Wednesday, 16 January 2013

Cashing in on the Cache in the Cloud


NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD.,  AMEERPET, HYD
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 Cashing in on the Cache in the Cloud
Abstract:
            Over the past decades, caching has become the key technology used for bridging the performance gap across memory hierarchies via temporal or spatial localities; in particular, the effect is prominent in disk storage systems. Applications that involve heavy I/O activities, which are common in the cloud, probably benefit the most from caching. The use of local volatile memory as cache might be a natural alternative, but many well-known restrictions, such as capacity and the utilization of host machines, hinder its effective use. In addition to technical challenges, providing cache services in clouds encounters a major practical issue (quality of service or service level agreement issue) of pricing. Currently, (public) cloud users are limited to a small set of uniform and coarse grained service offerings, such as High-Memory and High-CPU in Amazon EC2. In this paper, we present the cache as a service (CaaS) model as an optional service to typical infrastructure service offerings. Specifically, the cloud provider sets aside a large pool of memory that can be dynamically partitioned and allocated to standard infrastructure services as disk cache.
            We first investigate the feasibility of providing CaaS with the proof-of-concept elastic cache system (using dedicated remote memory servers) built and validated on the actual system, and practical benefits of CaaS for both users and providers (i.e., performance and profit, respectively) are thoroughly studied with a novel pricing scheme. Our CaaS model helps to leverage the cloud economy greatly in that 1) the extra user cost for I/O performance gain is minimal if ever exists, and 2) the provider’s profit increases due to improvements in server consolidation resulting from that performance gain. Through extensive experiments with eight resource allocation strategies, we demonstrate that our CaaS model can be a promising cost-efficient solution for both users and providers.

Existing System:
            Due to essentially the shared nature of some resources like disks (not performance isolatable), the virtualization overhead with these resources is not negligible and it further worsens the disk I/O performance. Thus, low disk I/O performance is one of the major challenges encountered by most infrastructure services as in Amazon’s relational database service, which provisions virtual servers with database servers. At present, the performance issue of I/O intensive applications is mainly dealt with by using high-performance (HP) servers with large amounts of memory, leaving it as the user’s responsibility.
Proposed System:                                                                  
            In this paper, we address the issue of disk I/O performance in the context of caching in the cloud and present a cache as a service (CaaS) model as an additional service to IaaS. For example, a user is able to simply specify more cache memory as an additional requirement to an IaaS instance with the minimum computational capacity (e.g., micro/small instance in Amazon EC2) instead of an instance with large amount of memory (high-memory instance in Amazon EC2). The key contribution in this work is that our cache service model much augments cost efficiency and elasticity of the cloud from the perspective of both users and providers. CaaS as an additional service (provided mostly in separate cache servers) gives the provider an opportunity to reduce both capital and operating costs using a fewer number of active physical machines for IaaS; and this can justify the cost of cache servers in our model. The user also benefits from CaaS in terms of application performance with minimal extra cost; besides, caching is enabled in a user transparent manner and cache capacity is not limited to local memory.

Software and Hardware Requirements
Hardware Required:                            
System                                    :           Pentium IV
Hard Disk                   :           80 GB
RAM                           :           512 MB
Software Required:
Operating System       :           Windows XP
Language                    :           Asp.Net, C#
Data Base                    :           SQL Server 2005

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