogeneous resources with different characteristics. These heterogeneous computing resources are designed for highly complex programs that require high processing power and huge volume of input data. Large scale applications such as meteorological simulations,...
In a cloud computing environment a proper load balancing technique is required to execute a process and manage the resource. In a distributed environment it is difficult to achieve this, as different resources have different configuration and capacity. Generally, load balancers generate the mapping of tasks to resources, based on some particular objectives. Load balancers employ a function that takes...
Divisible Load Theory (DLT) has received a lot of attention in the past decade. A divisible load is a perfect parallel task, that can be split arbitrarily and executed in parallel on a set of possibly heterogeneous resources. The success of DLT is strongly related to the existence of many optimal resource allocation and scheduling algorithms, what strongly differs from general scheduling theory. Moreover,...
The energy costs for cooling a data center constitute a significant portion of the overall running costs. Thermal imbalance and hot spots that arise due to imbalanced workloads lead to significant wasted cooling effort - in order to ensure that no equipment is operating above a certain temperature, the data center may be cooled more than necessary. Therefore it is desirable to schedule the workload...
Cloud computing is becoming increasingly popular for the provisioning of computing resources, in particular, through scientific tools that perform modeling or simulations. Vehicular Ad-Hoc Networks (VANETs) are mobile ad hoc networks which are meant to support primarily safety warnings and to manage challenging conditions to improve our transportation experience. This is a challenging context where...
Cloud computing has been gaining popularity for quite some time in various areas, on the infrastructure, platform and application level. Recently, the possibility to provide high performance computing (HPC) as a service has been investigated in conjunction with the cloud computing paradigm. While this is a viable solution for applications that do not require HPC in the truest sense -- with supercomputers...
In this paper, we consider a new variation of the classical online scheduling problem. In our model, an online scheduler is allowed to migrate the assigned jobs to different machines. Live migration is a powerful tool for load balancing. However, migration will incur additional cost in the destination machines. In this paper, we study the scheduling problem with migration cost model. Suppose that...
Divisible load applications occur in many fields of science and engineering. Such applications can be easily parallelized in a master-worker fashion, but pose several scheduling challenges. In this paper we investigate the scheduling problem for processing a set of tasks comprising both divisible and indivisible tasks on cluster systems. Indivisible loads are characterized by the property that they...
The emergence of grid and cloud computing require load balancers to deal with potential problems, such as high level of scalability and heterogeneity of computing resources. In this paper, we present a generic load balancing framework which separates allocating process and migrating process while preserving a guaranteed level of service. Based on this framework, an intelligent load balancer that is...
This paper addresses the problem of load-shaving in Smart Homes in view of improving energy efficiency in Smart Grids. An architecture of home power management systems, allowing the separation of domestic power load control from grid dynamics, is introduced. In this framework, the operation of appliances is modeled as a finite state machine which enables the implementation of online scheduling borrowed...
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