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Data conversion has become an emerging topic in BigData era. To face the challenge of rapid data growth, legacy or existing relational databases have the need to convert into NoSQL column-family database in order to achieve better scalability. The conversion from SQL to NoSQL databases requires combining small, normalized SQL data tables into larger NoSQL data tables, a process called denormalization...
Energy-efficient task scheduling is a fundamental issue in many application domains, such as energy conservation for mobile devices and the operation of green computing data centers. Modern processors support dynamic voltage and frequency scaling (DVFS) on a per-core basis, i.e., the CPU can adjust the voltage or frequency of each core. As a result, the core in a processor may have different computing...
An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine...
During recent years cloud service providers have successfully provided reliable and flexible resources to cloud users. For example Amazon Elastic Block Store (Amazon EBS) and Simple Storage Service (Amazon S3) provides users storage in the cloud. Despite the tremendous efforts cloud service providers have devoted to the availability of their services, the interruption is still inevitable. Therefore...
Web applications play a major role in various enterprise and cloud services. With the popularity of social networks and with the speed at which information can be disseminate around the globe, online systems need to face ever growing, unpredictable peak load events. Auto-scaling technique provides on-demand resources according to workload in cloud computing system. However, most of the existing solutions...
Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model...
Although the problem of data server placement in parallel and distributed systems has been studied extensively, most of the existing work assumes there is no competition between servers. Hence, their goal is to minimize read, update and storage cost. In this paper, we study the server placement problem in which a new server has to compete with existing servers for user requests. Therefore, in addition...
This paper proposes scheduling algorithms for assigning jobs with different release time and execution time, to machines with heterogeneous processor cardinality. We show that this scheduling problem is NP-complete, and propose dynamic programming to find the optimal schedules. Since the dynamic programming is time-consuming we propose techniques that improve the efficiency of the dynamic programming...
This paper introduces techniques in scheduling jobs on a master/workers platform where the bandwidth is shared by all workers. The goal is to minimize the total makespan. The jobs are independent and each job requires a fixed amount of bandwidth to download input data before execution. The master can communicate with multiple workers simultaneously, provided that the bandwidth used by the master and...
We study the replication transition problem (RTP) in the Grid systems. Most distributed systems replicate data to increase data access efficiency. A replication strategy dictates where the replicas are stored in respond to data access pattern, and a good strategy can improve data access efficiency. However, the access pattern in a distributed system is constantly changing. As a result a good replication...
Almost every computation job requires input data in order to find the solution, and the computation cannot proceed without the required data becoming available. As a result a proper interleaving of data transfer and job execution has a significant impact on the overall efficiency. In this paper we analyze the computational complexity of the shared data job scheduling problem, with and without consideration...
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