Workload Management Systems for the Cloud Environment

Introduction

Modern business software and services demand high availability and scalability. This has resulted in an increasing demand for large scale infrastructures which generally tend to be highly complicated and expensive for the organization requesting them. These higher resource and maintenance costs have given rise to a paradigm shift towards cloud computing. Its services are offered and maintained by various providers over the Internet. These service offerings range from software applications to virtualized platforms and infrastructures (Mittal, 2001). Cloud Computing is the combination of parallel and distributing computing paradigms. This distributed computing paradigm is driven by the economies of scale, in which a pool of virtualized and managed computing power, storage, platforms and services are delivered on demand to remote users over the Internet (Foster, Zhao, Raicu,& Lu, 2008). It is helping in realizing the potential of large scale data intensive computing by providing effective scaling of resources (Mian, Martin, Brown,& Zhang, 2001). Also, a Cloud computing environment is widely employed in scientific, business and industrial applications to increase the use and delivery model involving the Internet to provide dynamic virtualized resources (Jeong & Park, 2012). It offers the vision of a virtually infinite pool of computing, storage and networking resources where applications can be scalable deployed (Hayes, 2008).

As data continues to grow, it enables the remote clients to store their data on a cloud storage environment with different clients’ expectations over the internet. An increasing amount of data is stored in cloud repositories which provide high availability, accessibility and scalability (Duggan, Chi, Hacigumus, Zhu, & Cetintemel, 2013). As clouds are built over wide area networks, the use of large scale computer clusters are often built from low cost hardware and network equipment, where resources are allocated dynamically amongst users of the cluster (Yang, Li, Han,& Wang, 2013). Therefore the cloud storage environment has resulted in an increasing demand to co-ordinate access to the shared resources to improve the overall performance.

Users can purchase traditional data centers, sub-divide hardware into virtual machines or outsource all of their work to one of many of cloud providers (Duggan et al., 2013). As the number of users submitting the queries increase, that leads to increases in the load and traffic of the virtual resources. So it is essential to incorporate a mechanism to balance the load across these virtual resources (Somasundaram, Govindarajan, Rajagopalan,& Madhusudhana, 2012).

Workload management is the discipline of effectively managing, controlling and monitoring application workloads across computing systems (Niu, Martin,&Powley, 2009). Workload is as a set of requests that access and process data under some constraints. The data access performed by a query can vary from retrieval of a single record to the scan of an entire file or table. Since the load on data resources in a cloud can fluctuate rapidly among its multiple workloads, it is impossible for system administrators to manually adjust the system configurations in order to maintain the workloads objectives during their execution. Therefore, managing the query workload automatically in a cloud computing environment is a challenge to satisfy the cloud users. This is done by relocating resources through admission control in the presence of workload fluctuations (Niu, Martin, Powley, Horman,& Bird, 2006).